Repository: qiskit-community/prototype-entanglement-forging (language: python)

Issue:
Asymmetric bitstrings
**Is your feature request related to a problem? Please describe.**
With the current version of Entanglement Forging, it's not possible to model open-shell species.

**Describe the solution you'd like**
Having the option to specify different bitstrings for spin-up/spin down orbitals would make it possible to model open-shell species.

**Additional context**
N/A



Discussion / hints:
I would like to add, that it would be useful for nuclear physics simulations to permit a different number of alpha and beta particle. Usually, orbitals with different angular momentum are weakly correlated and this would permit the application of entanglement forging in this context. 

For example, given a 12 orbitals Hamiltonian, i would like to split it in a 8 orbitals times a 4 orbitals term.
Thank you for your comment @orielkiss. This is helpful to know when prioritizing which features we can implement!

Fix patch:
diff --git a/.github/workflows/citation.yml b/.github/workflows/citation.yml
deleted file mode 100644
index a8c3f4c..0000000
--- a/.github/workflows/citation.yml
+++ /dev/null
@@ -1,67 +0,0 @@
-name: Citation preview
-
-on:
-  push:
-    branches: [ main ]
-    paths: ['CITATION.bib', '.github/workflows/citation.yml']
-  pull_request:
-    branches: [ main ]
-    paths: ['CITATION.bib', '.github/workflows/citation.yml']
-
-jobs:
-  build-preview:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v2
-      - name: Check for non-ASCII characters
-        run: |
-          # Fail immediately if there are any non-ASCII characters in
-          # the BibTeX source.  We prefer "escaped codes" rather than
-          # UTF-8 characters in order to ensure the bibliography will
-          # display correctly even in documents that do not contain
-          # \usepackage[utf8]{inputenc}.
-          if [ -f "CITATION.bib" ]; then
-          python3 -c 'open("CITATION.bib", encoding="ascii").read()'
-          fi
-      - name: Install LaTeX
-        run: |
-          if [ -f "CITATION.bib" ]; then
-          sudo apt-get update
-          sudo apt-get install -y texlive-latex-base texlive-publishers
-          fi
-      - name: Run LaTeX
-        run: |
-          if [ -f "CITATION.bib" ]; then
-          arr=(${GITHUB_REPOSITORY//\// })
-          export REPO=${arr[1]}
-          cat <<- EOF > preview.tex
-          \documentclass[preprint,aps,physrev,notitlepage]{revtex4-2}
-          \usepackage{hyperref}
-          \begin{document}
-          \title{\texttt{$REPO} BibTeX test}
-          \maketitle
-          \noindent
-          \texttt{$REPO}
-          \cite{$REPO}
-          \bibliography{CITATION}
-          \end{document}
-          EOF
-          pdflatex preview
-          fi
-      - name: Run BibTeX
-        run: |
-          if [ -f "CITATION.bib" ]; then
-          bibtex preview
-          fi
-      - name: Re-run LaTeX
-        run: |
-          if [ -f "CITATION.bib" ]; then
-          pdflatex preview
-          pdflatex preview
-          fi
-      - name: Upload PDF
-        if: always()
-        uses: actions/upload-artifact@v2
-        with:
-          name: preview.pdf
-          path: preview.pdf
diff --git a/docs/1-tutorials/tutorial_1_H2_molecule_statevector_simulator.ipynb b/docs/1-tutorials/tutorial_1_H2_molecule_statevector_simulator.ipynb
index 442d82f..a467e83 100644
--- a/docs/1-tutorials/tutorial_1_H2_molecule_statevector_simulator.ipynb
+++ b/docs/1-tutorials/tutorial_1_H2_molecule_statevector_simulator.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:05.976371Z",
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:07.878080Z",
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:07.934705Z",
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:08.499392Z",
@@ -117,7 +117,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Classical energy =  -1.1373060357534013\n"
+      "Classical energy =  -1.137306035753399\n"
      ]
     }
    ],
@@ -147,12 +147,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ansatz for Forged VQE consists of a set of input bitstrings and a parameterized circuit. In the current implementation, the bitstrings and parameterized circuit are the same for the alpha particle subspace and the beta particle subspace. For a discussion on picking the bitstrings and designing the ansatz, refer to the Explanatory Material. "
+    "The ansatz for Forged VQE consists of a set of input bitstrings and a parameterized circuit for each subsystem, U and V. For this demo, we will use the same bitstrings and ansatz for each subsystem. For a discussion on picking the bitstrings and designing the ansatz, refer to the Explanatory Material. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:08.939346Z",
@@ -178,7 +178,7 @@
        "     └──────────────────────┘"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:08.991125Z",
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:18.545432Z",
@@ -248,18 +248,20 @@
     {
      "data": {
       "text/plain": [
-       "Ground state energy (Hartree): -1.136879759452257\n",
-       "Schmidt values: [-0.99387921  0.11047227]\n",
-       "Optimizer parameters: [6.2811056  6.49687807]"
+       "Ground state energy (Hartree): -1.1371879577404784\n",
+       "Schmidt values: [-0.99379314  0.11124381]\n",
+       "Optimizer parameters: [ 6.28184566 -0.11214057]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "calc = EntanglementForgedGroundStateSolver(converter, ansatz, bitstrings, config)\n",
+    "calc = EntanglementForgedGroundStateSolver(\n",
+    "    qubit_converter=converter, ansatz=ansatz, bitstrings_u=bitstrings, config=config\n",
+    ")\n",
     "res = calc.solve(problem)\n",
     "\n",
     "res"
@@ -274,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-04-27T13:41:19.072518Z",
@@ -291,7 +293,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -310,7 +312,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -329,7 +331,7 @@
     },
     {
      "data": {
-      "image/png": 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Q7IW+rUQiVSz5s0rMigRG33PgZmh97xIG7ribuoU7SO4Ypfv2V2i+MEuN/S0qzwyx98F69m48E7+vE3d0kJYPXo5fsYzs3h6G77obe2+ahquX0P3vP2RkX4zwqtNIPfoYrErSdPUicEZh0y8AyAzY7H24lUhzlI4LtqBMwIpCOAGhRHDCNt7ASNV7MGqbSVQcAM8BMwyxOvKVZ6Fdj5DZi246Ez+ZwkgkUFbZ/PcSp7iyeaf52sU0EuN/26YN7kSQ+yoIcsdzOJjqIrLg+/SMnka60C0x5SbpywwBMJgdwcvqoH1ZzJ5Ew3jf9PEzEbVvAsAGmv46mF6vGqj4VBIz0wk7HySGouk8i6E770ZVNtJ+RYJ49seQBWKgllTRvznOaDpCbm+MhjNGqL+yh4P5FANbEqSMBoxYB+El12PXxhn81fcxlENmX57uxvOovf4N2KEMhsqivDS4DkP3PUb3E/+AshSL3thDpDp4b+UzBnse6sDPazouPUDPztWkd/ShbJuWd59DVfgZqO4g3fp+sj0uNefG8RvWkh/KEWmrQ0drcXt6wDSxm5rm8uiLMlI+QY6LpSbatG0VAuWitcb1XVDBqIeO57Bz9BXsyk1sH9w+3r887SYZzA4BkB3uQXsKs1Zq5KcKM5GAxEpoCLqI1q6H2pv/KLjTy0P3i8HFTQO7abquHeO3+8h3d1N5zZupW7IfRg/Q9KeXwfNx8vv34yWTjPzmPvyREYyKBB3XDjPS20T/450Mb/jJ+H5VKIQKhfBTUWKNDrkRiwPPL6PymutQlkHy8V/ipQcxDM3u+xpA91J306WkX9pG923PEHp/B2zfx75//TzaVeizhxnurMPp91h45RDDA8sYfj6YaKX6qnNoWfoihOJBN81YHcRqcRffSHLrEFUXrYRoNU5vhvCidlSqB+INaDsWlFXODbxulVWQm8bE07HNINRd3x2vdQPkfIdkPvi7Nz1Izk8HJ0G9FCO5YQDUYNC3/K69Oc7vHGbNAqmZn9JMOxhjpm0dEIzN3LRu+sMU0Hzt1GV+NgtKYRg+YTNM5fYdONu24Q0O4KdSwU8uh5lIULe0h2xXls7vb2Dghz9F5/OoSITWty3GVGk6f+PQsDZPTfh23DMMdve2s+fb+8A0sSI5QrUuhzZWgcpjRT32P1SJ9nqpvmQZWoUZeuAFonY1VRcuQ2UGYWAX/vZH2f+le8n2mTirM/hEGdoMTRcb1LYdQBNm//aLQZm0r91K38gb8XSEptUHUL6L33EpQzsTVK5bgtX3FIQrIVIV/B5rOgpXBD+Jxll/qcTsKJsg14fVyENm8GU95+cYdSbGV8l7OdK5IMgHsoUgNyHnpxjNB0FujgZ9y/cZVdj7BiXIy5gRmTihrYDIyhWT2vOniwHL/0dwW/s++D7KDIaGWP45jcqNwp4nsPw8HbesZuS+B/EGB6g+vwVz8GU67+ylerVNOPMse39lUbEsSnPro6Ah17aY7t8M0X3/RsyaaqzqBnQuQa5vP7EGh4HNwQAPZsSj50mNecuHSD78EKktwfD/u7fncYbvAcA6J0Pd+mq6v/MEI3tjDNf7dFxxEMOc9pTG9Y2+kb4HdqFsGyMaxYhGUfEYRjRGqL2dpmvbMYe3QXU7KWs92a3bsZubqThvFapvS3DhVygOjafh+yZGKATax8/l0Y4DSgXbjsVe+wsmjqi8gtyYFORGEOSZvMNAdlKQa4eMFwT5sDOCS9C/3CVNxh0N1h0Nfu9o3Ybbvx9YNL6+72v+8+EdvOPcdpqrIujCvJzytfb1RxkGTOrTrpQKarurgmp/CKj/n1PH6ul4R+GG1iz7yxRGNILqegG8HO0fX8bw3b/B7evHGxrEGxrCGxqm7qarqAw/xf77o9gJaGx+kj0PL6Tr678BoP5sD+1q+l+xqVqSQ7s+vRtjDPXUke9yqGjLMHogyr4tVxFZtQojYmFGTIyQwrDAsH2cl5+h997NxM9cTGjV2fg5F53N4Wcd/HSa4bvuxN+QZsFVmtTuDPsfrwc/eO/XnZmjcXXf+HMc7u2g6xFN81VV2GGHA/fl0dnCcBmmSfPb1lATexLCFQyab2XwkVcxQmFUNEJk+XIaz3PJGqsYvH8jyjJR4QhGJIyKRKm88iLCI08H27IiwY8dAcMOhosY+4lW49eeDoBh5NF2gvzBg5iVlZiVlcG1FGX0f7ZsghzlTQ3yQo08lcsykB4dX573c+Pt4qO5IXyCN5hHGscfBQXhTBDuA3XdJAefIJO7iL/46Yt8+s0rcVyf/33fNizT4AMXLuLif3qQz91wOjecJeOLiJOgVNDuD9B+HhAM+Vb7gQ8cdZWOmws3fJ/Fn07jbNuOMg0iC6vB94nv6CXW5KNTQ9i/2UzuwH6qb3oLdY0vMHhwBQO/eiz4hpBMgjd9pqz4oijtq55AGU9AhODHsMCO058LmoV2/+40cju3Ea7K0P6hdfT+6iX6Xwqhl30Au6EWPdrHoYfuRyk4eF8fhqkJ1Uapfv87ARh9+GEO3rER+91n4h08yMF7f0542SKM6hq8ZJKB730f95E0qYNRtBnDiCfwHQdd+Bn89n+y+Mou7Lh/zMPrZg32PL4cDJOF52+j85U1ZPccAsOg+bp2IolhOh+N03DFAqrMh8GOMuBfz+CDm8AwUKaJsixUKETddedR4T4YBL8ZDnoxWcHAclS0MHhoGdkt21G2HfyEbMzaOmouWgh2hJHnu/AdJ/j2ctVVJ/MuOWFlE+QaF3tSG3l4cpBnRoLHaBNPO8G4KwqS7ijazII2UMojp4eCddPB5BOjUfCcXWzcP8TdL3dzZlsV7bUxzMQWdvQ2sLO3nv5Ujgdf7ZEgF3PHMDATCWJrz5myOF4XzHylgMbTr55yXy1Q+4ngttYanclMnANIp9H5PJGlHag9DwRdM3NpyKcgl4JcmtoLqvBfSZB+YSPxSy6j+QIHu+8pmq+I4iaWMvDz347vK9zeSPvZr7Dv2ZX4WZf2C3Zgj/wbAFVrFHsGlrD/h8EVxdEmWHjOkxihMFgRehzNwNYEZliz+JohQgsTYFWBFcHZ18Oe24fYu+F0ouesw7BNlG1i2AYYCtTEN+PRh+/FHRwGrdh1TwPa76HhHW8gvfFVDv5yD2ZI4zlDdN12AOMPTsfd30vPA78ismoJdlsH2tfgeeR2bKPzi7fScYNFdPmC4Ni4TvCjPQae6aXn+SrM6urg2ObzwU8uR7Yji/YNRvcHWRQ7ew0Vh74J1/1r0ecMKIsg11qD8rCNicvpw4VL65P5LEPZYPAs7cZxjTw5nQETUl4/yvLwczWo0CBaBV3KItng92gUfG8v23qGsGsf5ZWDVRzMZIi1f4eNw6Ns7WkguvC/+F3PG4Gp/6mEOFUppVCxWNBW3XDY0MRr3n7kdYCGK6cvN4CFgM7l8DMZ/EwGq64OlRthsYqi83lMpwvS/aA1phWm488Wk3ziCfB8KtYtw9jzm+ADI5+m8ZJ2zJds4qvqCQ3cU1ieAdch3BRlwaev5dD/fZHMS5uCq4YdB53Noj0vaC7xg5q6EY+z4IYGfCdP131pmq+MUWPdgXeWYm9qJflhh46rOune2MaBH+0GINaqaD/jcQzz8fHn59Ya7HmgmT13+qjwofFeTEYoBpZFfn+OxIIMbdeHUZEKMKNghuj77TZ6XwjOvzRe4FN1+VrY+RDsDUHvFgnyI8l7PiiP0KSmlYgZXByUzjsMF9rIDT+Bb+TI+1kwwdH9GICla/EJrurU2iSSzZG3wTMV2jjIg/seJtL0azYOQ70bjB7Y577KM13PYcV3M5y/nT0D7+NvfraV963v4IpVjVz7lcf4w/MXcuayPj735OdYXrOcv13/t2SzcSqjFi6jGH4FEdtgON/HD58YpmfE4b3rOzAMeGJHH8/sGuCylQ28fW0beZ0kalUwlB3lG6/8B4vsN9E/HGMk+gsOZQ6xvHol/fsvp6U6yo1nt1IR9djT6/Lgll7+8MJa+rN9WIaFZViQr+MXz3cRb3qMzf2vjB+z9Y2/x/bdi2jveJGwEee5zR0kneBDzTYN1i2qoTJis61nlNOWdvJY973j5wg0mrAZ5v2nfZiE2cRDXb9g5/BOFiTaSPdcwqUra+j3X2L70Haqw9Wsa1rHlv0xnt87RFXUpr71OSqtVrbvaaJ+wXMMOr0sr17OofQhLMPi9PrT8TMdZNw0O7MPcHnrdUTNOGl9kJf7XqY53kzUW8GmrmHaaiLscO6hO9XN0qpl9HafzYHBDEvrY9i1T9AWOYsN28MsqovTUh1hSUOCqphHb6aXnJdj9/BuBrIDpN006XyatfVv4MWdVVTHbN6wvJLf9f6WwewgjucQsSIsi7yZJ7aliIdMbjy3kr2pV4nZMeJWnI6qDjbtzzOazWObBn1Jh1jI4rSWCvq9V6ky29nVA1bFKxxKH+K0utPIpzporQ6xN/0iQ84QqXyKmB1jTdVl7OnLEDINhjJ5fK1prY6ysjnE7pEgjAazg+T8HHWROprDKxnNuvh2FyO54FvpaG6UZD5JW6KN/v4FDKZzrGmtoroySdSoYf+Ag2vvojnWzqsHfJKOyzkdUV4ceALbtDm7/jxe2J3DMBRrF1awN/UqbdHV2KZJxhjFiNSik3lyxgj14Uo822JExRkxPBZWLiSfC2PbDsmr1tHZG2VwNM/K0z/CiN5BX6aPJVVLsFY3sXUgTX7BZbiexvV98pN+69WasGUStgwitknYNrBNI6iUK8XKpgTdqT1syvSyrHoZ3kc1m/M+9vAufA2Zt7diOFkydj+tjQ0MP/0Ew6kE+xcvYGTgcUztonwPAw8F9LzhAqxnXsJwshhuHsPLY+RzKM+HtetJnGeRz23hZW8Ex89hahfzLWsIr1xMXUU14fDD5Po3oU/7PazrvzQrUzeWRZBn3TxK6fEuhwCRQo08nXMYKfRasVUlHr3kdXBxkLKD3ikJs54Rgq96OldDNNtDNlKYMk75bBy9A0IwqDaQzVaDBa69k439v0NrhWGP8LF7/omXd17I1vz3iLy0lYPpa/nKCz+DXY/Qmmjlya4nuenOtzG4439Q3fQSo+H7MHs/TKLiEEORu3AOXU1+4HJu37B//Dm0VEV4YMshvvHcnQxXfIs65+0MOH34lQ/j5x7Ey7RhV21kSdViHt7/MNmDneQHL+arjz1GZNF/4qeXM9Szju903kZ+0tjsMW8lA72riDTfSXtFO2EzzLAzzP17H8Lpu5xw/wOgDfIHbmFhfA0AyazLXS8GV74a4W4SB79KdaSCqnBV4TgpDiYPcs/WjWQHz8FsuJOqcBXDzjBO78t8e/ce3NDO8TIoDNIH3k3IOZtc9BmirXegdIhs36WEu+8/4uvsD7wJbR/ErHiJf00/gMqchlv3QyAYtnhk55/iO62EG+4lVP8QMStG2k2T7b6JRO5iUrF7CDfcj/KqGN35UbQXfCjb4X7qV/wXSXfkiPs19PcY3f0n+E4z1Qt/jhf/XbBcGfjax0/dR77rZlw1yA+6biWvBsfXjZqV9G37I/zc1K594apNhFq/j+UuINm3lkjzL8fvy3a/g8rabeTCL017/qmeqdViZSZpXf0NRtyeaeUOpS4lk6rHbPz5EZ6VIr3/fXjJ1diJ7cQWfoeou4K+7rVE224johfQu/WPQZvULv0u+dC24PnQzKGtt4AfpW7RneSiT2MMXY1tglPxG8yhawGNV30P0dSbyTs1uLU/BiBh1dCz7WZalt9B2u9heNcf4WfbiVS/iN3yo8IxNcns/SNyqeNPkH40ze1PkUrcCUDMrOHQlj9Gu9WTHjEU/DIyNK36Bxz6GN33YbytcWDNEbaogTOCrx9jX/qjk+5+VtO8tJNUaNLr5QGLd1NhNtP96p+ivQTWgOKWumE+dmUj0dAxug+9BmqsRjWX1q1bpzds2FC07XWPDPOmX1zMJXUf5D+v+xQAX3ny13xj+2f4+3X/wVOdL3Bv97eodM9nRG3B8GrwQ3vH119s3sBu7y4AzOwqPn3nJpqSij+7OQxGMPCW9kMoI4fWCuXVgtWP9qLEaGM0WYFV9TzaD6MMB+1WoKzgBOuS8BuJp26itS7Lg8P/i5yrg/u0ifZDYDjgRVFWihuX/AFL7LcQD9kkjVfZk3qJiLuUH+38GsrIolEowMu0YUb3gdJ4/VdxacN7eXj4S4QqtnHzqo/y3y//CG0Oo4xCeOea+OS6j2Cbiqf3befRvu+jlMZNLWV97K+JWBb7h/vYHf5/MewhvEwbyshSEc+zvjXokG0ZFu2x1dgqyk+2f5fe1AhLcn9La0UDWgdv9U3DjzBa+Z1gn+kVXFb1N9zf9y/YVS+htWKV9SHOqrmccCTDbbv+Ed/ez1uWXMNvdv8GJ70AI9SPYY3iphdxRfVnWNrikskkMJTPLw/cStJ+Bsaf/wG0Noj7y3hT84f4efcXiJoJrl/2Zu7Y+R30yPlcVPXHPDb6T9iJnVzS9gYePfAobnIFZmw3HZULubT1jYSo5bubv42nRnnvio9iqzBOppaskyCXs9jee4hXzc9TE41yVv06Hu3+NbHUm7is8b24nuLh7l+Sr/kJZ9Sdzd7hLoadEdYnPs6Kxjqy/gg/2f1v2KbNu1e8D60VrhpFa4ufbPseXj6BCvWjlMZLLeMtLX/GvYf+DzqyHYAW7ybe0HIlTj7E3QduxY89zw0dHyRhV+ORQinN3TseYMTfx5ubPkJ9rJp8Por2LR44cDeD1kMA2M5q3r7sD9Fa4blhklmDu7v/BTvay3WLbuKXu/4vnhfCsIMPMt+pR4X6WRQ7i4pwjFeGnqQu+y5aEi28nPsKjeHFLK1eztO99+DnajFCQYXIz1dg2KNTbmttEHVXcGXbDdzd9RWUkUfjo90EIUtxzaLr+eWen4DTzlsW3MLd3V/GsNK8dcl7UcpH46Fx0Xj4ePjaReNjYGMqG0OHUIRQmICiJ9XPY70/osJby9m1V/LowL+TMBt586I3B/tl7CSv4snOp+nJbUPnKwnZLm9edC1jM/6qoME96NxSWBb8a6IwAAOFgcJk7/ABnh/6NTXuVVzUfAVKaZTy6U338dTI16k0W7m0/Q3s6rb53Uun8fX3nsvVa15b04pS6jmt9bSrJIoS5EqpauCbBB9nGviQ1vqpoz2+2EG+s+8Qb737Kq5q/B/8n2s+DsCtzzzAv2/5JJ8568s83vksj/feTqu6km7vSfAqITRRg7mw8haeGgnG5a71LuPPbnsA3zT45/esYNTbjzIdlhrvZYd3G0ppzgz/CS85XwNgdeSdjBy8mO3pxzh9aRejAyvZtXcx77xqO5t2V7N55wLiIZNUzsOI7KNyyTeJ0kTPzrdStfRWQlTSs+VPuHz9Bl4Y+g2+njgbHzEjZL0slgozvOvD1C/5CcrMsMT5PGes2EVvbicHtt/AoZE8K1tMhqv+k80Dm1AYpPZ+mDUdDom6V3n6mTfj5SeGL1i+ZDut7RupGPkgWzvHvnnAZWc4dKk7qUy9k2jYY4f/XZKFsWjS+TSdyWAiiZpwDZfX/AVPb64K3vKFr7S2pYi3f589yZepGfwrRpIJTmsJk2j/Of2HVvDoC23YpiLvaSwrw9rz7qI/18nCioXUJD9EU12KLv0bQkNv554X0yQdF8tQeFqjtcfFF91PTTRK357ryTV8jb5MP3tf+RB4cU5fcpADka/ga58z69aSPXAzg0lY2WqhGn9ET/ogHRWLyXS9g6UdB3ig51scSBbmZVUWzoEPkx6ZqAWGTIN42CQetrjyrCwvO99g9/BuVlefx95Xf5+8qwhZBrXxEOvOeo4XBh4makWxhm7k0ZcnjnVN9SESi77DkBOEnUKh0dSG6wgf+iRrlvUwoJ4h1/1eNu7JEY/kWXrGT0jolTz41Dn4GuIhk5Yag4rF32DH8JYp733bsKkc/QB79i2bstw0NBde8CBZ3curL7yDlDP5YjnFmnaFveC77BreQV24kb4dH+S05TsZNp/G6vljGlu38MzQ9zCVyRsa3sGTG85jMJ3j4rM62eF/h5HcCGfXXM6BbddRu+x74IfZ8uKNrDnnVyil2fziDSw+/ac4/ii7Xno/uXyY1uZ9OHXfYpF5E6MDK1HN36I71UlbooPUnlvY12fQWp/EXPA1Bp2JmatMZY43C9qGjaEMHM/B8Zzgqu3DdMTXsOWFd+F6Fmet6KIrdCtZLzvtcSEjxFnRDzEw0Ibf8N/0ZXrRaHzto5nIRa01mmCUTx//iPs8vepSXthwLc5hd521opPe6HfIeTnWNq7lE2v+mdUtla+5u/JsB/l3gce01t9USoWAmNaFLiBHUOwgf6l7P++571quafkIX3rT/wTg+88/zpde/hM+fvoXeXT/U7wweB/Lo29ie/YetJfAsCeK9672/8WP938OgFWhd3HLf/yAA/WKO959IbsHD2FEOvmHs+/gs099EjPayf937i/4zDPvxwj18572fyY92sYPn9nHE391JT0jWe7b1MOn3rSCV7tHue2ZvXzsyuXs6kvyyLZefn99jAq7ki1dLi0NQ0TNBF39Yc5bVMOB0QPcv+9+YlaMFbUrOLP+TJ7sepK4lSCbaqe9IY/jpVhWs+xIhwGA7YPbSeVT6GwHy5sqSIQtNneNsP3QKH7htb5yZRNVr2GI3gOjB3A8h8VVizHUkccEd32XZC5JdaR6yvK859M5mKGtJspo1iWVc2mrOfqFIZ6vOTiSpbEijOdr+pLOlMfn/Xww32qvQ208RENFmJ5UD3E7TiKUOOp2J0vn0/Rl+ojZMdxcgt19KWxTsaQhQW18+jj0ru8G5xiOQWvNSMYlmXNJOy6NlREqIiajuVHyfp6acA2O52AZ1ngX2cnrag2GEfwnz+Q8QpaBWfjb1z6juVEcz6EqXIWBgatdLBWmczCDrzV18TBh28D1NYmwNb6d0WzwzbIyahO2jClBorXG8zWWOfU19XwPc9IVRFrr8fUm3zeWIZO34fsapYIyp3I+acejJm6Dcgmb4fFtTb4OY+z5e7jkvfx4eB/tvTZWDsdzcHUwHIevfarCVWTzPoZShC0D13fR6Gnbmvx8TpavfTzt4fkevvaJFYZJ8H2NVzievtZEbbOo15gcLchn3EaulKoCLgU+CKC1zgG5mW73ZKTzQRNCZNJ/jKgdnOzMuDkyXgbth0iEoqicC9oBbY6Pv7Kivg0KTdNLK5qIZyEVgZpILX2Zc+kf6mX94hYq7/8D+voPce7bGrAeW4lrvMhF7edweks1N569gKbKCE2VEc5sqwZgdWslX7gpGPipuSrCRUvrx8vXuAIg6DHQVhksa69s5+Y1NzPZJW2XnNSxWF4zfbLo1a2VrG6tPKntHElbRdtxH2MZ1rQQh+Bk6aL6OAA18RA1RwjKyUxDsaA6WliXaaFvF05sr2ye2E5T/OQGnYrZMRbahdmVosFrdCzHC3EIAqkqZk/7oBw7lwAQM478AaaUmnKNyuHtqIYypmwHwC6MHj92bI8kGjKP2SarlMIyp4eNedhloJMDafJ9Y8snb2Psw8hUJpURk8rI2PEwp6wzeZtjz9/AHn99j8c0zCMez1hoIrAnnzs72vM5WYYyMJQxrZyGETS42MVtAj9+eYqwjcVAL/BtpdQLSqlvKqWO/q6aBZl88LkRnvSCxQpBnnUdMm4apcPj4Y6RJWZMzB25vK4F7dtobbCqooq4EwR5fbSWjvg5RLLraamKsKpuOeH8aTRXRlhq/AHp3R9jeWMldYkw53bI+NVCiPlRjCC3gLXA17TW5wAp4K8Of5BS6hal1Aal1Ibe3t4i7HZCplAjD0+alm0iyHM4XgaDCFErUiiLpjo00YtgYU012ougvRhLQxByIRVWNMbreMsZLfz+unaUUnzo4sV86k0rUUqxrKGOmFFHU4VMPiGEmF/F6H54ADigtX6m8PcdHCHItda3ArdC0EZehP2OGwvyiD0xsUQ8FNwOToqkpwQ5QGOshS5nE2hFdSQOfvA1vkPnSRHUyBcnGvjA2YvG17lsRQOXrQiaQz5+1XJuOqdt/CukEELMlxnXyLXWB4H9SqmVhUVXAZtnut2TkXWDppXJbeSxQmg7bo68zmITGa+lAzTHgk75SkcwDANTJzB0BVWFAbNSEWivPOyqt0naamJcuFTGKxdCzL9iXRD0MeC2Qo+VXcDNx3l8UWXGgnxS00o8VAhyL0deZwgbjcTsiV78LfEgyM1Cz/6q9DvRGuzhoItdOgxtVUcPciGEOFUUJci11huBIwzlPzfGauRRe1KQh4Pad87L4eksISNKPDQR5I3xepS2CBfOeNfai1BK4Q+9DEAqoqiPSY1bCHHqK5NL9AtBbk00nUTtoBdKzsvhqTRRI0FiUpDXRCqoiVTRmgh6r7xnfUdw1eSDweXVqQjTunoJIcSpqLyCfFKNPGQaoE3S7igol5hVOd7cAlAdTVAbraEqHIy38fvr2gEY/L/BLEF+LHHCfVmFEGI+lUWQO15w1drkILdNBdoi6QY17Aq7kopJNfK6aCV/fu6fj1+RNcYrnOyM10mzihCiNJRFkOe8oPthzJ40/6IqBLkXjHFRE6miMjKpaSUaZ1X99Ksm/dEkvglXLH3TLJdaCCGKoxgXBM07xw1q5LHQ1Mu+lbZIFWrkdbFqKsITQR63j3zxqZfKYEdMPn7uJ2aptEIIUVxlEeS5QtPK5H7iAYucDtq862M1VIYnmlGiVpQj8dIOZqQsvqgIIV4nyiLInULTytjVnGMMrPHp2xrj1VSO1ch1aNqAQGP8dB4jdvgHghBCnLrKIsjTuSDIDx8WVDHR66QlUTteCzf00YPay7qYcRk/RQhROsojyAujH04bUrJwLldrk6aKqmAYUq0wmQjy/MGD7Hnve3EHBsDL4+fArJjTwRuFEGJGSjrItdZ0f+7vadx7ALQ1bbxoQwXBrr0oNbFQoSeLjaUmatzZV14hs+E5nK1bITuMl1MYFRVz+jyEEGImSjvIczmGbr+dldv2YzG9Fm2O1ci9KJXRINQNZRO1Jk56esNBrxZvaBCdHsTLG5iVM5+EQQgh5kppB3k+OJEZzmYIHWEuC1MFbeamjo9Pl1UXi7OiYWKmHn/vi4XfL6GHDoGvMKtqp21LCCFOVaUd5LlgQtWazAhhY/o8jWahacVSEzXwuB2jIjwR+v7IWI28H6+/GwCjWoJcCFE6SrrDtM4EM7xHHZeoNT3IrcLJz7CauO/T532aOl/BnR+Ba/83/ugIAN7wEP7gIQDM2vpp2xJCiFNVSdfIKdTIo45Pwp5+gtIq1Mij5kSb96Vtl3L6cA+88AM4+Ap+Mvgw8EdG8PqDKeiM2sZp2xJCiFNVSQe5djIARB1NRWj6Ccqx7ohxe2ptXWdGSHaHITOIn0oB4I0mcfv7ADAbjz9bvBBCnCrKIshjWag+wtjhlhGc7KwITb0vuWEz+x+pI7t1C1462IafTOENBu3lVmPzbBZbCCGKqsSDPGhaiWehJnz0GnnVYUHuDQ0Fvw9146eDbXipLN5gsNyslZOdQojSUdpBXmgjNzU0GbFp99uFGnltdGrIjzen9PfhZ4MBt/y0gzucxLDBCMtYK0KI0lHaQV6okQM0+aFp94+NvVIXrZmy3E+nAXCHBseD3Mvm8UbTmLEjD6YlhBCnqtIO8txEkDf6059KqNC00hif2lTij7WLD4/g5XwAvKyHl3Qw49M/EIQQ4lRW4kHujN+uyzjT7q8NL0B7UdqrmqYs9zOFdvGRJH5OB9vKgzuax6qQkQ+FEKWlaEGulDKVUi8opX5VrG0e16Qgj40mp929ouI8ktv+jqbE1JOdfjZYzx1JoV2FGfaCzQ37mJUy8qEQorQUs0b+CeDVIm7vuCbXyCMjo9Puj4ZMQFF7WHPJWLt4fjBoYhmb9U17CqtaRj4UQpSWolyir5RqA94CfAH482Js80RMbiM3Cl0KJ7vujFYSYYumyqnNJb5TCPJC9ls1ERgIlpk11bNSViGEmC3FqpH/H+AvAf9oD1BK3aKU2qCU2tDb21uUnepcbvy2PzQ47f6qmM2NZy+Yttx3glET3UzQQ8Wuqx6/T/qQCyFKzYyDXCl1HXBIa/3csR6ntb5Va71Oa72uoaFhprsNtjmpacUfHjrh9fzc1M8bu3libBWrTsZZEUKUlmLUyN8A3KCU2gP8GLhSKfWDImz3uHRhire8pfFGp7eRH42f11P+ttsWjt82G+TyfCFEaZlxkGut/1pr3aa1XgS8C3hQa/3eGZfsRPZdaFpJx8AbTZ3YSl4ePz91kd2xfPy22dRSrOIJIcScKOl+5F4hyHNRjZfMnNhKuRS+qzBjE+d5rcWngQpq6VbTwqOtKYQQp6SiBrnW+mGt9XXF3Oax+GNBHlP46ekXBB2Jzo6iXQO7fmL8FaNpMabto0wfo6bpGGsLIcSpp7Rr5PmgjcSNGXiZ/HEeHdAj/QDYTROzABm1LRghjRn24QijKAohxKmspIPczTn4gBsL4WV9tNbHXccfC/KWoC1cWaDsEGZYYUUVGCV9SIQQr0MlPWenm8vhmeCHw+A74Lpg28dcZ2yyZbsl6J1ihIPgrlhqgVKzW2AhhJgFJR3knpvHNcG3g0vwdS6HOsEgtwo1cjMcXBRUf2ENKKmNCyFKT2kHeT6HZwB2MBGEdhyIH3vQK390GACzqhYjHsWoLbSV1y0FJWORCyFKT0kHuZ93cU2gUCP3sylMjn2J/ViQG1W1mHUNGPWtwR1v++ZsFlUIIWZNaQe5GwS5sgtPI5s+/jrJ4ApQo7KW+AXnY9bVBXdYMqGEEKI0lXaQex6uAarQtOJnj391p58Kxi03qupp+fznZ7V8QggxF0r67N5YjdwIBSc4debEg1wlErNaNiGEmCslHeTa9fEMMOxgvHHtHP8y/bH5Os3jnBQVQohSUdJBjufhmmCGC71WsicY5AaokLSJCyHKQ0kH+ViN3AydRI08k8Ww5cIfIUT5KO0g9/2gjTxSCPITqZFnHYxQST9tIYSYoqQTTXk+rqmwwzEAdO5EgjyHEZYLf4QQ5aOkg1x7GtcAOxqcuNRO9jhrBPN1GuGS7nUphBBTlHSQK0/jmWCHC0GePZEg9zDCcqJTCFE+SjrI8TWuCeF40Cd88mTMR10l72NEjj2wlhBClJKSDnI11rQSKQT5iTSt5DRGRGrkQojyUdJBjh80rYQTwaw+Op877ira1ShpWhFClJGSDnLlgWtCNF4FFIaxPRbfQ3sKo3ABkRBClIPSDnIfPAMiiUKQH6+NPJ/B90BFJMiFEOVjxkGulGpXSj2klNqslNqklPpEMQp2Qvv2gxp5JFYJaHT+2BMw63wG7RkYkejcFFAIIeZAMTpUu8CntNbPK6UqgOeUUr/VWm8uwraPyfA0rqEIhRIo4/hNKzoZTCqhCleCCiFEOZhxjVxr3a21fr5wexR4FVgw0+2eCOWDbyqUFUaZGp13j/l4nSrMDhSJzUXxhBBiThS1jVwptQg4B3jmCPfdopTaoJTa0NvbO+N9ad/H0ArPUGAYKAP84/Ra8VMjQVmiEuRCiPJRtCBXSiWAnwGf1FqPHH6/1vpWrfU6rfW6hoaGGe9Pu0Ht2y88A2VyAjXywjRvMQlyIUT5KEqQK6VsghC/TWv982Js83h0Lqh9+0YwJG0Q5Mc+2emnx2rkMqmEEKJ8FKPXigK+Bbyqtf6XmRfpBBVObPpG8BSUoU6gRh5MBWfEZJo3IUT5KEaN/A3A+4ArlVIbCz/XFmG70wx873vs/8hHAdC5NBCc7ARQlkK73jHX99NB04qSIBdClJEZdz/UWj8OzMmUO34mS/KBB8j39EC2EORjNXLzBGrkmWAdI14xuwUVQog5VFJXdiYuvxyA5I//HT3aDxD0WgGUaRy/Rp4JmlZUvHL2CimEEHOspGZYCA89jBXXJO/6AbHaJADaDGb7Udbxg1xnghmEjJgEuRCifJRUjVz5LhWn1ZM6GMHv7wImNa1YBtr1j7m+X2iOUYnqWS2nEELMpZIKctb/CYlbvoj2FKlNnQD44zVy87hBrjPBeOVyslMIUU5KK8iVIrZuHQDpfcHl9tocq5GbaO84NfLCxBNGVMZaEUKUj9IKcoKrMo2IgTtcuCDIDJr5DfsEauTZoO+5kvHIhRBlpOSCHMCuCJFPBr1Vxk52YploTx9zPd9xUCYooySfthBCHFFJJppVHcN3g6L7ZjCRsmHbxw1y7eRQJdVPRwghjq80g7xmUvfBQtOKsiz0sXsf4ufyGNacXLskhBBzpjSDvL5m/La2CkEeso5fI8/lUXZJPmUhhDiqkkw1q3FiGFxtBSculWWjfYXWRw9znXMxbHPWyyeEEHOpNIO8uWX8ti60katQ8JtjDGXr51yUBLkQosyUZJDbLW2T/ijUyEMh4Nhjkuu8hxGSs51CiPJSkkFuLVg0fltZQYArO/jtH2MCZj/voyTIhRBlpjSDvG3p+G19eI08kzzqetrVGGNNMEIIUSZKMshVRQNmOOhrqKzDgjybOup6vqtR4dDsF1AIIeZQSQY5poVRmHZThaKF30Ggj00ecSTa1RgS5EKIMlOaQQ4YiaCt27SDAbDGg9w5SpB7eXxXyTgrQoiyU7JBrivD5E2wzEKQFwJaZ48S5PkM2lMYERn5UAhRXkqyC0c656JOq+YhuwdrrEYeDn7rwlC1h9O5NNqXGrkQovyUXI18c9cI537+fg601vDNq01CZuFk51iQH+Vkp04F45eraHRuCiqEEHOkKEGulLpaKbVVKbVDKfVXxdjmkWRyHh//8Qtk8h7dXhDI9tiVneHgb509co3cT44AYEQkyIUQ5WXGQa6UMoGvAtcAq4F3K6VWz3S7R/LFX7/KjkNJ7Gg399rrAQiPB3mhRp47StPKWI08Fp+NogkhxLwpRhv5+cAOrfUuAKXUj4Ebgc1F2PYUv7+uHSO2g593/xuvOBcAEBoL8shYjTxzxHX91CgARjRW7GIJIcS8KkbTygJg/6S/DxSWTaGUukUptUEptaG3t/c17Whpk80zo18HIMNBAELGWI08CGidCy7RH7rjDrKbJz5LdDoIchWViZeFEOVlzk52aq1v1Vqv01qva2hoOP4KR/CVF75CZ7ITQ4fxzQEAwoWxVoyxNnIni9aag//rHxi642cT+08HJ0ENaVoRQpSZYjStdALtk/5uKywruqsXXU1DtIHvbLyHIbUVgLBZuFIzMlYjz+INDaHzeXxnopnFH6uRxypmo2hCCDFvilEjfxZYrpRarJQKAe8C7irCdqc5u/FsPnzGh6mw6saXhazCnJ2Ftm/t5HB7eoLbB14af5w/ViOPS5ALIcrLjINca+0CHwXuBV4FfqK13jTT7R5LTWgiyCNjw9hGgiYTnXNw9+0Ibk8aCVFngiBXiYlp4oQQohwU5cpOrfWvgV8XY1snoi7aAIWMjlhjvVaCk5g6n8Pds6lw250o41iNvKJqroophBBzouSu7ARoijWN3x5vIw9HAI3v5HD37QRAuxNB7qWC5Dcqa+esnEIIMRdKcqyVBRWN47djhZmBlBXBDPl4I0m8oaBHi85744/zU8FgWkZCuh8KIcpLSQZ5e+XE5MvjNXIzRKjKxekcwLSCqzu1OynI02lQGiWjHwohykxJBvni2ubx29HCzEAYBuFqn5H9A4QqfQC0648/zk9nMUIKpdScllUIIWZbSbaRt1ZWo71g1MOoNTHjT3jpEvysi9MX1MSnBHnGwQiV5NMVQohjKslki9gmeFWF2xPji0cuvhEA7Qe1bn9KkOcww+YcllIIIeZGSQY5gKWDII8Wuh8ChK74wymP0a4ev+1n8xjhkmxJEkKIYyrZIA+rGrQ2CFkTtWyrrgGrKhhzxYybaG+iRu45LkbUnrYdIYQodSUb5FVmBzpfg2VMPXkZXn0WAKG6OHqi0wq+42NEZJo3IUT5KdkgXxa+lvSuP8c8PMhXrATArouhvUlNKzmNEZMgF0KUn5JtNK6Nh7ENe1p3wsrrr8dPpzEHX0B7oLVGKYWf1xgxmeZNCFF+SjbI33haE1pPXx5dczrRNf9A36feDihwXbQCP68w4zI7kBCi/JRskF++spHLVzYe9X5VuHRf53LgjAAKIyZBLoQoPyUb5MejwkGQ+46DHgymljMSMha5EKL8lOzJzuNRhUv3dSaJPxwMomVUSJALIcpPGQd50ENFZ5L4Q/2AjEUuhChP5Rvk4UKQZ1P4o0MAGJUS5EKI8lPGQR4MV6szKfzhIQDMSpnmTQhRfso2yI3xIE/jjw4Hy2R2ICFEGSrbIB+vkWdTeMlRAIzqumOtIoQQJal8gzwS9Bn3M2n88SBvmM8iCSHErJhRkCul/lkptUUp9ZJS6hdKqeoilWvGVDi4HF87GfxUKpjmrULayIUQ5WemNfLfAmu01mcC24C/nnmRimOsRq6zGfxUGsPWKFvGWhFClJ8ZBbnW+j6ttVv482mgbeZFKo7xIHey+OkMpg3IfJ1CiDJUzDbyDwH3HO1OpdQtSqkNSqkNvb29RdztUfYXHWtayRbm65QQF0KUp+OOtaKUuh9oPsJdn9Va31l4zGcBF7jtaNvRWt8K3Aqwbt26I4xbWFwqHA/262Tx0g5GuGzP6wohXueOG+Ra6zce636l1AeB64CrtD7SwLLzw4iNBbmD7+QxZb5OIUSZmmmvlauBvwRu0Fqni1Ok4lCRQpDnnGDi5YjM1ymEKE8zbW/4D6AC+K1SaqNS6utFKFNRqGgCAD/n4DueBLkQomzNqL1Ba72sWAUpNhWOgdLoXA4v62PGI/NdJCGEmBXlewbQCqMMjZ/KoF0wE9KHXAhRnso3yM0QygB3KBn8WVU5zwUSQojZUb5BrhTKBHc4BYBZVT2/5RFCiFlSvkEOQZAPFYK8RkY+FEKUp7IOcsMEd8QJbtfKyIdCiPJU1kGuTAPtBtcomXVN81waIYSYHeUd5NbE+CpmY+s8lkQIIWZPeQe5WQhypTFqW+a3MEIIMUvKO8jt4OmZto+KyXydQojyVN5BbpkAmGEN0er5LYwQQsyS10WQG2EIZpYQQojyU9ZBbozVyKMyhK0QonyVdZArOwhwMxaa55IIIcTseX0EuYx8KIQoY6+PIK+Iz3NJhBBi9rw+gryyYp5LIoQQs6e8gzwUtI0bVVXzXBIhhJg95R3kdtDl0KyRi4GEEOWrvIM8NBbkMoStEKJ8lXWQG6EwICMfCiHKW1kHuVmdQBkaq7ltvosihBCzpihBrpT6lFJKK6Xqi7G9Yqlcv4qlbzmE2bhgvosihBCzZsZBrpRqB94E7Jt5cYpLhWLYcQ8i1fNdFCGEmDXFGITkX4G/BO4swraKa8XVkOqF6oXzXRIhhJg1M6qRK6VuBDq11i8WqTzFVd0OV/wNKHX8xwohRIk6bo1cKXU/0HyEuz4L/A1Bs8pxKaVuAW4BWLhQashCCFEsSmv92lZU6gzgASBdWNQGdAHna60PHmvddevW6Q0bNrym/QohxOuVUuo5rfW6w5e/5jZyrfXLQOOkHewB1mmt+17rNoUQQpy8su5HLoQQrwdFmzpHa72oWNsSQghx4qRGLoQQJU6CXAghSpwEuRBClLjX3P1wRjtVqhfY+xpXrwdOxZ4xUq6TI+U6OVKuk3OqlgtmVrYOrXXD4QvnJchnQim14Uj9KOeblOvkSLlOjpTr5Jyq5YLZKZs0rQghRImTIBdCiBJXikF+63wX4CikXCdHynVypFwn51QtF8xC2UqujVwIIcRUpVgjF0IIMYkEuRBClLiSCnKl1NVKqa1KqR1Kqb+ax3K0K6UeUkptVkptUkp9orD875VSnUqpjYWfa+ehbHuUUi8X9r+hsKxWKfVbpdT2wu+aOS7TyknHZKNSakQp9cn5OF5Kqf9WSh1SSr0yadkRj48KfKXwfntJKbV2jsv1z0qpLYV9/0IpVV1YvkgplZl03L4+x+U66uumlPrrwvHaqpR68xyX6/ZJZdqjlNpYWD6Xx+to2TC77zGtdUn8ACawE1gChIAXgdXzVJYWYG3hdgWwDVgN/D3wF/N8nPYA9Yct+xLwV4XbfwX80zy/jgeBjvk4XsClwFrgleMdH+Ba4B5AAeuBZ+a4XG8CrMLtf5pUrkWTHzcPx+uIr1vh/8CLQBhYXPj/as5VuQ67/8vA383D8TpaNszqe6yUauTnAzu01ru01jngx8CN81EQrXW31vr5wu1R4FVgwXyU5QTdCHy3cPu7wFvnryhcBezUWr/WK3tnRGv9KDBw2OKjHZ8bge/pwNNAtVKqZa7KpbW+T2vtFv58mmDyljl1lON1NDcCP9ZaO1rr3cAOgv+3c1oupZQCfh/40Wzs+1iOkQ2z+h4rpSBfAOyf9PcBToHwVEotAs4Bniks+mjhK9J/z3UTRoEG7lNKPaeC6fUAmrTW3YXbB4GmeSjXmHcx9T/YfB8vOPrxOZXecx8iqLmNWayUekEp9YhS6pJ5KM+RXrdT5XhdAvRorbdPWjbnx+uwbJjV91gpBfkpRymVAH4GfFJrPQJ8DVgKnA10E3y9m2sXa63XAtcAH1FKXTr5Th18n5uXPqdKqRBwA/DTwqJT4XhNMZ/H52iUUp8FXOC2wqJuYKHW+hzgz4EfKqUq57BIp9zrdph3M7WyMOfH6wjZMG423mOlFOSdQPukv9sKy+aFUsomeKFu01r/HEBr3aO19rTWPvANZulr5bForTsLvw8BvyiUoWfs61rh96G5LlfBNcDzWuueQhnn/XgVHO34zPt7Tin1QeA64D2FAKDQdNFfuP0cQVv0irkq0zFet1PheFnA24Dbx5bN9fE6UjYwy++xUgryZ4HlSqnFhZrdu4C75qMghTa4bwGvaq3/ZdLyyW1bNwGvHL7uLJcrrpSqGLtNcLLsFYLj9IHCwz4A3DmX5ZpkSk1pvo/XJEc7PncB7y/0LFgPDE/6ejzrlFJXA38J3KC1Tk9a3qCUMgu3lwDLgV1zWK6jvW53Ae9SSoWVUosL5frdXJWr4I3AFq31gbEFc3m8jpYNzPZ7bC7O5Bbrh+AM7zaCT9TPzmM5Lib4avQSsLHwcy3wfeDlwvK7gJY5LtcSgl4DLwKbxo4RUAc8AGwH7gdq5+GYxYF+oGrSsjk/XgQfJN1AnqA98sNHOz4EPQm+Wni/vUwwufhclmsHQfvp2Hvs64XHvr3w+m4Engeun+NyHfV1Az5bOF5bgWvmslyF5d8B/udhj53L43W0bJjV95hcoi+EECWulJpWhBBCHIEEuRBClDgJciGEKHES5EIIUeIkyIUQosRJkAshRImTIBdCiBL3/wP5YX63xoZLvwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -356,13 +358,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.2</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.18.3</td></tr><tr><td><code>qiskit</code></td><td>0.34.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.3.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Fri Feb 11 09:37:47 2022 CST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.20.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit</code></td><td>0.36.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.4.1</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Jun 29 10:42:21 2022 CDT</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/docs/1-tutorials/tutorial_2_H2O_molecule_statevector_simulator.ipynb b/docs/1-tutorials/tutorial_2_H2O_molecule_statevector_simulator.ipynb
index 3136c10..86b2f54 100644
--- a/docs/1-tutorials/tutorial_2_H2O_molecule_statevector_simulator.ipynb
+++ b/docs/1-tutorials/tutorial_2_H2O_molecule_statevector_simulator.ipynb
@@ -113,7 +113,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Classical energy =  -75.72890671869291\n"
+      "Classical energy =  -75.72890671869246\n"
      ]
     }
    ],
@@ -361,9 +361,9 @@
     {
      "data": {
       "text/plain": [
-       "Ground state energy (Hartree): -75.70352751309613\n",
-       "Schmidt values: [-0.99371757  0.09108812  0.0650258 ]\n",
-       "Optimizer parameters: [ 1.07552606  0.30566044  0.04598305 -0.77945442]"
+       "Ground state energy (Hartree): -75.70487920958833\n",
+       "Schmidt values: [-0.99355654  0.09998218  0.05337577]\n",
+       "Optimizer parameters: [-1.06503717 -0.74395818 -0.01527152  0.78056925]"
       ]
      },
      "execution_count": 11,
@@ -373,7 +373,11 @@
    ],
    "source": [
     "calc = EntanglementForgedGroundStateSolver(\n",
-    "    converter, ansatz, reduced_bitstrings, config, orbitals_to_reduce\n",
+    "    qubit_converter=converter,\n",
+    "    ansatz=ansatz,\n",
+    "    bitstrings_u=reduced_bitstrings,\n",
+    "    config=config,\n",
+    "    orbitals_to_reduce=orbitals_to_reduce,\n",
     ")\n",
     "res = calc.solve(problem)\n",
     "\n",
@@ -394,7 +398,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -413,7 +417,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -432,7 +436,7 @@
     },
     {
      "data": {
-      "image/png": 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IXMmwsJCUssv+3gM8Sn37gUKhmKZ0J3IAtDUqj2CqMx5C8DywWAgxXwjhwirsD+v9I4RYBoSBp2vSwkIIt73cDJwObBm+r0KhmH5UhUB5BFOdMfcaklKWhBDXAg8AOnCjlHKzEOIrwFopZVkUrgR+K6WUNbsfA/xICGFiidL1tb2NFArF9KUnkQVQ009PA8YsBABSyvuA+4alfWnY+pdH2O8p4LjxsEGhUEwtDiVyOHVBs9892aYoXgE1slihUEwIPYkcMxo8aJqYbFMUr4ASAoVCMSEMpPI0B5Q3MB1QQqBQKCaEoVyJBq+aWmI6oIRAoVBMCMlckaBnXJohFROMEgKFQjEhDGVLNKjJ5qYFSggUCsWEkMwVaVAewbRACYFCoRh38iWDfMlUoaFpghIChUIx7iRzJQDVWDxNUEKgUCjGnbIQKI9geqCEQKFQjDtD2SIAQbfyCKYDSggUCsW4o0JD0wslBAqFYtxJ5myPQIWGpgVKCBQKxbgzpIRgWqGEQKFQjDsqNDS9UEKgUCjGnaFsESEg4FIewXRACYFCoRh3EtkiAbdDTUE9TRgXIRBCXCiE2C6E2CWEuG6E7VcJIfqFEBvsz4drtr1fCLHT/rx/POxRKBSTQ08ix6d++wLbepLMDql3FU8Xxuy3CSF04HvA+cBB4HkhxF0jvHLyd1LKa4ftGwH+HVgNSGCdvW9srHYpFIojz8Pb+rhzwyEALlg+Y5KtUbxaxsMjWAPsklLukVIWgN8Cl7zKfd8I/FlKGbUL/z8DF46DTQqFYhLY1ZeqLLdHfJNoieJvYTyEYDbQWbN+0E4bzmVCiJeEELcKIdr/xn0RQlwthFgrhFjb398/DmYrFIrxZnd/VQjmKiGYNhypxuK7gQ4p5fFYtf6b/9YDSCl/LKVcLaVc3dLSMu4GKhSKsVPrESghmD6MhxB0Ae0163PstApSykEpZd5e/Slw0qvdV6FQTA+yBYOueBaH3VNIhYamD+MhBM8Di4UQ84UQLuBK4K7aDEKItprVi4Gt9vIDwAVCiLAQIgxcYKcpFIppxoFoBoArTm7nlPkR5RFMI8bca0hKWRJCXItVgOvAjVLKzUKIrwBrpZR3AZ8UQlwMlIAocJW9b1QI8VUsMQH4ipQyOlabFArFkac8v9CbVszkzMUqfDudGJdhf1LK+4D7hqV9qWb5C8AXRtn3RuDG8bBDoVBMHsm8Na2E361GE0831MhihUIxLqTKL6NRQjDtUEKgUCjGhZTtEQTUjKPTDiUECoViXCh7BAHlEUw7lBAoFIpxoewR+NWMo9MOJQQKhWJcSOVL+F26mnF0GqKEQKFQjAupXEm1D0wERhFu/SB0vzRhp1BCoFAoxszPntjLQ9v6VPvARDDUBZv+CLsfmrBTqF9NoVCMma/eY806PzvkmWRL/g7J2rPypwcm7BTKI1AoFGMiXzIqyyo0NA6YJqy7CZ74lrWesSdbmEAhUL+aQqEYE/FMsbKsQkPjwObb4O5PWcunf7rGI5i46ffVr6ZQKF4zP318D7etr04YLFA9hsbMwI7qci5eFYKM8ggUCsUU5Gv3bq1bj2UKk2TJNKZnE2y7F5a9GWauqIaCANKDqo1AoVBML5QQ/A2YptU19J5Pw6P/Cc/8wErP1gpBf1UIhrqstoPs+L/SXQmBQqEYNxa1BibbhKmPlJYX8PxP4TsrIdVrpZfbADJR0N328kB9wX/3pywvYZxRoSGFQjEu/ObDp3DcnEYApJSY0kTX9Em2agpy6wdg8+0w83gYOgiaXQyX2wAyg9C8BHo3WuJQGyoSGjQvGneTlEegUCjGhdMWNRP0OAG4afNNXH735ZNs0RQk3mmJAECfNfYC05qjqeIRZGPQvNhOG6z3CGatmhCzxkUIhBAXCiG2CyF2CSGuG2H7Z4QQW4QQLwkhHhJCzKvZZgghNtifu4bvq1Aopg5SSm557gBfuXsLmUJp1Hz7hvaxN7EXKeURtG6Kc8fH4eGvVtfNmvunu6shn0wUgm3gbqy2Ecw83tp21mHF67gw5tCQEEIHvgecDxwEnhdC3CWl3FKT7QVgtZQyI4T4R+C/gXfa27JSypVjtUOhUEw8z+yJ8oXbNgKwZn541HyZYgZDGmRLWXzOo/jdxYU0uPzW8vb7oJAaOV85FJSNQzENvjD4m6F/K6T64LjL4erHQJuYIM54HHUNsEtKuUdKWQB+C1xSm0FK+YiUMmOvPgPMGYfzKhSKI8z9m7ory/sHM6PmSxfTACQLyQm3acqy5zH4xnxI9oBpWDV7Y5ReVeVQ0MBO69sbgVIO9v4VSllY+qYJEwEYHyGYDXTWrB+000bjQ8D9NeseIcRaIcQzQohLR9tJCHG1nW9tf//EjbBTKBQjI6XkT5t6OGV+BID9UUsIPn3eYh773Fl1eTMla9tRLQQDO8DIQ2yfVdOnJkzmb6nP27zE3me79e2LwMJzQHPCRx6BxedPqKlHtLFYCPFeYDXwPzXJ86SUq4F3A98WQiwcaV8p5Y+llKullKtbWlpGyqJQKCaQbNGgL5nn9UtaEAIO2B7ByvYQ85r8dXkzRWtbqjhKKORoIGPH/NMD1WWwev5EhhVzZY9g/9PWtzcCb/0OfPGQNchsghkPIegC2mvW59hpdQghzgP+FbhYSpkvp0spu+zvPcCjwInjYJNCoRhnEllrTqGwz0Wj18kB2yMo9xSqpewRDBWGjpyBk8m6m+Dmi+vTyt0+MwP1g8S8YQgMq8y2LLW+N/wKXAFoWgiaDg7XhJlcy3gIwfPAYiHEfCGEC7gSqOv9I4Q4EfgRlgj01aSHhRBue7kZOB2obWRWKBRThKGs1cul0esk4nNVhKBhhBlHy20EKbtxtDvVzSV3XEJnsvOwvNOWPY/Cj14PvZuh8zkrnm9WZ2KteAGZwXqPwBsBX3N1XXNCeL697IBPb4TGI9uMOuZeQ1LKkhDiWuABQAdulFJuFkJ8BVgrpbwLKxQUAP4ghAA4IKW8GDgG+JEQwsQSpeuH9TZSKBSTzJ7+FP9+12ZMuytog9dB2O+CAauwH9EjGBYaumX7LexJ7OHOXXdy7YnXHiHLJ4BC2hKAcAf8wu4T07XOLuil1SDstwv5sheQHgR/jRD4mqp5PCFwuMEdgA8+AK3LwdNwZK6lhnEZWSylvA+4b1jal2qWzxtlv6eA48bDBoVCMTF8/9HdPL6zOuFZo9dJ2FcNWQSHeQSmNA8LDXWnrN5GXod3os2dWP50Haz/BbQsq6bVtgFkBquFfCVtWBuBr8YjWPnu6niCuadOrO0vg5piQqFQHMb6AzGiqQJnL2tlMJWv29bgcRL2WV6AQxP4XPXTSORKucpyPBfHMA12xqxukf3Zadjjr38H/OhM+MenrJHBAP3bqtszg/UvjynH+zM1s4YODw0d8xZreonz/sNqC5hklBAoFIo6Htzcw9W/XAfALz+0hmjNi2fAbiPwWx7ByR0R7HBvhbI3AHDzlpvpy/axO7EbgN5070SaPj5ICULAn78ErceC02P16e/bWt/oW6a2DaD2nQF1HkEMPI2QS1iDxRrnwAVfm/hreZWouYYUCkWFgVSeL9y2sfKmsd6hPPFhU0sHPQ4GUlbaWUvre78YpsHDBx6uS3tg3wOV5b5MH1OWno3wjQ749vFglOCFX8PWu4Y1+g6bAtrpt2YPzcWreQBKeWuEMECqH2J7oXEuvO5aOOYSphpKCBQKBev2x/jp43v46j1bSOZK3PzBkwGIpQvE0gVmNlRfSu/QNc5f3grAW06YVXecRzof4avPfLUuzZQmAK9rex29Gcsj+MCfPsD773//hF3Pq+bxG2DDLdbywbVWY2/iQLXLZ23YZ3g3ULD6/w/sqq7XzhcE4ApC8hDsf9IaFPbGr0P7yRN7Ta8BFRpSKBTcuu4gf1x3kJagmwtXzGTV3DBOXdCfyjOUK3FCe4ieoWrs/8IVbez9r4sOCwuV2wKGE3KHWNG8gud6nsMwDdb2rq1sk1IykB2gxTdBA0VN8/DpGTb8BrbeY83v07QIVr6rPo4f3QvStOP7dqE+1G3NFdS0GAZ3AsISgo1/qO5XPsah9db34vOsQWIXfBWOe8fEXN84oDwChUJBIlugYJj0DuVoCboRQhD2udjTb4U3FrcGD9tnuAgA7EnsqSy/acYpvH7O6wFYGl5Kq68VQxpEc9Va9VBhiCvvvZJz/nAOO2I7Djve38TgbqvgNkqw/pdWn/6Xfg9fa4G7Plmfd+/jsON+qwZfLrxrp3suvze4tsfPoC1y5cZgTyMEZtQfd+igda7fvhsaZsOF34DPbofjr7DaHaYoSggUiqOUJ3cN8M4fPc3+wTRxu0G4ZMpKj6CI38WefmscwJIZr+7NY3sTeyvLV0dW8d2zv0uDq4EVzSvoaOwAYEP/hkqeax+6li2D1tCh2sFmG/o20JPuqaxLKckbNb2Xilm471/gV5dbffsBfvNOeOg/YO9jcNe1Vjhm021W98yudfWGZgatGn8xXRPOqfEIykKQjUHabtcoTwhXng7C12R9yjg8sPVuWH8znPYJ+PhzEBwmFK+Sklnipk038YMXf4BRO0htgjiqQkMDPZ043V4aw82vnFmh+DsmVzR4z0+fBeDZvdGKEADWYDGsqSSe3mMVjrPDr9z/3zAN9g3tq6z7U33oms6tb72VkCeElBKHcHD/3uqcky/0vcDbFr2N23fdzlB0NyRicNzlfPLhT3L+vPP5h6VX8rlH/4k17Wfxy62/ZEXTCq454RpeX9LguR8BsH//X+kJRDhxqIti4iD+9AASMFI9OMqFe37Y5He1hX6mJq4fmAmpHhisifuX2wCG7JlzyhPE1Q4MA5hxrNWz6O0/hmPe+or3azQGs4N88uFP8tLASwCs613H10//OjP8r01UXg1HjRBI06Tn5+8lVOhh6OLv0b7y3CntqikU401XPMvmrgRP7hogmau+FCWaLlTmEQIqg8UiAddhaecsax31+N3p7rpauz9h1ejbAm2VtGObj+XP+/9ct9/yyDJuB+J7HoKND1Jc+mZi+RgDmT5e/OWb2BryEttzD16Hl/3J/fx22295/aw3Vvb/ya5beXJoN23NQTaau7j6wP38vKMd14br+e+CQcbv48JMlHQxzT898k8siyzjg9kB7m0I4JKSS5Npnt73Z1Zk+vlz80x0kSQ5tI1SYwMfSQxZjce1NNV4BOXJ40Jz4Z2/sryP0NxR79Gr4Z499/DSwEtcf+b15I081z93PR/9y0dZGlnKZ1d/lmbv+FdkjxohEJqG45x/xXn/NaQ/fg03ntXB7td9gob8U1x0xjs5LrQI3PbQbiUQir8DOqMZXjqYYNOhBB88fT6/eHofP318L+1hb13D76hCUDN6OORzsvs/L0J7mb9GLGfF2L/d28/8YpGGOTVzT5oGrPs5J7eu4sX+F+v2a7/rn3C0tZHID4E0iQ3tByCe7CJm5AAvPblBjm06lkZ3I4l8ou49voPZQWL5GEMuy967ElspCkHRLPINt0HBHeLCzkPsGtzK091P83T307zoKbHebU2nvdbj4b7HPsMqDdYLoLmJ+YU8Tr+PP/l9fCAxxFvSGaKaxha3i9ODMxGugDVCeN5p9txA7aOWG8/3PM9Th57igys+SNB1eFvLcKK5KA7NwUXzrcb4iCfCJx7+BLviu1gzcw1vX/z2VzzG38pRIwQAy065gCuf/RH/Hv0U8f3dPDL3myS9cX71wP28kG3k0N1Z7mtJMW+/A4eA+MfezpbeR2jfO4N5mTDr18yHpR7ae3axbJ8DzwVnEph7GgN7tuPcvotFJyxkZ7YPZ9TLrNb5yLY23I0RXLoDUcyQMwROtxdNmgh98kcTKqYfUko2dQ0R9DjoaPaz+VCCFzsT9CfzvOuUdlqD1W6eV/38OXbbjb3tYR+DqQKGKTkYy1Iyq3Pj9w3lSOWrHkLY77S/q0IQ8bvQh6nA3sReNvRt4G2L3wZU5xUKGSYLmldAdF818/4n4d5/5rw3f4OfDrumJsOgUXMRt/ePJSwhiBUSRPVqM2bIHaLR3Wi1JdSEduKFJIY0MWz7eozqgLaDukb5KuI1NfvtTo05xSIRw+Qpr3XPtmsSsI7R6XTgkpKMprHZ7eItBcHPA05uCjVw5ZYbOeu0D9PedjJzhRjVA+hKdfGddd8hUUjw1KGn2D+0nxvOuuGwfDdvvpnfbP0NHz7+w7xjyTtI5BOE3KFKY/xZ7Wdx61tv5fK7Lx+1V9ZYOaqEAMCjWQ+5pwAFZ/WBSSc6yR70sDCuMWfQ6vd834u38dBCwbefGaRtADbHnuInQuei50yOe9jg056HCc5ZztJtG3jPz3XWnZnhTkeQqx4yOQjsWODgWxc38K3/jfPgySYXvCh5dFkDF65Pcs/JgntOc/LNXxX544kNvGO3D/PcMC+9lGPhtl7uO7mRy17qJmdCqinCjRcPccbjDhZ2hZn7sU9yQu4WMnujdD6TY7PpYdMaP4sPNOKNbmTbSVdx+uanWT57iEe8ARy79lGgyEOnr+C9ThfhUort67pB0ynobpZ96PO0x5/jiT8/T9wRxdnRTvOBVnbOyqItvgTfrEM4O/fS8OenGDhmEWfOPZvk40/T421jcHEP57YtJ1HQWLbyDNK7Bnn8gR+hN0DsDWfRvGcXx2hNPFXwc9Kp55D2JGh95ikejZhEWmexJOUju2MrWxvSzBoyafCdTJO3SIIeIu4QsTzsap/FG1raGKCJ8NCLFEOzKWSC+ENB0pm9+I0wT+5/HK+vAXfvEE5tJsvPeB2HhvKUYpuZ63NzYPdmct5mHG0teIwUOELMbVvBwRf/RKB9JdGUhIH9uL1B2ma3QzbGIEWEy4vu9+PW3QRcAdKFDJ3RAqapMa/Jh9/toC+Zo9nvopCKsaEnTwEny2c10BxwY5qS9dv3snrpPOsFJQ43OW+ErJkn7Amz51A/P336EEUTzls+g7OWtvDZP7zEJSfM4p6XDvE/7ziB7z60k18/e4DPnL+EjiY/7/2ZFdvf9B9v5PO/XctAXxc9hDD0ft53yuJKN8we40mWz29ny94mYplCXYNwGacu2DvsTWNlT8ClWwXRMW0N+FyHFxW37byNmzffzCWLLkETWmXG0YAElr0ZHvm69ZrFQGtlaoblxuG15ibDICScDBlZAKJDBwGrgI/VVJhCnhCNrkbiubjViOvwglEgVkqP+n83hSAHZIQglqx6KGlNY2GhSJNh8JLHbadVbSsJQckuiOO6DvNWE49bXUIfPvhX7jfznOcSfJm31J3vs499lvZgO59a9SmeOfQM9++7n7DbeqXn7vhurrj7Clp8LVxz/DV894Xv8ub5b+aOXXdwKH2I57uf5x1L3kE8HyfkDtUdd2lkKcc2Hcuu+C4mgqNOCMLC+jN4C5DXqyMmU4U0ZsFLc8306YWSRAoNl53Nl4PVLKAxsxNNCkp56Ev3sqAowRTscZ5E09B2Cg7obgZvqkRDIoGnZHLqFvBkBCv3WI1Wxx4yeTZq4B+UnL8ugTeWYG7fJrJbW2hOmpz/QppgFPJBmL9jkL6S4LQXiwRzWQbuvRXa72foxVbMXTodDg3/kGSuPY1LuOcmmmMZ+jZIWhsFjSlwlWBt01rM9gH2PdXIsu06eZd1H543vs6AuYOlT1kNgntndDG3F7adKPijvpaB7iQffMBg1XqJtm4/2egjeIomHcCv3qnh/P1dzO7UkFcvYP8N+5hnWEL6g/SzXLTeoKOrSFvJyb0rfsnTc/v519+b/OwjOl3Ngu/+zGBmnyS1UBDYLTG5hX6gPyyRpRLFpJNHz9Zg/iCl1Ak817KDnYFGrv5+A4mTjuHXs57gS7eYXH+NzpV/NTlji1XI/ebsK7h79gB75zzGF/6UZ8UGBzpwz8mCm8+zCpfPPefl5IeSbJktmJuUiCEoAsPrXA+eKFi5R7LnDQVuONZHoyH55N4OCusHCF96Ne/akeKLa3/Ij94mec9Gk9bNGi8IQVOwFbd04TB384VTGvn0tl7cPoP/M38moZ4ic09/HemHn+R1BZPfzlrG93o/y/MPfYaLtr7ILQfeQyH7MPf8v30c0mazxqfzp5f6OMV5EgsWPc9AaRbfXbeTbMONLPTnKDh9/PyAwc2dGn9ufwfNB56DpgHmG3le7zqNy5/7Em8o+djTOoduVwnn4BryRiPX+nZzSf/t/LO4huflYkAjZAvBsbMaAfjyW5eP+F8aKgwhkaSLaYKuYMUj8AfbYNG5lhDs/Su8eIvVLx8QqW5umHkef9p1Jw8GrJfZhDxNNCKIm9YfLZa2JqhLGDkGa4Qg7A7T4G4gWUxSTPfj9DdDKUfcyPFKxHSNRLp+VHPINAmb5ivuG9c0uPAbxF/4Jhx6gmguSkmWiOaibB3cygce+ADvXPpO1vaspTfTawkVEMvH6r4PJg9SMAtsjW6lL9PHtug2nu1+lojHClFF81a4K5aL0ehuPMyORaFFPHnoyVe097Vw1AlBg7SEwFOg7AUCkDYBKfDUTKsiClYGj93+5ctDuOTEZ697CoLefAyjYLmvuYKOPwdJL+xvEiw7KPHlrIKpyRaYstCEc5JIxnoIWxJWmqNo4MvXpz27TPDm5yXLkyV8eetPoaWsg5j+Dpzefayb6eLYzqrhkZT9x5CCYBoOLi2xYLMDfw4Suo5e8tI9I8+ZZw3w4l0t6Ok0xRoXfFbMcpGDWUi6rNrim/MNGCRoi4OjaNLYkSGxz0dDBmRWI5gGY7AXYZg8sVxwxhbJ2/oLuFMOshknjQXwJqI02Z03mpOShQEDv72+9JB1n+44fT7LYgeZv7NIwXQigKakpNfh4EKxlducbg7KIk3JAdI9+5jhA03CzLhkTX+RrhYH4SRkOzvxLohTFIJSxkO0oUTAlJzdW8SRb+Fn7hS5AavgikQlIidZv0DDOzvHaQUHD7gk+x0OLt4S4qwdUVxpjWfiVgGZ0AUnubdQ6HSTfOY5FjoEC7sloQEN50EIZmDrPDC9gjk7+nFnXJyTyRKNB3H4Nd6yJ8/cATi47Wnm9AJofHz7Tr54RZzLfv4CAJe0386Cgym+9lEHvf4BhjRBIAXXffs5zjPhYFMXW5f9mv9cX+S514H7mAAHI0vpLj1H945ncG94Cf/KEHmZ5R/0PzEj28cMYEGkl15XE7JjGy7gmN5+vtfs5/zC/7KtKYBMr8DlsGq5Zy1tYfN/vBG/u1pMFI0iN6y7gZ3xnZVujalCiqArWPEI/KH50LbS6me/8Q+w6y/VP9VQN+ebRU4s+XkQK9zj9PlpNCVd0gpPxexpKEwk+53VKa4b3Y2VmnIiM0CzL0KhlCNNtpKntVSiz+EgYhhEa0QkrunEsv04NAdISUkahAyDsFHfNdMhNEqyXhwSs0+E5kWV0FXJtjORT7A9tp10Mc29e+6lL9OHU3NWbcwn6o5TMKsVz12xas2+PK6i3MaSyCcqXW1rWRxezJ277ySWixH2hA/bPhaOunEEQawf0VuQdekZ4/Bb4cuDkBJvRQgk4SIVISh/S1sI9HQafx7Sbkh7rO1+O49un85hP2PuPDRlZV1aIa/hs58Vl/189jVaYtQ2JCvH0NPWH84oOtBckpTXqBMwR7H6IHuKIDwmwmkSyEniQseRk5TcErcoknWDK5vDLFSv320LoD8Hed0yxJm2DCvfC3ejdR8DOXDnLI8jPWg9+Ptbrf1np/MEsxKtYD1ovmyJgK1RrRmTubkMPnvdb/+XD87SONQkcRdB2PcgmIWEptEiEgxpGqUC6EgcmRQBe7+WjEkgB9mQwZAfnOkkmm7dJ1e2RCrsIOQr0JQxWe47xbpP+erxkYJNHYLNx5vMnN/LM6skm080aFqwEFfavjf5as1ByAQUJEY8TkMxWTmOMw+DrSa3v1lwx4eXkXrn6QDMjAGGoJS2l4EWOwT5p1WCYFZiJA5Wjj+7L4MuIZfTieuCgiaYEQfd/mnnDMLKjSkasnDJliIfTPiYIS+0nouHDnHoET8/+06RJS9pzJT9SBNuTZzHkC4q9wXgP1vmcl/Qxx1ByxvU3Icgth++uwqx7V78639sTcJm80TXE/xq6694tvtZ1vVaffOT9vWn7ZfQ+JuXWDNqdpwJO/5EHclDkOylIWhNTRHxRMDfTGOxQFxY54lmq/H//c6qCIXd4UohG88NgjdC3FdfIM4vWs/ljJKBt6a2H9U14rmYdQx7KuyI1Ag76l+xOd9/+OvW43boKZ6P16XH8rFK4d2b6UUiKZiFSr7ytpEoi0ktZUEYKTQEsLxpOataV03IW9/GRQiEEBcKIbYLIXYJIa4bYbtbCPE7e/uzQoiOmm1fsNO3CyHeOHzf8abBVmVP/TxaZEqHN9768lY+rWa9qWBUCy+7to9dcDqzWXw5yHhsIchRKaiG48oLwtn6mkcyc7iD1heyvmfGq39GRzYDCIw8CJdJ2isP268W3WWiu0z8OejT3bizBobb2ifvAVeuCPnDH4WALVQ6Thg2A6UzYAtBlkrhnohaF9tj/zd9ueo2gGBOVo7ZlJFE8mZF3MoUvAVinvr74s9C3PZY4rqGN2fdb1c2R8D+DVoyEqMgMN2SpBeChQxSt+wJZqHoc6K7TYyCRlqfW0mvJeWFqGb9BglNI2SY6O4a+2vyD5V0QCATcRrsAjCQtT6GWxI2DBL5BFJYO5XFHkNWRN5dgpIGfSGBw4SGdHUwVtkz9OaoxKqD9r37/RnWvSh7mUZBo0mkSGcsj8VI5hGBEgk/tHdqaEiGDng55v4tlHIaiOpNH9AsNeyyC1xNz8LtH0VGd/ODhz/LRzfcQObQC5X8tV0/pf0y9vJbyFLpXlymxFXuZz//9RzG0CFI9eAKzMTr8NLktQZlhbKWyAPEhtWkyzR6Ggl5QgDE8wnwNRHz1PfC6Shaz2nINAjVCEFM14klu2iUglDBuuaQfwahYTXrOeFFltdQQ6Vgz8eYHagKRTwXH7GwL3sCtR7BvIZ5I15TmYgnQjwXx5RmpbF4OCfPPJmb33TzKx7rtTBmIRBC6MD3gDcBy4F3CSGGBxU/BMSklIuAbwHfsPddjvVqy2OBC4Hv28ebMPyGXbO1hcBh13ZyIwqBrNT6rXVozhfx5WVlHUAU7YIpl8Ofl6TdgrRHoAHNQyMX0npREKlvoyOTrn/TU0mDaNA6dlPNf0PP5cHTgJEpYLolKc/Ld3d1OS0hCGUkA8KFNyeRLsuuklviyRlohcOPUS7EHSKAka2vwTjcJnmXJJCTFbHLJK3HaaBBgJB4hwlhIFstTCMZSSRTf29MAXlvlsFhwhbIyUohkdC0yjHd+ULd8YyChnBLEl4IFjOYeq5yXsPnQneZGHmNQebgkFVRKpP0wqBu/QZxXaPRNNGd1RpD7bUki3ZhMZQgWLDf3ZuVlj1uSbNZIJaLIRi9IbN8zqQ9VmtO5uBh24M1z0j5Wg/Zg1nLf14jrxEiSTJj9xjKmphBk54weOzfMJN2owEiY/3OH3jQ4JRtJhKrsDRtcTBEluKBp3nR7eL7ASdP+rys63meHbEdSCl5tPNRzm4/u87GlF2DT2cGCEgT7No+899w+AUPdUOyF4IzCblDVp94fzONhSw5TeNxr4ee4hCCw5/HsDtcaXiNF4fAFyHu9tXlmV8WAuEkZNQIgTdEPNNLOHaAUNG6KeGTP0L4jH+uP4eneo4yyUKSglFgKD/E/Mb5lfREIcFgbpDh5IwcuVKu0jYA9UJQFpPa8QDzG+dTkiV6072UZGlEIZhIxsMjWAPsklLukVIWgN8Cw+dZvQS42V6+FThXWH2jLgF+K6XMSyn3Arvs400YPqPeI2gtWdWzfKnmwROShN8q6MuF/UDQbiMo5irhHr/9J/PYIQNProAvp5HxQMauSbaMULmJ2qP1mxL1BVE+bYlRzPZWUx7rAxCMV7fp+SJ4QhjpLCW3JF3tMUjc3lerqVW7XSamU6MhBzHNgT8rEW67AHCbeHISR16Q9lXtifuqQqBJH0auug+A4TbJeazCqZyvkLJsTHpBc0vcGa0uZBWo8RBCWUmjXbAl7P9y2gOGs0Cfr/pbDHmhIWMJQBHIaFqlAPfmjErh3BwXIAW6yyThEzQW0pT0EkircDZ8XksIChp9RS9BwxKwVM29S3kFCd0q4OO6wxICrVoSB7KSRjumnC5a1+pMJWkoWEY0ZiwvSHObhAyTeC6OUw5haKN7bEmv5YkAzEkd/tKWWi8kbAtnorH+eKWCTtAcIp7WcAgHImtS8EhSXoEvC1khSOetB9KVFyAl574oWb1zZLsSuqhrpL1+5y1c+9C1pItpksUkJ7aeiF5TX7tny2/4+aaf05XpwWea1Vcttiy1Ruo2LaoevJiGQhKCM/nK6V/ho8d/FHzNtNr39WMzW3m8FGNWoDqrqRfrXOXuo4A1vsDXRMxZ/QH9UtBi/5/DDXPrPYLjLyMemkNo3pmEF1mBh1DjPMKRJXXX3uhuHDH+fmDoABLJgsYFlTRTmuy3xzwMJ56P13kEHQ0dleWFIWsQ2sLGhZW0ssCU52oaqbF4IhkPIZgN1L6R+qCdNmIeKWUJSABNr3LfccVnj3wsewTlB7BQ4xEIpyTvsryBckx8oMHapzGfqhRmvoog2AVTvoQ/Z4lAuXBuSRz+Z+uzf+PwUH2tx7CFoDdkrac91eN4h6rbtEIJvCGMZJqCW9YVZuVju/3VGrzPYZBzufHnJHE0dAm60/6TuKwGbVcOUg1VW3vD1vXphkQrejCLglKg6jKnPZD2QtOQrIQ9zKRlo3SZSJeGPlT/ePny0Ji2zmHVnq3lcigp54GsI0eiRgi6I9CQs2roCTs8VL7/7oKsHC8St7Y5XSYpLwQKGfJaEXfRCsuU/AF0twlSsG/QwGdajeHxSPVcSS8kNIEBJDVhhYZkvLI9mIV5dgw6b7epaKZBS8oqrWfGrD+Uw2USMk1SpRSOUpy0d3SPLeWFIXv77CHrXP01ZUCtEMxIQd4Bfl/1tzVcElkUeAtDJDJ5Gt0NOHKCnMcW0azV6yWftzydQBbcRatNpzHrpCkh+cxtBq0xid8uOOOabvWUsTmQH6Q/00+iYBVsjUM9NGrVmNn9/Wu5Yd0NPBnbSsCU4LbDNULAu26Bd/0OPvIwvO1H1YsJzOTUtlNZEFoA/mYuTKX5UXcf348X+Ehe57oTPo5me+snuC0XKLTpDkK3W+87vj0Q4AP9j/FLwxLPIA5C/lbC537ZyrvsYkLHXGr9RkIjruvEMQmFOirhoLAnXAk1lan1OmopT51R6xFA/dxKtSTyiTqPoLxfo7uRVp81Oru2QXh+Q70QTEeP4IgghLhaCLFWCLG2v/+1v+7OU7JKb6dhFXItthAYxeqfVXeYFN2WCJTDQIMNdpw2n6lrPLa+7fWc1esn7bYajAFaR/AI+u0G4IZh24Rdo+4PWdvTHsi6wQR8SSutLyTQihJDb4BCgVyNWOSckPDb7RWBamHhdxqk3X5LpIqWzS6X9acXLtMSvJwgWysEtg2+PAQydk0/UHXDkx5B0lNt+ATQkjqmAIcTDJeGTB0ebmuNW9/eXDXE0xO2zpVzCwadpTph6wkLvDnJEDpx+5V+tYVj+fyhuHUMt9Mg5RG4TAPDMCt5i/5GdPuaO3uyBPMCdwkykWqBlvRCUqcShgqZBnqp+qwFsjC7VMIhJYVC9dpmxS1laouW761Bo92jxpRJhl5mmp6kV1Q8glkJy9iuSHV7sCZ81ZjWSXrB56zxzBqt88gCeI00IXy484KM7WkEbCEoFqr3rsF2ckJ5J8sPSE7dLvnfHxosSdq9dnSNmGNYmFKWKu8dbnjiO4SyI8fx/aYJrpoJ6mavguZFMPsk6Dij5sJq5s3xRXABp+VynDnvPD55aC9n3fZpHjnQxZ9nvpl/mXU+7xxK0vLwf+IZ3MUM4WKb10fW08DuTDchd4gFLccS8bXSuOAc69q8kUqB3x5s5487/0g0FyXkDlUK2ZA7VOm66XP4KmkhT4igM0jAGcBpjzsqC8GswCxcWnWg3WgNt7F8jKF8dVvZIwi7w5VzNnubKzX/slD85KWfWHYME6iJZjy6j3YB7TXrc+y0kfIcFEI4gEZg8FXuC4CU8sfAjwFWr1798q2jL4OnWA36ewoQNE18polRrGqi7pKUXNJuI7AKmHItzVfIYBash8M3LEQUSVt/0JILMnbcvnmE56Q/ZH170/U67LDXy7X6tEcghSDjgYB97L6Q1daXy1qlR8qtVWqctaEkV6DaLc6tQ84ToCmXgKKV7rYLE4fLRJNW4TDgMzE0B7op7cLZCp805Ky8/f4wIVIUHJBxaSS8gqWpqv3OjEbWI/GYDvJODW1YYzhAxM7vzgm8ecjX3I+cWyPqELhr2gjiIYlAUCoKEnYBXRurD9sheJ/dFdfnMkjaZZhZkATt3y/rDVseAVCKJwhFHECJQnMjkLPvtxUr73NYhWajIdHNKGDFcgM5CJVMGkyTUk3jenPKuqfl39rrMivx6bxIMeStUbZhpGraCGbFDAqOcqWj7DlV8/oyTpK+Eg3SIOO2ujHrwRL0OzAKGmFPkua89ZdOemHII3CYkpjhwJOv3ruyEDSV9LrjX/mg5IV3WGG4eKAZqO9RcSBpjcwNmiYh0wDqxQIgIGV1qpbhNM6pyTizulwOHZ35WTjn3+DkD8HdnyKSGYDZa5i55EL+bc6p0LwUGtq4vZDEoTnwOrwYpkHRLLJlcAuGNOho6OCCeRfwurbXMcs/i2guypqZa7hp800MZAdY0byCwdwgDs1Bk7cJn8OHz+FjaWQpL/S9QMQT4YJ5FzDLP4vnep7D4/Cwrncd31n/HcBq1A15Qq/4prVyKEkXOoY0mB2YjUM4iHgiFSGIeCKE3WES+URFCGL5GG/qeBPLIste9vjjzXgIwfPAYiHEfKxC/Erg3cPy3AW8H3gauBx4WEophRB3Ab8RQtwAzAIWA8+Ng02j4ipWu7F4C+DHJGCamDVtBJrDxHDq+NI1bQT2n9OTzZOhXghqG5QBSjUewUhYXUJH17JeuxAu1/RTHqsQkkj6GzVAkrHDLlGPs1L4p7xV70B3m+ScGp6iCQ4PBa8f3RREknat1Wl9O13Vwlp3m3YIKUev7R0HchBJWvfsgK+VxXSS8kBO14h7D7+OjAdLCFwOvMMKklrcOYE7J8gDQyErLed2AAZ5JxR1S/CyAWtMgyxolekGgtnR713AaVB2RESh2p6Q8rag2w3kwWKGYMYJ5Cg0twG9pN1g2iNL9zusv0XI4UV3V39chwlNOZOQz6x0GR4Jv7NE0R69G3XmSXq9h92nMkn7NzOxvNSBYLXNAKqi5zetxveYTxAyTbJ292R3sEQJKOU1Ip4kXrt3WcIrSNnPYLqoV7olVxq0AU+mSENWYgj4y0rB+S9qICUxXSPubWBGrodBXa/0WirHwxtMk7BRL/JzArM5mOrCbZrV0NDLEawRgrYT4Ivd4LI9ztmr4KOP179QZsFZ1V1r5uvRNR1d01k1Y1Ul7ZtnfROwQi9vaLcarK9YekVle8EosKp1FQ0uS7B+esFPaQu08VjnY5w26zScupMLOi4gXUyTKqS48LYLKZl2t1TfDJo8TTg1J12pEeusQNWDWBBawN743koYKuKJ0OSxQl1lUejL9DHDP4OOhg7e2PFGrj3x2le+f+PMmIVASlkSQlwLPADowI1Sys1CiK8Aa6WUdwE/A34phNgFRLHEAjvf74EtQAn4uJRyQiffdhaqf2xvAfwOid+UlS6gYHkE0mXiLWg1QmB9u9I1IZdKW0H9n9xwiboG3OGUu4SORFGHmO1Zl49R7gwiXNX2gKz9hx/0OCppVpuCXTi7TNJuHU2aODQ/hYDlZsyI28eyuwu6a8IMTpdB2uXBn8vRZ4eGAlnJjKx1E3b5ZnMua0l5BT0OR6UmW0vKC07DScbpJkTq8Azlc+UFel6Qc0LeZ9mcczkBA4Qg5bF7ObpNwKq5HvBaNtd2SR1Og6NE0lPtblkuSGPumZXQULCQwWd31c36Wsi4qLuW8iCmkCuI7qp/NWEka9WGtdzoQtDgNMB+irucOrmXCw35BFKzwkMN2bKHUOsRWN+N0gqRdTZB2LAag0kInEGNElbPobBIUjxkVfejfijYYp8uaMw1rf5BgRyE7XYVRypHY9q69v6QQDMF7iIkdAf6AJy9xyQYhZ+do2NqggNDNR7BMCFYHV7GwVQXKd0BjpepBV3zV9j0R/AOi8O7fIfnHf5WsXHCpbsqDbYAx7UcB8BlSy6ry+d3+vE7/Tz+zsdx6S6iuShhT5jPnfw5DGnwkQc/AlhhpUwpg0f34HP6iOai3LLNev3l50/+PG7djcfh4RMnfoL2YDsNrgZ0oTO/cT5N3ibCnjBOzcndb7t7Qq731TAuI4ullPcB9w1L+1LNcg4Y8T1tUsqvA18fDzteDY58tlI38+TBr1keAcV6jwCntHsNWTWmWMDaXspUH86yAPiHFUwlp0bWXRUHE6sxxhTWKNh4ULNTwRDWYLPyd9pNpTtoygMOhL0uEa6ql5CPWd1xBrwOsM+V9ghy9nLODSmPjk6RAn6MoPXHmxmztpu6h4x043NWddflMEm5fOCMVeLa/hzMyFmlaberCVyWXXtdHnI1BVbGBb6C1fDpNN0kXdXSL+22BnGXRTXlse9r1uphZbis+5FxuSmHaZJeDU2a6Pb1tA9IuiJuZklJa0KSscdpQLVR1PqBfZUa9cJuCOV0wGRfMUTBbRXwi+MHabV/xoNmiKV2PD3iiRDNRVlvzz0jTH/l/KbHRMtpzOjSWSIdeIes8QK6HXoquk2cdvilsaEBw7DiROs8buba9mRdAAJvQWI4QC9B1uUATLL2NWQ9VVEyNCs01GhIQsKJL29a7TKOAIbHAHS8ER9ZrLEEP2i5nVt6LXfo4bCXRbkioPEnR5Cl9gvoG7IwJ2UCAmFKWuOCIV/1nC1pk5Tp550/6UFI63oeOQa6I5L9SdsjwElY1BcdJ/lmcwcQczhefvbethOszzQiYLd5zPRbXszJM613Dv+fU/8P1z93PSfNOInHux5nRfMKANy6G4fm4Lo113FK2ymV49TOGvrUu57C5/RxzfHX1L2xbbI46qaYcORzlHs0egsSn0fikxKtqFPSwWGA5pIIl8BbsAqSrLtU6Q5ashtOh7zWn7QtY/UUSgVcBFLWn810OfBQIO+0emhEg9CchMEgtAxBwV+tTQ02WA3KA41WbT1f0/U04xHM0n2kPVah4rDHJwD0v7iTMLA+BH5hYuqWR+B2a4DBd2Y2sHpvEbcEU/opNVru6Os32QLl9NElnAScfZR9HI/TIOpvgOwhTDuefvWfTFwyBgji/haE3SPqtoCX19d0UY2GwdcLMa/AqweJ1fTv7g6DWwPfIWu9sxmOOQhDB3ykZ0DB5QJKJNxBrA5lkPC5QeRwewzAwT/eZwIu3mFXtTfNE6zYb13LnpmClXsl0q2RcM2h5B3CBN7zqAmYFHR4slsnFmjB0A0u2/WY9TsBD8VbOLlBo98vOaHlBB7pfIQnfF4wdbb1+zjWITHcGlpLATpdtDzuo/x33jRPq9hgNBs4u+w4/KylDHR34pSSJ3xeLvKbSCQHWsBdlLQPgLOhiBl1EmpcSKPYh3QVAA2n2yTptY4z1ChpSgj+41cSw2viKxRJeWHuvDMouR7BFBotK1YSvetFit7lOPO7WRHVARcdPi+XJKNAI6enQoDVqn5swUd4KAb2nJyzopLBJhdJr/UUtKUlBc2FkAX6Zpi09mp88EEDlwH/es0BBBAItBJy1IcFj9es3zurHT2z6l6x9AouX3I526LbaHA38MVTvoiUEk1oeHQPTv3wNpQyPqd1v5ZGlh4pc1+Wo04IRC5LzmlNveApgo6PmQkTT8pBLGD1+9cdJkWnE5CctFOSd4Npx5cT+6wfsC8Ei7rhO1YbEpvmFVlhR0I0f5BIKUo8CDOisG2u4LTNko0LdNbsLjFfFnA1BykMZHluqc4FL5V4epnGpc9IUi0GC1wG6RZBdkEr75t7Nr3td5DtdNN0fJh5gU72tlkDhTbNdTMU8nORzOM5toi5UHD26W9l67476JnTyOMrg6wpOQkd93bmL38Dzs2P0F00OBAIsWTlxRTdDmZld7Kh6wHSBJh1zIW0nX8xu568k6uCnfC+dqI71hHO6eTCrVx9xfnk53RRSDzN5bOPZ3awl0LTPNYmuxCRIN07N5FY4uSa8/6NLfMhM/sXFFedzH4OcF6ogb3PPUND0s3+007npL4iBw52MTAvxOkLVtCwppE1DWEWDv6KWDZC5N9OZH9yJ8udQZra9vN0LI1fDJFgHv5SFvf8JNv7czQ4V+NvLzHHuYCs34Von80PsjvZ6X2QFulidvuZbCgZ3LzkFPyBezHOO8RQT5y5EQ9bEgf49sq30phaQysDXLnkbDqHOtlzYCuehiW0n9vIwdgOfJe24PQ7Gdi7k9klE6fHw6FkJ0s7ZpHv2oc3lee40y5kaMte8mE/2kmns8Ao8If+TSRkkabzw4TfNYDLV8JZMAklcnjnzWLo8ef44nvfx2dEkULgDnrvvY/T3nQubcs7KPTeyJJLLiP6+9vp0HTkUBKzeZB3X/Jh5r/1H4hFb2LovvvR3vsb9B+eyeBfDxFb14xfSiQZvvW+eyGbYscf38w5+qkMcT9oGkGthZWzTyfxwoMANCWhdMwckj6rtn/MUJHNXp23At2LS7T2upjfC4YOFzyZJdqkoc2fxWqZ4eRsD8/bDeEduRzv0CJcnh3WYPZ3jiY0ljct5/ozr59sU8aEkDXziEwXVq9eLdeuXfua9t1zxRXsO7CJtrikvwGaMqDZVeL1CwRz415mLcnw+/AJnP7sSwRDbQwsdbFjaZpTnjNxJ3T2zlxJ6k1LCT74Aj6jF8echcTOPp7k2oMsnrma9jcs58W9e1iigbtzF87jFuH0LscVcuGMd2O6XHiCs8mmsoiGBnyaSQmJEDq6ftRps2KMFLu7ST/9DLktW5BGCde8eTRddRXSNNl1zrmUenoAcM6di5FI4Fu5ktRjj1X2T73ldP573ma+8r04vtdn+HY4xNV3FthwuZeVtx4+R8oxXznZemfwzgd58rzrOLTup7wjuASKGXB64YN/OmwfxdRACLFOSrl6ePpRVersfulxYru30NUGuluAS8d99jkccjnoaW+l9YQTOeWkc3A5HHxh2L7nA3zUWj6unPiWYZlOrS7ObrGHlK+uH46Pv9pI5vdU4+hH1Q+hGFecbW2E3v42ePvb6tKFprHw3nsYuv9+MuvW4160iL7/+R9Sjz2GY8YMSr29AMybezy/fs832P29Mwie8TXe1r8RuBv3+VfDrd85/ITBNshZIbzTW1eBHoEDT1nbFp0/kZeqmCCOmvLHNE12f/4zhKRJy9Uf5+QLP4LPfrXdolfYV6GYrmh+P6HLLyd0+eVIKXHN7yD74kt4TziBgx/7GACOSBPOxhAIgRGPs0zMZEDXueCUd3NAfKd2jjqLlmXVl7t7GuHi/4UbL7DWX03XUcWU46gRAk3TWHrD93C6PKxZePxkm6NQHHGEEATPOYfgOecgpcS9dCn57dtxtLYgdB29oQEjHsMsFHBGIgQ8DYiGBkjUj4qUSy+mcN+3MKNOvJ5GaD0GTv04PPO9kbuBKqY8R40QAMw7ZkLns1Mopg1CCObf+gdSTzxB4PXWdNF6OEwpFkPm8uhNVi8zZyhEcZgQ7DjvzZiJBNDCgmtyuFuBmVbXSUaYOE8x9Zk2cw0pFIrxRTidBM8+G2HPNKqHwyTv/xO5zZtxRKxpEPSGw2fBNBMJwu97LwDJvz5jJbYeY33H9k243YrxRwmBQqEAwDXXemFPqa8PYbef6Q0jzxvUeOmleFetYugBqxsqrcuhcS6c9+UjYapinFFCoFAoAGj72ldp/8mPAcs7ANAbRxYCR0sLvlPWkN+2DWma1rQS/7QRll10xOxVjB9HVRuBQqEYHeF0EjjzTDr+8Hucc6yZQjXbIxAeDzJXnUvF0dRUCRuZ6TR6UPUWms4oj0ChUNThPe44HBWPIASAZ8WxCGd1ygSh6+hBaw4eM5k84jYqxhflESgUilEJvOH1lPr7mfEvnyO3ZQsHPvihyjYtYHkBRjI5wpsJFNMJJQQKhWJUfKtW4VtlzfXvbG+v26Ypj+DvBhUaUigUr4rhPYjK7QKGEoJpjxIChULxqtAClgfQeMkl9rolBGZq9BcQKaYHYwoNCSEiwO+ADmAfcIWUMjYsz0rgB0AD1nubvi6l/J297SbgDZQnoYerpJQbxmKTQqGYGISus/jppyqeQLmxWHkE05+xegTXAQ9JKRcDD9nrw8kA/yClPBa4EPi2ECJUs/1zUsqV9mfDGO1RKBQTiCMcRtjvdNZsQTCTyiOY7oxVCC4BbraXbwYuHZ5BSrlDSrnTXj4E9AEtYzyvQqGYZITbDU4nZkp5BNOdsQrBDCllt73cA8x4ucxCiDVY78jbXZP8dSHES0KIbwkhRn3rtRDiaiHEWiHE2v5+NbGVQjHZCCHQA4FKaMgYGkIaxivspZiKvKIQCCH+IoTYNMLnktp80nrV2aivOxNCtAG/BD4gpSy/7PYLwDLgZCACfH60/aWUP5ZSrpZSrm5pUQ6FQjEV0IJBzGQKI5Vix5pT6P/OdyfbJMVr4BWFQEp5npRyxQifO4Feu4AvF/R9Ix1DCNEA3Av8q5TymZpjd0uLPPBzQM0TrVBMI/RAACOVpNjVBUDywQcn2SLFa2GsoaG7gPfby+8H7hyeQQjhAm4HfiGlvHXYtrKICKz2hU1jtEehUBxByh5BWQjKk9UpphdjFYLrgfOFEDuB8+x1hBCrhRA/tfNcAbweuEoIscH+rLS3/VoIsRHYCDQDXxujPQqF4giiBQOYySGKnZ2AEoLpypjGEUgpB4FzR0hfC3zYXv4V8KtR9j9nLOdXKBSTi3v+fFKP/ZXczp0Ala6liumFGlmsUCheM77Vq6FYZOieewFrSmrF9EMJgUKheM14TzoJoPKuAjXdxPRECYFCoXjN6IEA3tUnVdaNtBKC6YgSAoVCMSbaf/hDljzzNI1vextmOjPZ5iheA6plR6FQjAndnpVUCwRUaGiaojwChUIxLmgBP2YqhTXJgGI6oYRAoVCMC7rfD1IiMyo8NN1QQqBQKMaF8otrDNWFdNqhhEChUIwLmt9+h3FKCcF0QwmBQqEYF7SAHwBTdSGddighUCgU40K595DqOTT9UEKgUCjGhXIbQfaljfT93/+reg9NI9Q4AoVCMS4429oA6P/WtwAIv+8fcM5onUyTFK8S5REoFIpxQQ+F0JuaKusyn5tEaxR/C0oIFArFuOFesKCyrGYinT4oIVAoFOOGq1YI1MCyacOYhEAIERFC/FkIsdP+HvH1REIIo+btZHfVpM8XQjwrhNglhPid/VpLhUIxTXEvVEIwHRmrR3Ad8JCUcjHwkL0+Elkp5Ur7c3FN+jeAb0kpFwEx4ENjtEehUEwivlNOrSyr0ND0YaxCcAlws718M9YL6F8V9gvrzwHKL7T/m/ZXKBRTD8/SJSz8y18A1JTU04ixCsEMKWW3vdwDzBgln0cIsVYI8YwQ4lI7rQmISylL9vpBYPZoJxJCXG0fY21/f/8YzVYoFBOF5vcByiOYTrziOAIhxF+AmSNs+tfaFSmlFEKMNoJknpSySwixAHhYCLERSPwthkopfwz8GGD16tVqpIpCMUXR/PZUE6qNYNrwikIgpTxvtG1CiF4hRJuUslsI0Qb0jXKMLvt7jxDiUeBE4I9ASAjhsL2COUDXa7gGhUIxhdBcLnA6lRBMI8YaGroLeL+9/H7gzuEZhBBhIYTbXm4GTge2SGv8+SPA5S+3v0KhmH5oPp8KDU0jxioE1wPnCyF2AufZ6wghVgshfmrnOQZYK4R4Eavgv15KucXe9nngM0KIXVhtBj8boz0KhWIKoPl8yiOYRoxpriEp5SBw7gjpa4EP28tPAceNsv8eYM1YbFAoFFMPza88gumEGlmsUCjGHc3nVx7BNEIJgUKhGHc0v4/0E09w8JOfmmxTFK8CJQQKhWLc0TxeAJIPPjjJliheDUoIFArFuFM4cKCyLEull8mpmAooIVAoFONOqa86pKikZgKY8ighUCgU4077D3+A96STACh290yyNYpXQgmBQqEYd3wnncTML30JgFKvEoKpjhIChUIxITjbrCnKij29k2yJ4pVQQqBQKCYELRhE+HyUerpfObNiUlFCoFAoJgQhBM6ZMykeUkIw1VFCoFAoJgznnNkUug5OthmKV0AJgUKhmDBc7XMpHujEmmxYMVVRQqBQKCYM19x2zFQKIx6fbFMmFFkokHz0UbIbN022Ka8JJQQKhWLCcLa3A1Ds7ARAmubf5ayk/T/4AQc/+o/su+IKBn9+07TzgJQQKBSKCcNlC0HBFoLe/7qe7SetJvmXvzD0wOTPQyQLBfI7d2IMDY24vXDwIGahgJlOk9+9m9gf/sC2VSfR87WvU+zrY9uJq8i88ALFri6Ey0Xw/PPp+8Y36Prkpyh2VxvJi4cOUThYfQGjLBTIvPAC2ZdeIrt5MwBGPM6et76VQ9d9gVIsNrEXPowxvY9ACBEBfgd0APuAK6SUsWF5zga+VZO0DLhSSnmHEOIm4A1U3198lZRyw1hsUigUUwfnnDlA1SOI/fKXABy89hMA+J5+Ckc4/IrHMVIpjFgMTBPh8eKc0VrZZuZymKkUjubmun3MXI70U0/hnDWLUn8/uc2baXzb2yn19TL4k58y+4Zv0vnRfyT91FPgcND+wx8SOON08nv2UOw6ROL22xm67z5C77qS+C2/BcBz/PHITIbsCy9Q2LcPmc2S374DIxbHvWQJs7/9LQZ/9jMGvvd9Um9+kgV33oGrvZ3uL/07slCg/Uc/pPPqa/AcdxzRG28EQG9qov0H36d4qJv8zl3kd+4i88J6wldcQW7bdtq+/O9ofj9GMokeDI7xFxmZMQkBcB3wkJTyeiHEdfb652szSCkfAVZCRTh2AbVVgc9JKW8dox0KhWIKonm9OFpaKHR2IovFw7bHfv0bQpe9nUP/8nla/unT+FatQkpJ6uGHyb74EsLtIvL+9zP4ox+RuONO9OZm3PPnM/uGb1aO0f/t7xC96SZa/vkzZNauJXjOuQzdfTfuJYuJ/eYWXPPnUxoYwEwmAQFIkg8+SKmvj/zu3eiRCEY0Sm7zZgJnnM7gj39C6vHHwTQBSD38SOVc+Z07Aav2bkSj1nIsihGPo4fDCE2j+SMfwXfSava/+93kNm7E1d5OqbcXWShQOHCAzPPPk9+zxzqgEBixGJ0f+zjCYRXHjZdeSuKOO+j7v98EKdF8PkLveAf7Lr+c2d/9Dg0XXDDuv9NYheAS4Cx7+WbgUYYJwTAuB+6XUqo3VigURwnO9naKBzrJ2SGQWnJbt5LdtInM88+z/93vwbdmDb7VJzHw/R9U8rjmdVA81E2pvx8zn0fz+eqOUdi7F4D47/9AsbOTwu49FA8erISjCgcPgi1CRixa2a8Ui2HE44Tf8x5iv/lNpUG7NDhoeR/lfL3VkdEym63sW7KFoBSNYcRiuDo6qja3W55QyT6mEY8jCwWMmL0+OIgWCBD5wFUM/L//xRgYqOzrWb6cxB13gN3OUNi7l/wuS4CG7rl3QoRgrG0EM6SU5UBYDzDjFfJfCdwyLO3rQoiXhBDfKr/kXqFQ/P3gam+n0NlJbseOw7YZg4MYUavQFW43meeeI3H3PfjWrGHxk09YeaLVgtkcGjqsB5KRTAJQPGiNVyh2WbH4SgFe44kY8Xgl/l7q7kbm8+ihEHo4XDluOQRV9ghGQmYylA4dsu2LYsRi6OFQZbseClWOJaWkFI9jDA1RGqwW+HokUslXd78WLKi/vngcDMuW0uDgqDaNhVcUAiHEX4QQm0b4XFKbT1rN5KM2lQsh2rDeXfxATfIXsNoMTgYivIw3IYS4WgixVgixtl9Na6tQTBucdmik2Fk/sEz4fHW179bPfhawCnLX/Pno4TDoOqVotK7wN4Y1pFbWyz11XqbHTtkLAMjbnoQeakQPhSrHKYd8RkNvsdoi8rt2W8fs68NMp+vaOoTTiRYIYMQTmOmMJUZSUti/v5LHEQ4fLgS6jmve3Prri8crnkyt5zCevKIQSCnPk1KuGOFzJ9BrF/Dlgr7vZQ51BXC7lLIiz1LKbmmRB37Oy7zIXkr5Yynlainl6paWlld7fQqFYpJxzW0HKcm8sB69ublSkHqWH2MLQRTN58Mx0w4omCZ6OITQNKuAjsYoxauFvxGPI2tq68OFYTSE04kRT1TCM4W9+wDQw2Ec4VA1NFQjOrpduDtaW0EIANwLFgKQ320JQVVQQnXn08NhjBrhASjs21fdPoJHoDc04IhEqjZ7vRixGKVBSwiK3d0T8qKfsYaG7gLeby+/H7jzZfK+i2FhoRoREcClwPQcjaFQKEalPJYgu3YdztZWHHZFznPMcmQmQ/HQIfRIpK5GXV52RMJWY6xdeANgmnbDL0jDwEgkcMwYOSrtmj+/urxggVUw28JRsBtsHaGQJTjxOGYuh8xkDttfb2pCb2y0j2OllXtClWvp+rDeT+Vj1gmBLT4AeiSM3hg6bB/h8yE8HgDcCxYgi8VK2EsWCnVexXgxViG4HjhfCLETOM9eRwixWgjx03ImIUQH0A48Nmz/XwshNgIbgWbga2O0R6FQTDFc8+ZVlh0zZlhCoOu4lywGIL9zl1U7rilIy8t6OELxUHelkbZMJYwzNARSVgrnw85dE293L1xQV0Mv1NTk9VC4TiQq+9sNwI5wqGKTe359DL9ic2gEIRjuEdjnBHBEmg73CEIhhBDokXCd/fk9e3DMnEn7T36Mc+bMEc8/FsYkBFLKQSnluVLKxXYIKWqnr5VSfrgm3z4p5WwppTls/3OklMfZoab3SilTY7FHoVBMPRyRCM5Zs6zlGa14Fi/GvWhRxTMo7NtnxcvrhMAKj+iRSCX0Uku5wbdccI9WOLtrhMDV0YGZSmGmrGLGSFjDl/SyRzA0RGmgvjHWNb+jmidcXzgPp7axuLw+3CMon7t8bYcJge11OOzrdy+0zlXYswf3ksUEzjwTze8f8fxjQY0sVigUE47n2OUAOGfOpOWTn6TjN7/G0VQdAKaHw+gNDZU4vF4TGqoN1ZSp6+HD6IWzq2MeCIEWDFaEZzh6Y6N1vmGNueg6LntAnB6qCpVrzuxKFufs6vJItfvhHkEtjkgYze8Dp/OwY+hNkfrrkhJHpGnE44wHSggUCsWEU/YIkBLhcqH5/TiaqwWbHokgHA5LDLBCMVD1DIZT6Y9f9ghGCQ3pkYhV0EfqPY6y4Gh+P8LlqhTA5XYDqPcCrOWyTWG8J52EY8YMmj/2j2iBAA0XXXSY0DjCYcxMhlKf1YdGuFyVc1auWQj0UCPYg8nKdjjCERACd00bhx4Z+V6MB2MdUKZQKBSvSMOb30z05l/gO/nkSpreVCME5UI2EsFIJKoFcFNNDxq3GzQNmc2See45SgMDlUZU59x5lcIdKUHXwTCsAjwSQQ8G62rszjlzKHZ2IuzaeHnbwPe/D4Bj5kw0v79aQ7ftER4PWjBIx69/VTlW6LLLRrzm8r6ZtWvRGhrQg0GKXV34zzwT15zZ+Favts4VCqE3NlLYtbtyHt+akykNDtbdI0fklafieK0oIVAoFBOO9/jjWbZpY2UaBQDN5SL83vcS+9WvcLZacwfp4TDi0CGE1wtQFw5xLViALBQodnVZI2/L6DqOZqtXjxYMUuzsxDVnDoX9+9FDIQJnn4Xm99c1sjZd/RH6rv9GRZjcixehNzfjaG7G2daGcLuRpSKu9nY8xx2H98QTcbXPIXDOOQjt1QVS3EuWgKaRXb+eyFVX4V15Al2f/ie8x62g6UMfquRruOgihNeLc+bMij2hyy4jdNlldV1Ffaec+upu9mtATLfpUgFWr14t165dO9lmKBSKMSKlJPvCBjwrjkVzuej8+LXkNm9m8aPW/D5GKk30xhvRGoJ4jzsOI5m0avGlEp7jj6ewZw+yWMJ/6in0fuO/cc2by9A99xL50AfJrl9Py6c/jdD1yvmymzbjmjcXPRhESokoexHDMPN5a54fuxvna6XY20upfwDvimPt9T4ckXDFE3k1dP7jx/Cdsoamq64aky0AQoh1UsrVh6UrIVAoFFOF9DPPUOzuIfS2SyfblL9LRhMCFRpSKBRTBv+pExf+UIyO6jWkUCgURzlKCBQKheIoRwmBQqFQHOUoIVAoFIqjHCUECoVCcZSjhEChUCiOcpQQKBQKxVGOEgKFQqE4ypmWI4uFEP3Aa31NTzMwMS/+nBimk73K1oljOtk7nWyF6WXvWG2dJ6U8bD7uaSkEY0EIsXakIdZTlelkr7J14phO9k4nW2F62TtRtqrQkEKhUBzlKCFQKBSKo5yjUQh+PNkG/I1MJ3uVrRPHdLJ3OtkK08veCbH1qGsjUCgUCkU9R6NHoFAoFIoalBAoFArFUc5RJQRCiAuFENuFELuEENdNtj3DEULsE0JsFEJsEEKstdMiQog/CyF22t8T9wbrV7bvRiFEnxBiU03aiPYJi+/a9/olIcSqKWDrl4UQXfb93SCEuKhm2xdsW7cLId54hG1tF0I8IoTYIoTYLIT4lJ0+Ve/taPZOufsrhPAIIZ4TQrxo2/ofdvp8IcSztk2/E0K47HS3vb7L3t5xpGx9BXtvEkLsrbm3K+308XkWpJRHxQfQgd3AAsAFvAgsn2y7htm4D2gelvbfwHX28nXANybRvtcDq4BNr2QfcBFwPyCAU4Fnp4CtXwY+O0Le5fbz4Abm28+JfgRtbQNW2ctBYIdt01S9t6PZO+Xur32PAvayE3jWvme/B660038I/KO9/DHgh/bylcDvjvC9Hc3em4DLR8g/Ls/C0eQRrAF2SSn3SCkLwG+BSybZplfDJcDN9vLNwKWTZYiU8q9AdFjyaPZdAvxCWjwDhIQQbUfEUEa1dTQuAX4rpcxLKfcCu7CelyOClLJbSrneXk4CW4HZTN17O5q9ozFp99e+Ryl71Wl/JHAOcKudPvzelu/5rcC5YrQ33E8AL2PvaIzLs3A0CcFsoLNm/SAv//BOBhJ4UAixTghxtZ02Q0rZbS/3ADMmx7RRGc2+qXq/r7Vd6BtrwmxTxlY7FHEiVk1wyt/bYfbCFLy/QghdCLEB6AP+jOWRxKWUpRHsqdhqb08ATUfK1pHslVKW7+3X7Xv7LSGEe7i9Nq/p3h5NQjAdOENKuQp4E/BxIcTrazdKyxecsv19p7p9wA+AhcBKoBv45qRaMwwhRAD4I/BpKeVQ7bapeG9HsHdK3l8ppSGlXAnMwfJElk2uRS/PcHuFECuAL2DZfTIQAT4/nuc8moSgC2ivWZ9jp00ZpJRd9ncfcDvWQ9tbdvXs777Js3BERrNvyt1vKWWv/SczgZ9QDU9Muq1CCCdWofprKeVtdvKUvbcj2TuV769tXxx4BHgdVgjFMYI9FVvt7Y3A4JG11KLG3gvtcJyUUuaBnzPO9/ZoEoLngcV2bwEXVkPQXZNsUwUhhF8IESwvAxcAm7BsfL+d7f3AnZNj4aiMZt9dwD/YvRpOBRI1YY5JYVjs9G1Y9xcsW6+0e4zMBxYDzx1BuwTwM2CrlPKGmk1T8t6OZu9UvL9CiBYhRMhe9gLnY7VpPAJcbmcbfm/L9/xy4GHbGzsijGLvtpoKgcBqz6i9t2N/Fo5ki/hkf7Ba2HdgxQj/dbLtGWbbAqyeFS8Cm8v2YcUnHwJ2An8BIpNo4y1YLn8RKxb5odHsw+rF8D37Xm8EVk8BW39p2/KS/Qdqq8n/r7at24E3HWFbz8AK+7wEbLA/F03hezuavVPu/gLHAy/YNm0CvmSnL8ASo13AHwC3ne6x13fZ2xcc4Xs7mr0P2/d2E/Arqj2LxuVZUFNMKBQKxVHO0RQaUigUCsUIKCFQKBSKoxwlBAqFQnGUo4RAoVAojnKUECgUCsVRjhIChUKhOMpRQqBQKBRHOf8fMQOjyDi1C7MAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -463,7 +467,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.2</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.18.3</td></tr><tr><td><code>qiskit</code></td><td>0.34.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.3.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Fri Feb 11 09:44:53 2022 CST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.20.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit</code></td><td>0.36.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.4.1</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Jun 29 10:44:06 2022 CDT</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/docs/1-tutorials/tutorial_3_H2_arbitrary_hamiltonian.ipynb b/docs/1-tutorials/tutorial_3_H2_arbitrary_hamiltonian.ipynb
index d8e23e8..1bc0d8d 100644
--- a/docs/1-tutorials/tutorial_3_H2_arbitrary_hamiltonian.ipynb
+++ b/docs/1-tutorials/tutorial_3_H2_arbitrary_hamiltonian.ipynb
@@ -138,7 +138,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The ansatz for Forged VQE consists of a set of input bitstrings and a parameterized circuit. In the current implementation, the bitstrings and parameterized circuit are the same for the alpha particle subspace and the beta particle subspace. For a discussion on picking the bitstrings and designing the ansatz, refer to the Explanatory Material. "
+    "The ansatz for Forged VQE consists of a set of input bitstrings and a parameterized circuit for each subsystem, U and V. For this demo we will use the same bitstrings and ansatz for each subsystem. For a discussion on picking the bitstrings and designing the ansatz, refer to the Explanatory Material. "
    ]
   },
   {
@@ -239,9 +239,9 @@
     {
      "data": {
       "text/plain": [
-       "Ground state energy (Hartree): -1.1372960821731657\n",
-       "Schmidt values: [-0.99376317  0.11151129]\n",
-       "Optimizer parameters: [  6.28338313 -12.53378645]"
+       "Ground state energy (Hartree): -1.1170532053875672\n",
+       "Schmidt values: [-0.99998635  0.00522467]\n",
+       "Optimizer parameters: [ 6.28284897 15.22401595]"
       ]
      },
      "execution_count": 6,
@@ -250,7 +250,9 @@
     }
    ],
    "source": [
-    "calc = EntanglementForgedGroundStateSolver(converter, ansatz, bitstrings, config)\n",
+    "calc = EntanglementForgedGroundStateSolver(\n",
+    "    qubit_converter=converter, ansatz=ansatz, bitstrings_u=bitstrings, config=config\n",
+    ")\n",
     "res = calc.solve(problem)\n",
     "\n",
     "res"
@@ -282,7 +284,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -301,7 +303,7 @@
     },
     {
      "data": {
-      "image/png": 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s+2e+C6uvzT7RX35DFgb7noUF58PVHzulv56Z2cko1+GjG2+Dz74xG4pZfe1Ye+fM2sNCkB250pWOZum5JFsOHoDlvdDzyuzx8t6x17xoTba8/Ibs4je7++FN/w06uhr7+5iZNUC5egTX/iG85n3ZZPFlbx9rnywIIBseGjwI51yeHYK5f3s2BDRzIQw8ActfAxe+MRviOe912TZnX5odDdTeCZe+feLXNzObJuUKgrMvzm6VTjQIXvoJzF+ZHQ+/fzucfUl2e+xvYcVrs+PviwEjwboN2dE1tYadzMymWbmCoJbOmbVPJsvlE8YLVmUnNj21EZZcBj0Xw+prah/XW/wKCzOz01BDPqZKWiNpi6R+SbdUeb5b0j3p+e9LWlV47sOpfYukNzeininrnHViPQLIguDS6+DiX4VFq7NP/Q0+ucPMrJnq7hFIagduB64BtgObJG2ouPbwe4G9EfEKSTcBnwDeKelSsovdXwacCzwg6aKIaO6X+pzI0FB+LsGCVTB/xdg8gJnZGa4RPYKrgP6IeDoiBoG7gbUV66wF7kr3vwK8SdlV7NcCd0fE0Yh4BuhPr9dcsxZP/ql+2ZVw9mVwVuPP6jMzm06NmCNYBjxbeLwdeG2tdSJiWNI+YFFqf7Bi22XVfoik9cB6gPPOO68BZRf8yn8Z/10+1Vx+Q3YzM2sxZ8yhLBFxR0T0RkRvT0/P5BtMxayF2ZCPmVkJNSIIdgArCo+Xp7aq60jqAOYBu09wWzMzO4UaEQSbgNWSzpfURTb5u6FinQ3AunT/RuCbERGp/aZ0VNH5wGrgBw2oyczMTlDdcwRpzP9m4D6gHbgzIh6TdBvQFxEbgM8BfympH9hDFhak9f4KeBwYBt7f9COGzMxKTtGoqwY1UW9vb/T19U13GWZmZxRJD0VEb2X7GTNZbGZmp4aDwMys5BwEZmYl5yAwMys5B4GZWck5CMzMSs5BYGZWcg4CM7OScxCYmZWcg8DMrOQcBGZmJecgMDMrOQeBmVnJOQjMzErOQWBmVnIOAjOzkqsrCCQtlHS/pK1puaDKOldI+idJj0l6WNI7C899QdIzkjan2xX11GNmZlNXb4/gFmBjRKwGNqbHlQ4B746Iy4A1wJ9Kml94/vci4op021xnPWZmNkX1BsFa4K50/y7g+soVIuLJiNia7j8H7AJ66vy5ZmbWIPUGwZKI2JnuPw8smWhlSVcBXcBTheY/TENGn5TUPcG26yX1SeobGBios2wzM8tNGgSSHpD0aJXb2uJ6ERFATPA6S4G/BH4rIkZT84eBi4HXAAuBD9XaPiLuiIjeiOjt6XGHwsysUTomWyEirq71nKQXJC2NiJ3pjX5XjfXOAv4euDUiHiy8dt6bOCrp88AHp1S9mZnVrd6hoQ3AunR/HfC1yhUkdQFfBb4YEV+peG5pWopsfuHROusxM7MpqjcIPg5cI2krcHV6jKReSZ9N67wD+CXgPVUOE/2SpEeAR4DFwB/UWU9dXjx4lNHRmqNbZmYtSdnQ/pmlt7c3+vr6Gvqa+48M8Zo/eIA/ecfP8qs/c25DX9vM7HQg6aGI6K1s95nFyf7DQxwdHuWpXS9PdylmZk3lIEiGRrKe0cDBI9NciZlZczkIkuGR7IjWgQNHp7kSM7PmchAkgw4CMyspB0EyfGxoyEFgZuXiIEiGCj2CM/FIKjOzk+UgSPLJ4iNDoxw8OjzN1ZiZNY+DIMl7BAAvHhycxkrMzJrLQZAMj44FgSeMzaxMHATJ4PDYvICDwMzKxEGQjO8R+KQyMysPB0FSnCPwIaRmViYOgmQoDQ1JHhoys3JxECRDaWioZ063g8DMSsVBkAwNZ0GwdP5MDw2ZWak4CJLhdEGac+fNcI/AzEqlriCQtFDS/ZK2puWCGuuNFK5OtqHQfr6k70vql3RPuqzltMi/dO7c+TN58eCgr1RmZqVRb4/gFmBjRKwGNqbH1RyOiCvS7bpC+yeAT0bEK4C9wHvrrOek5V86t3TeDEZGg72HfHaxmZVDvUGwFrgr3b+L7AL0JyRdsP6NQH5B+ylt32hDI6NIcM68GYC/ZsLMyqPeIFgSETvT/eeBJTXWmyGpT9KDkq5PbYuAlyIi/4a37cCyWj9I0vr0Gn0DAwN1ln28oZGgs62Ns+dmQeB5AjMri47JVpD0AHBOladuLT6IiJBUa2B9ZUTskHQB8E1JjwD7plJoRNwB3AHZxeunsu2JGBoZpbNd9MztBnzJSjMrj0mDICKurvWcpBckLY2InZKWArtqvMaOtHxa0reBVwN/A8yX1JF6BcuBHSfxOzTE8MgonR1tY0HgHoGZlUS9Q0MbgHXp/jrga5UrSFogqTvdXwz8AvB4ZFd/+RZw40TbN8vgSNDR1sbsrnZmdrY7CMysNOoNgo8D10jaClydHiOpV9Jn0zqXAH2SfkT2xv/xiHg8Pfch4AOS+snmDD5XZz0nbXhklK52IYnFc7scBGZWGpMODU0kInYDb6rS3ge8L93/HvCqGts/DVxVTw2NMjQySkd7los9c7p9drGZlYbPLE6GRoPOdgHQM9ffN2Rm5eEgSIaGR+nMewQOAjMrEQdBkh0+mg8NzWDvoSEGh0cn2crM7MznIEiGR4OOwtAQwO6X3Ssws9bnIEgGK4aGwOcSmFk5OAiS4YrJYoAXfeSQmZWAgyAZN0fgHoGZlYiDIBkaiWNBsHBWdlmE3S/7G0jNrPU5CJL8S+cAZnS20dEmDh4ZnmQrM7Mzn4MgGS4MDUlizowODh51EJhZ63MQJEPpS+dyc7o73CMws1JwECRDI6N0dejY47kzOjngHoGZlYCDIBkaGR3XI5jb3cGBI0PTWJGZWXM4CJLhwlFDgOcIzKw0HATJYOGoIfAcgZmVh4MgKZ5QBjDXPQIzK4m6gkDSQkn3S9qalguqrPMrkjYXbkckXZ+e+4KkZwrPXVFPPSdrZDQYDY4bGtrvHoGZlUC9PYJbgI0RsRrYmB6PExHfiogrIuIK4I3AIeAbhVV+L38+IjbXWc9JGRrJvm66ozA0NLe7g8HhUY4Oj0xHSWZmTVNvEKwF7kr37wKun2T9G4GvR8ShOn9uQw2PBgBd7ePPIwB4+aiDwMxaW71BsCQidqb7zwNLJln/JuDLFW1/KOlhSZ+U1F1nPSdlaLhKj2BGJ4AnjM2s5U168XpJDwDnVHnq1uKDiAhJMcHrLCW7iP19heYPkwVIF3AH8CHgthrbrwfWA5x33nmTlT0lQ6NZEFTOEQDs97kEZtbiJg2CiLi61nOSXpC0NCJ2pjf6XRO81DuAr0bEsXfWQm/iqKTPAx+coI47yMKC3t7emoFzMoZGspfrrJgjAHzkkJm1vHqHhjYA69L9dcDXJlj3XVQMC6XwQJLI5hcerbOekzI8UrtH4KEhM2t19QbBx4FrJG0Frk6PkdQr6bP5SpJWASuA71Rs/yVJjwCPAIuBP6iznpMydtRQ8TyCNEfgHoGZtbhJh4YmEhG7gTdVae8D3ld4vA1YVmW9N9bz8xslHxrqqjizGPD3DZlZy/OZxRR6BG3jzywG/A2kZtbyHAQUJos7xnZHd4evUmZm5eAgYKxHUDxqSBJzZ3RwwEFgZi3OQUD2FdQw/qgh8FdRm1k5OAgo9ggqgqC70z0CM2t5DgKyaxEAdLRpXPvc7g4OHvVRQ2bW2hwEjA0NdXWM3x2eIzCzMnAQUDx8dHyPwHMEZlYGDgImmiPw5SrNrPU5CCh+6dz43TFvZif7Dg8dCwozs1bkIACGR48/jwDgFWfPYXg02Pbiy9NRlplZU5Q6CHYdOMLlH72P72wZAMZ/6RzAxeecBcATzx9oem1mZs1S6iB4aNteDh4d5h/6XwTGX6oS4MKzZ9PRJp54fv90lGdm1hSlDoJHduwD4Ohw9aGh7o52LuiZzRM73SMws9blIChorzh8FLLhIQ8NmVkrK20QRASP7Nh3rBfQ1d5GdqG08S5eOpcdLx32tYvNrGWVNgi27z3MS4eGuPqSJQB0tB8fAgAXnzMXgC3uFZhZi6orCCT9K0mPSRqV1DvBemskbZHUL+mWQvv5kr6f2u+R1FVPPVORDwu9o3cFcPw5BLn8yKHNP32pKXWZmTVbXZeqJLvY/K8Bn6m1gqR24HbgGmA7sEnShoh4HPgE8MmIuFvSp4H3Ap+qs6aa8hPDDg+NsPHHu+hsF//iFYtYclY3I6NRdZul82ZwxYr5/NnGrVy5cgGPPbeP5Qtm0rtqIQ88/gKL5nTzs8vn8e0tA8zqaueyZfPo27aHQ4MjLJ03g8uXzWNWVzvPvXSE59IQ08hoMDIaDKdlm6BNOjZHEQF5NRGR3Q8I4thzkR6PWz9ft0GigS8WDXyx0/V3hEbX1uDiGqShfxcN3GONrauxGlnbTa9ZwYLZjf3MXO81i38MVB1bL7gK6I+Ip9O6dwNrJf0YeCPw62m9u4CPcQqD4N9+sY9vp3MGAK65dAndHe1csvSsmkcGSeJ//caVvO1//gM3fOp7x9rbBDWyw8zslLnm0iWnVxCcoGXAs4XH24HXAouAlyJiuNB+3AXuc5LWA+sBzjvvvJMq5IYrl9O7cgGSeM2qhfzcygUA/PYbLpzw7OFz58/kL9b1smHzc9z4c8t5auAgj+7Yx5rLz2HgwCCP79zPGy7q4ejQCE88f4DeVQtYPKebn+45xCPb9zE4Msqy+TNZtmAm82Z20tEmOtraaGvLjlQaDRhNPQQBEghRzFcpC6XK54+tUq2tASYJ+am9VsNeCRpYFmpoZTT0F23s79nA1/LfxdRfr0EvV3m+UyNosu6npAeAc6o8dWtEfC2t823ggxHRV2X7G4E1EfG+9Pg3yYLgY8CDEfGK1L4C+HpEXD5Z0b29vdHXd9yPMjOzCUh6KCKOm8+dtEcQEVfX+bN3ACsKj5entt3AfEkdqVeQt5uZWRM14/DRTcDqdIRQF3ATsCGyrsi3gBvTeuuArzWhHjMzK6j38NG3S9oO/Dzw95LuS+3nSroXIH3avxm4D/gx8FcR8Vh6iQ8BH5DUTzZn8Ll66jEzs6mbdI7gdOQ5AjOzqas1R1DaM4vNzCzjIDAzKzkHgZlZyTkIzMxK7oycLJY0APzkJDdfDLzYwHIaxXVNjeuaGtc1Na1a18qI6KlsPCODoB6S+qrNmk831zU1rmtqXNfUlK0uDw2ZmZWcg8DMrOTKGAR3THcBNbiuqXFdU+O6pqZUdZVujsDMzMYrY4/AzMwKHARmZiVXqiCQtEbSFkn9km6ZphpWSPqWpMclPSbpd1L7xyTtkLQ53d46TfVtk/RIqqEvtS2UdL+krWm5oMk1vbKwXzZL2i/pd6djn0m6U9IuSY8W2qruH2X+PP29PSzpyibX9ceSnkg/+6uS5qf2VZIOF/bbp5tcV81/N0kfTvtri6Q3N7muewo1bZO0ObU3c3/Ven84tX9jEVGKG9AOPAVcAHQBPwIunYY6lgJXpvtzgSeBS8mu2PbB02A/bQMWV7T9EXBLun8L8Ilp/nd8Hlg5HfsM+CXgSuDRyfYP8Fbg62RXY3wd8P0m13Ut0JHuf6JQ16rietOwv6r+u6X/Bz8CuoHz0//X9mbVVfH8nwAfmYb9Vev94ZT+jZWpR3AV0B8RT0fEIHA3sLbZRUTEzoj4Ybp/gOwaDTWv1XyaWAvcle7fBVw/faXwJuCpiDjZM8vrEhHfBfZUNNfaP2uBL0bmQbIr8i1tVl0R8Y0Yuyb4g2RXAWyqGvurlrXA3RFxNCKeAfrJ/t82tS5JAt4BfPlU/OyJTPD+cEr/xsoUBMuAZwuPtzPNb8CSVgGvBr6fmm5O3bs7mz38UhDANyQ9JGl9alsSETvT/eeBJdNTGpBd4a74H/R02Ge19s/p9Df3b8g+OebOl/TPkr4j6RenoZ5q/26ny/76ReCFiNhaaGv6/qp4fzilf2NlCoLTiqQ5wN8AvxsR+4FPARcCVwA7ybqm0+H1EXEl8Bbg/ZJ+qfhkZP3RaTnmWNmlTq8D/jo1nS777Jjp3D+1SLoVGAa+lJp2AudFxKuBDwD/W9JZTSzptPt3q/Auxn/YaPr+qvL+cMyp+BsrUxDsAFYUHi9PbU0nqZPsH/lLEfG3ABHxQkSMRMQo8Becoi7xZCJiR1ruAr6a6ngh726m5a7pqI0snH4YES+kGk+LfUbt/TPtf3OS3gP8KvAb6Q2ENPSyO91/iGws/qJm1TTBv9vpsL86gF8D7snbmr2/qr0/cIr/xsoUBJuA1ZLOT58sbwI2NLuINP74OeDHEfE/Cu3Fcb23A49WbtuE2mZLmpvfJ5tsfJRsP61Lq60Dvtbs2pJxn9ROh32W1No/G4B3pyM7XgfsK3TvTzlJa4D/DFwXEYcK7T2S2tP9C4DVwNNNrKvWv9sG4CZJ3ZLOT3X9oFl1JVcDT0TE9ryhmfur1vsDp/pvrBkz4afLjWyG/UmyRL91mmp4PVm37mFgc7q9FfhL4JHUvgFYOg21XUB21MaPgMfyfQQsAjYCW4EHgIXTUNtsYDcwr9DW9H1GFkQ7gSGy8dj31to/ZEdy3J7+3h4BeptcVz/Z+HH+d/bptO4N6d93M/BD4G1Nrqvmvxtwa9pfW4C3NLOu1P4F4Lcr1m3m/qr1/nBK/8b8FRNmZiVXpqEhMzOrwkFgZlZyDgIzs5JzEJiZlZyDwMys5BwEZmYl5yAwMyu5/w9MJjuLPzkFFgAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -320,7 +322,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -353,7 +355,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.2</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.18.3</td></tr><tr><td><code>qiskit</code></td><td>0.34.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.3.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Fri Feb 11 09:46:41 2022 CST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.20.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit</code></td><td>0.36.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.4.1</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Jun 29 10:48:15 2022 CDT</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/docs/1-tutorials/tutorial_4_H2_qasm_simulator.ipynb b/docs/1-tutorials/tutorial_4_H2_qasm_simulator.ipynb
index 6ff3601..d9b92a9 100644
--- a/docs/1-tutorials/tutorial_4_H2_qasm_simulator.ipynb
+++ b/docs/1-tutorials/tutorial_4_H2_qasm_simulator.ipynb
@@ -135,9 +135,9 @@
     {
      "data": {
       "text/plain": [
-       "Ground state energy (Hartree): -1.1371913169346164\n",
-       "Schmidt values: [-0.99378125  0.11134999]\n",
-       "Optimizer parameters: [ 12.56837914 -18.95337037]"
+       "Ground state energy (Hartree): -1.136948142747784\n",
+       "Schmidt values: [-0.99386396  0.11060935]\n",
+       "Optimizer parameters: [-12.56570247 -12.39676273]"
       ]
      },
      "execution_count": 4,
@@ -151,7 +151,9 @@
     "backend = Aer.get_backend(\"qasm_simulator\")\n",
     "config = EntanglementForgedConfig(backend=backend, copysample_job_size=100)\n",
     "\n",
-    "calc = EntanglementForgedGroundStateSolver(converter, ansatz, bitstrings, config)\n",
+    "calc = EntanglementForgedGroundStateSolver(\n",
+    "    qubit_converter=converter, ansatz=ansatz, bitstrings_u=bitstrings, config=config\n",
+    ")\n",
     "res = calc.solve(problem)\n",
     "\n",
     "res"
@@ -176,7 +178,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -195,7 +197,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -214,7 +216,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -250,9 +252,9 @@
     {
      "data": {
       "text/plain": [
-       "Ground state energy (Hartree): -1.1371913169346164\n",
-       "Schmidt values: [-0.99378125  0.11134999]\n",
-       "Optimizer parameters: [ 12.56837914 -18.95337037]"
+       "Ground state energy (Hartree): -1.136948142747784\n",
+       "Schmidt values: [-0.99386396  0.11060935]\n",
+       "Optimizer parameters: [-12.56570247 -12.39676273]"
       ]
      },
      "execution_count": 6,
@@ -272,7 +274,7 @@
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.2</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.18.3</td></tr><tr><td><code>qiskit</code></td><td>0.34.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.3.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Fri Feb 11 09:56:09 2022 CST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.20.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit</code></td><td>0.36.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.4.1</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Jun 29 10:50:38 2022 CDT</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -304,7 +306,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -318,7 +320,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/1-tutorials/tutorial_5_H2_equilibrium_distance.ipynb b/docs/1-tutorials/tutorial_5_H2_equilibrium_distance.ipynb
index 4e6138b..552c576 100644
--- a/docs/1-tutorials/tutorial_5_H2_equilibrium_distance.ipynb
+++ b/docs/1-tutorials/tutorial_5_H2_equilibrium_distance.ipynb
@@ -75,25 +75,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Distance = 0.3, Energy = -0.6016841185694219\n",
-      "Distance = 0.4, Energy = -0.9138650790654701\n",
-      "Distance = 0.5, Energy = -1.0449617026054983\n",
-      "Distance = 0.6000000000000001, Energy = -1.1162402492976309\n",
-      "Distance = 0.7000000000000002, Energy = -1.1359262629168354\n",
-      "Distance = 0.8000000000000003, Energy = -1.1334807983370823\n",
-      "Distance = 0.9000000000000001, Energy = -1.1205268961158692\n",
-      "Distance = 1.0000000000000002, Energy = -1.1011481693720442\n",
-      "Distance = 1.1000000000000003, Energy = -1.0791896757504489\n",
-      "Distance = 1.2000000000000004, Energy = -1.0567280461567747\n",
-      "Distance = 1.3000000000000005, Energy = -1.0351862662602327\n",
-      "Distance = 1.4000000000000004, Energy = -1.0154202988571464\n"
+      "Distance = 0.3, Energy = -0.601171064489257\n",
+      "Distance = 0.4, Energy = -0.9126835228459282\n",
+      "Distance = 0.5, Energy = -1.0528502178276746\n",
+      "Distance = 0.6000000000000001, Energy = -1.1161342995912382\n",
+      "Distance = 0.7000000000000002, Energy = -1.1360591281154382\n",
+      "Distance = 0.8000000000000003, Energy = -1.133879337102356\n",
+      "Distance = 0.9000000000000001, Energy = -1.1202416452753126\n",
+      "Distance = 1.0000000000000002, Energy = -1.1010261779313404\n",
+      "Distance = 1.1000000000000003, Energy = -1.0791929444577208\n",
+      "Distance = 1.2000000000000004, Energy = -1.056738113700157\n",
+      "Distance = 1.3000000000000005, Energy = -1.0351856287724186\n",
+      "Distance = 1.4000000000000004, Energy = -0.941484658110538\n"
      ]
     }
    ],
@@ -105,7 +105,9 @@
     "converter = QubitConverter(JordanWignerMapper())\n",
     "\n",
     "config = EntanglementForgedConfig(backend=backend, maxiter=100)\n",
-    "calc = EntanglementForgedGroundStateSolver(converter, ansatz, bitstrings, config)\n",
+    "calc = EntanglementForgedGroundStateSolver(\n",
+    "    qubit_converter=converter, ansatz=ansatz, bitstrings_u=bitstrings, config=config\n",
+    ")\n",
     "\n",
     "energies = []\n",
     "\n",
@@ -131,12 +133,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -156,13 +158,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.19.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.2</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.18.3</td></tr><tr><td><code>qiskit</code></td><td>0.34.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.3.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Thu Feb 10 13:07:08 2022 CST</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.20.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.10.4</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit-ibmq-provider</code></td><td>0.19.1</td></tr><tr><td><code>qiskit</code></td><td>0.36.1</td></tr><tr><td><code>qiskit-nature</code></td><td>0.4.1</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.4.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>Clang 10.0.0 </td></tr><tr><td>Python build</td><td>default, Sep 16 2021 08:50:36</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Wed Jun 29 10:54:32 2022 CDT</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
diff --git a/docs/3-explanatory_material/explanatory_material.md b/docs/3-explanatory_material/explanatory_material.md
index ab7e0a0..6bef66b 100644
--- a/docs/3-explanatory_material/explanatory_material.md
+++ b/docs/3-explanatory_material/explanatory_material.md
@@ -129,9 +129,7 @@ Notes:
 ### Ansatz & bitstrings
 - It is currently an open problem how to pick the best circuit (ansatze) for VQE (and thus Entanglement Forging) for a given system.
 - It is also currently an open problem how to pick the best bitstring for Entanglement Forging.
-- In the current implementation of the module, the spin-up and spin-down particles are treated symmetrically (U=V for the ansatz with equivalent bitstrings).
-  - It would be useful to be able to specify different bit strings for spin-up/spin down orbitals as this would open the door to open-shell species.
-  - There are plans in the future to break the ansatz and bitstring symmetry. This will be made possible after a new expectation values class is merged in Terra.
+- In the current implementation of the module, the same ansatz circuit is used for both spin-up and spin-down systems, U and V.
 - In the current implementation of the module, the ansatz must be real.
   - For molecular calculations, one can usually force the ansatz to be real. On the other hand, in crystalline solids (away from the gamma point and without inversion symmetry), the Hamiltonian is defined by the complex numbers.
   - There are plans in the future to implement complex ansatze.
diff --git a/entanglement_forging/core/forged_operator.py b/entanglement_forging/core/forged_operator.py
index a847e8c..17f99e1 100644
--- a/entanglement_forging/core/forged_operator.py
+++ b/entanglement_forging/core/forged_operator.py
@@ -46,7 +46,10 @@ class ForgedOperator:
     """
 
     def __init__(
-        self, problem: ElectronicStructureProblem, all_orbitals_to_reduce: List[int]
+        self,
+        problem: ElectronicStructureProblem,
+        all_orbitals_to_reduce: List[int],
+        calculate_tensor_cross_terms: bool = False,
     ):
         self.problem = problem
         self.all_orbitals_to_reduce = all_orbitals_to_reduce
@@ -54,6 +57,7 @@ def __init__(
             self.all_orbitals_to_reduce, self.problem
         )
         self.epsilon_cholesky = 1e-10
+        self._calculate_tensor_cross_terms = calculate_tensor_cross_terms
 
         if isinstance(problem.driver, ElectronicStructureDriver):
             electronic_basis_transform = self.problem.grouped_property.get_property(
@@ -95,10 +99,6 @@ def __init__(
         )
         self.h_1_op, self.h_chol_ops, _, _, _ = fermionic_results
 
-        assert (
-            num_alpha == num_beta
-        ), "Currently only supports molecules with equal number of alpha and beta particles."
-
     def construct(self):
         """Constructs the forged operator by extracting the Pauli operators and weights.
 
@@ -141,8 +141,15 @@ def construct(self):
         for op_idx, paulis_this_op in enumerate(paulis_each_op):
             pnames = list(paulis_this_op.keys())
             tensor_paulis.update(pnames)
-            if op_idx > 0:
+
+            # Only pull bases from the single-body Hamiltonian to the
+            # superposition basis if the calculate_tensor_cross_terms flag
+            # is set
+            if (not self._calculate_tensor_cross_terms) and (op_idx == 0):
+                pass
+            else:
                 superpos_paulis.update(pnames)
+
         # ensure Identity string is represented since we will need it
         identity_string = "I" * len(pnames[0])
         tensor_paulis.add(identity_string)
@@ -164,6 +171,12 @@ def construct(self):
             i = pauli_ordering_for_tensor_states[pname_i]
             w_ij[i, identity_idx] += np.real(w_i)  # H_spin-up
             w_ij[identity_idx, i] += np.real(w_i)  # H_spin-down
+
+            # In the special case where bn=bm, we need terms from the
+            # single body system represented in the cross terms.
+            if self._calculate_tensor_cross_terms:
+                w_ab[i, identity_idx] += np.real(w_i)
+                w_ab[identity_idx, i] += np.real(w_i)
         # Processes the Cholesky operators (indexed by gamma)
         for paulis_this_gamma in paulis_each_op[1:]:
             for pname_1, w_1 in paulis_this_gamma.items():
diff --git a/entanglement_forging/core/wrappers/entanglement_forged_ground_state_eigensolver.py b/entanglement_forging/core/wrappers/entanglement_forged_ground_state_eigensolver.py
index 282f43d..5da3156 100644
--- a/entanglement_forging/core/wrappers/entanglement_forged_ground_state_eigensolver.py
+++ b/entanglement_forging/core/wrappers/entanglement_forged_ground_state_eigensolver.py
@@ -14,7 +14,7 @@
 
 import time
 import warnings
-from typing import List, Union, Dict, Optional, Tuple
+from typing import List, Iterable, Union, Dict, Optional, Tuple
 
 import numpy as np
 from qiskit import QuantumCircuit
@@ -28,6 +28,7 @@
     GroundStateSolver,
     MinimumEigensolverFactory,
 )
+from qiskit_nature.converters.second_quantization import QubitConverter
 from qiskit_nature.mappers.second_quantization import JordanWignerMapper
 from qiskit_nature.operators.second_quantization import SecondQuantizedOp
 from qiskit_nature.problems.second_quantization import BaseProblem
@@ -37,6 +38,9 @@
 
 from entanglement_forging.core.classical_energies import ClassicalEnergies
 from entanglement_forging.core.forged_operator import ForgedOperator
+from entanglement_forging.core.entanglement_forged_config import (
+    EntanglementForgedConfig,
+)
 from entanglement_forging.core.wrappers.entanglement_forged_vqe import (
     EntanglementForgedVQE,
 )
@@ -58,17 +62,49 @@ class EntanglementForgedGroundStateSolver(GroundStateSolver):
     """
 
     def __init__(
-        self, qubit_converter, ansatz, bitstrings, config, orbitals_to_reduce=None
+        self,
+        qubit_converter: QubitConverter,
+        ansatz: QuantumCircuit,
+        bitstrings_u: Iterable[Iterable[int]],
+        config: EntanglementForgedConfig,
+        bitstrings_v: Iterable[Iterable[int]] = None,
+        orbitals_to_reduce: bool = None,
     ):
+        # Ensure the bitstrings are well formed
+        if any(len(bitstrings_u[0]) != len(bitstr) for bitstr in bitstrings_u):
+            raise ValueError("All U bitstrings must be the same length.")
+
+        if bitstrings_v:
+            if len(bitstrings_u) != len(bitstrings_v):
+                raise ValueError(
+                    "The same number of bitstrings should be passed for U and V."
+                )
+
+            if len(bitstrings_u[0]) != len(bitstrings_v[0]):
+                raise ValueError("Bitstrings for U and V should be the same length.")
+
+            if any(len(bitstrings_v[0]) != len(bitstr) for bitstr in bitstrings_v):
+                raise ValueError("All V bitstrings must be the same length.")
+
+        # Initialize the GroundStateSolver
+        super().__init__(qubit_converter)
+
+        # Set which orbitals to ignore when calculating the Hamiltonian
         if orbitals_to_reduce is None:
             orbitals_to_reduce = []
         self.orbitals_to_reduce = orbitals_to_reduce
-        super().__init__(qubit_converter)
 
+        # Set private class fields
         self._ansatz = ansatz
-        self._bitstrings = bitstrings
+        self._bitstrings_u = bitstrings_u
         self._config = config  # pylint: disable=arguments-differ
 
+        # Prevent unnecessary duplication of circuits if subsystems are identical
+        if (bitstrings_v is None) or (bitstrings_u == bitstrings_v):
+            self._bitstrings_v = []
+        else:
+            self._bitstrings_v = bitstrings_v
+
     # pylint: disable=arguments-differ
     def solve(
         self,
@@ -100,12 +136,19 @@ def solve(
         start_time = time.time()
 
         problem.driver.run()
-        forged_operator = ForgedOperator(problem, self.orbitals_to_reduce)
+
+        # Decompose the Hamiltonian operators into a form appropraite for EF
+        forged_operator = ForgedOperator(
+            problem, self.orbitals_to_reduce, self._calculate_tensor_cross_terms()
+        )
+
+        # Calculate energies clasically using pySCF
         classical_energies = ClassicalEnergies(problem, self.orbitals_to_reduce)
 
         self._solver = EntanglementForgedVQE(
             ansatz=self._ansatz,
-            bitstrings=self._bitstrings,
+            bitstrings_u=self._bitstrings_u,
+            bitstrings_v=self._bitstrings_v,
             config=self._config,
             forged_operator=forged_operator,
             classical_energies=classical_energies,
@@ -125,6 +168,31 @@ def solve(
         res.combine(result)
         return res
 
+    def _calculate_tensor_cross_terms(self) -> bool:
+        """
+        Determine whether circuits should be generated to account for
+        the special superposition terms needed when bn==bm for two
+        bitstrings within a given subsystem's (U or V) bitstring list.
+        """
+        bsu = self._bitstrings_u
+        bsv = self._bitstrings_u
+
+        # Search for any duplicate bitstrings within the subsystem lists
+        for i, bu1 in enumerate(bsu):
+            for j, bu2 in enumerate(bsu):
+                if i == j:
+                    continue
+                if bu1 == bu2:
+                    return True
+        for i, bv1 in enumerate(bsv):
+            for j, bv2 in enumerate(bsv):
+                if i == j:
+                    continue
+                if bv1 == bv2:
+                    return True
+
+        return False
+
     def returns_groundstate(self) -> bool:
         """Whether this class returns only the ground state energy or also the ground state itself.
 
@@ -148,7 +216,7 @@ def evaluate_operators(
             OperatorBase,
         ],
         operators: Union[PauliSumOp, OperatorBase, list, dict],
-    ) -> Union[float, List[float], Dict[str, List[float]]]:
+    ) -> Union[float, Iterable[float], Dict[str, Iterable[float]]]:
         """Evaluates additional operators at the given state."""
         warnings.warn(
             "evaluate_operators not implemented for "
diff --git a/entanglement_forging/core/wrappers/entanglement_forged_vqe.py b/entanglement_forging/core/wrappers/entanglement_forged_vqe.py
index eadfd0b..2c18b62 100644
--- a/entanglement_forging/core/wrappers/entanglement_forged_vqe.py
+++ b/entanglement_forging/core/wrappers/entanglement_forged_vqe.py
@@ -15,16 +15,21 @@
 import datetime
 import time
 import warnings
-from typing import List, Tuple, Callable, Union
+from typing import Iterable, Tuple, Callable, Union
 
 import numpy as np
-from qiskit import transpile
+from qiskit import transpile, QuantumCircuit
 from qiskit.algorithms import VQE
 from qiskit.utils.mitigation import complete_meas_cal
 from qiskit.quantum_info import Pauli
 from qiskit.utils import QuantumInstance
 from qiskit.opflow import OperatorBase
 
+from entanglement_forging.core.entanglement_forged_config import (
+    EntanglementForgedConfig,
+)
+from entanglement_forging.core.forged_operator import ForgedOperator
+from entanglement_forging.core.classical_energies import ClassicalEnergies
 from entanglement_forging.core.wrappers.entanglement_forged_vqe_result import (
     DataResults,
     Bootstrap,
@@ -60,8 +65,37 @@
 class EntanglementForgedVQE(VQE):
     """A class for Entanglement Forged VQE <https://arxiv.org/abs/2104.10220>."""
 
-    def __init__(self, ansatz, bitstrings, config, forged_operator, classical_energies):
+    def __init__(
+        self,
+        ansatz: QuantumCircuit,
+        bitstrings_u: Iterable[Iterable[int]],
+        config: EntanglementForgedConfig,
+        forged_operator: ForgedOperator,
+        classical_energies: ClassicalEnergies,
+        bitstrings_v: Iterable[Iterable[int]] = None,
+    ):
         """Initialize the EntanglementForgedVQE class."""
+        if ansatz.num_qubits != len(bitstrings_u[0]):
+            raise ValueError(
+                "The number of qubits in ansatz does "
+                "not match the number of bits in reduced_bitstrings."
+            )
+
+        if any(len(bitstrings_u[0]) != len(bitstr) for bitstr in bitstrings_u):
+            raise ValueError("All U bitstrings must be the same length.")
+
+        if bitstrings_v:
+            if len(bitstrings_u) != len(bitstrings_v):
+                raise ValueError(
+                    "The same number of bitstrings should be passed for U and V."
+                )
+
+            if len(bitstrings_u[0]) != len(bitstrings_v[0]):
+                raise ValueError("Bitstrings for U and V should be the same length.")
+
+            if any(len(bitstrings_v[0]) != len(bitstr) for bitstr in bitstrings_v):
+                raise ValueError("All V bitstrings must be the same length.")
+
         super().__init__(
             ansatz=ansatz,
             optimizer=get_optimizer_instance(config),
@@ -69,21 +103,53 @@ def __init__(self, ansatz, bitstrings, config, forged_operator, classical_energi
             max_evals_grouped=config.max_evals_grouped,
             callback=None,
         )
+
+        # Prevent unnecessary duplication if subsystems are equivalent
+        if (bitstrings_v is None) or (bitstrings_u == bitstrings_v):
+            self.bitstrings_v = []
+        else:
+            self.bitstrings_v = bitstrings_v
+
         self.ansatz = ansatz
         self.config = config
         self._energy_each_iteration_each_paramset = []
         self._paramsets_each_iteration = []
         self._schmidt_coeffs_each_iteration_each_paramset = []
         self._zero_noise_extrap = config.zero_noise_extrap
-        self.bitstrings = bitstrings
-        self._bitstrings_s = np.asarray(bitstrings)
+
+        self.bitstrings_u = bitstrings_u
+        self._bitstrings_s_u = np.asarray(bitstrings_u)
+
+        # Make circuits which prepare states U|b_n_u> and U|phi^p_nm_u>
         (
-            self._tensor_prep_circuits,
-            self._superpos_prep_circuits,
-        ) = make_stateprep_circuits(bitstrings, config.fix_first_bitstring)
+            self._tensor_prep_circuits_u,
+            self._superpos_prep_circuits_u,
+            hybrid_superpos_coeffs_u,
+        ) = make_stateprep_circuits(
+            bitstrings_u, config.fix_first_bitstring, suffix="u"
+        )
+
+        self._tensor_prep_circuits_v = []
+        self._superpos_prep_circuits_v = []
+        hybrid_superpos_coeffs_v = []
+        self._bitstrings_s_v = np.array([])
+        if self.bitstrings_v:
+            # Make circuits which prepare states V|b_n_v> and V|phi^p_nm_v>
+            # Where U = V but bitstring |b_n_v> does not necessarily equal |b_n_u>
+            self._bitstrings_s_v = np.asarray(bitstrings_v)
+            (
+                self._tensor_prep_circuits_v,
+                self._superpos_prep_circuits_v,
+                hybrid_superpos_coeffs_v,
+            ) = make_stateprep_circuits(
+                bitstrings_v, config.fix_first_bitstring, suffix="v"
+            )
+
+        hybrid_superpos_coeffs_u.update(hybrid_superpos_coeffs_v)
+        self._hybrid_superpos_coeffs = hybrid_superpos_coeffs_u
         self._iteration_start_time = np.nan
         self._running_estimate_of_schmidts = np.array(
-            [1.0] + [0.1] * (len(self._bitstrings_s) - 1)
+            [1.0] + [0.1] * (len(self._bitstrings_s_u) - 1)
         )
         self._running_estimate_of_schmidts /= np.linalg.norm(
             self._running_estimate_of_schmidts
@@ -109,7 +175,7 @@ def __init__(self, ansatz, bitstrings, config, forged_operator, classical_energi
         self._add_this_to_energies_displayed = classical_energies.shift
         self._hf_energy = classical_energies.HF
 
-        self.aux_results: List[Tuple[str, AuxiliaryResults]] = []
+        self.aux_results: Iterable[Tuple[str, AuxiliaryResults]] = []
 
         self.parameter_sets = []
         self.energy_mean_each_parameter_set = []
@@ -125,11 +191,14 @@ def __init__(self, ansatz, bitstrings, config, forged_operator, classical_energi
         else:
             self._is_sv_sim = False
 
-        if self.ansatz.num_qubits != len(self.bitstrings[0]):
-            raise ValueError(
-                "The number of qubits in ansatz does "
-                "not match the number of bits in reduced_bitstrings."
-            )
+        # Load the two circuit generation operations
+        self._pauli_names_for_tensor_states = None
+        self._pauli_names_for_superpos_states = None
+        self._w_ij_tensor_states = None
+        self._w_ab_superpos_states = None
+        self._op_for_generating_tensor_circuits = None
+        self._op_for_generating_superpos_circuits = None
+        self._load_ops()
 
     @property
     def shots_multiplier(self):
@@ -151,9 +220,50 @@ def bootstrap_trials(self, trials):
         """Set the bootstrap trials."""
         self._bootstrap_trials = trials
 
+    def _load_ops(self):
+        # Get the weighted Pauli deconstruction of the operator
+        (
+            pauli_names_for_tensor_states,
+            pauli_names_for_superpos_states,
+            w_ij_tensor_states,
+            w_ab_superpos_states,
+        ) = self.forged_operator.construct()
+
+        self._pauli_names_for_tensor_states = pauli_names_for_tensor_states
+        self._pauli_names_for_superpos_states = pauli_names_for_superpos_states
+        self._w_ij_tensor_states = w_ij_tensor_states
+        self._w_ab_superpos_states = w_ab_superpos_states
+
+        op_for_generating_tensor_circuits = to_tpb_grouped_weighted_pauli_operator(
+            WeightedPauliOperator(
+                paulis=[
+                    [1, Pauli(pname)] for pname in self._pauli_names_for_tensor_states
+                ]
+            ),
+            TPBGroupedWeightedPauliOperator.sorted_grouping,
+        )
+        if len(self._pauli_names_for_superpos_states) > 0:
+            op_for_generating_superpos_circuits = (
+                to_tpb_grouped_weighted_pauli_operator(
+                    WeightedPauliOperator(
+                        paulis=[
+                            [1, Pauli(pname)]
+                            for pname in self._pauli_names_for_superpos_states
+                        ]
+                    ),
+                    TPBGroupedWeightedPauliOperator.sorted_grouping,
+                )
+            )
+
+        else:
+            op_for_generating_superpos_circuits = None
+
+        self._op_for_generating_tensor_circuits = op_for_generating_tensor_circuits
+        self._op_for_generating_superpos_circuits = op_for_generating_superpos_circuits
+
     def get_energy_evaluation(
         self, operator: OperatorBase, return_expectation: bool = False
-    ) -> Callable[[np.ndarray], Union[float, List[float]]]:
+    ) -> Callable[[np.ndarray], Union[float, Iterable[float]]]:
 
         if self._is_sv_sim:
             self._shots_multiplier = 1
@@ -179,6 +289,8 @@ def energy_evaluation(parameters):
 
             Log.log("Parameter sets:", parameter_sets)
 
+            # Compute the expectation value of the forged operator with respect
+            # to the ansatz at the given parameters
             eval_forged_result = self._evaluate_forged_operator(
                 parameter_sets=parameter_sets,
                 hf_value=self._hf_energy,
@@ -316,68 +428,80 @@ def _evaluate_forged_operator(
         """Computes the expectation value of the forged operator
         with respect to the ansatz at the given parameters."""
         # These calculations are parameter independent for
-        #   a given operator so could be moved outside the optimization loop:
-        (
-            pauli_names_for_tensor_states,
-            pauli_names_for_superpos_states,
-            w_ij_tensor_states,
-            w_ij_superpos_states,
-        ) = self.forged_operator.construct()
-        # We have a bunch of 6-qb Paulis we want evaluated.
-        # We bundle these in a single operator that will
-        #   use qiskit's standard routines to define the minimum
-        #   number of circuits required to evaluate these Paulis:
-        op_for_generating_tensor_circuits = to_tpb_grouped_weighted_pauli_operator(
-            WeightedPauliOperator(
-                paulis=[[1, Pauli(pname)] for pname in pauli_names_for_tensor_states]
-            ),
-            TPBGroupedWeightedPauliOperator.sorted_grouping,
-        )
-        if pauli_names_for_superpos_states:
-            op_for_generating_superpos_circuits = (
-                to_tpb_grouped_weighted_pauli_operator(
-                    WeightedPauliOperator(
-                        paulis=[
-                            [1, Pauli(pname)]
-                            for pname in pauli_names_for_superpos_states
-                        ]
-                    ),
-                    TPBGroupedWeightedPauliOperator.sorted_grouping,
-                )
-            )
-
-        else:
-            op_for_generating_superpos_circuits = None
+        # a given operator so could be moved outside the optimization loop:
         circuits_to_execute = []
+
         for params_idx, params in enumerate(parameter_sets):
             Log.log("Constructing the circuits for parameter set", params, "...")
-            tensor_circuits_to_execute = prepare_circuits_to_execute(
+
+            # Get all the tensor circuits associated with ansatz U
+            tensor_circuits_to_execute_u = prepare_circuits_to_execute(
                 params,
-                self._tensor_prep_circuits,
-                op_for_generating_tensor_circuits,
+                self._tensor_prep_circuits_u,
+                self._op_for_generating_tensor_circuits,
                 self._ansatz,
                 self._is_sv_sim,
             )
-            if pauli_names_for_superpos_states:
-                superpos_circuits_to_execute = prepare_circuits_to_execute(
+
+            # Get all tensor circuits associated with ansatz V
+            tensor_circuits_to_execute_v = prepare_circuits_to_execute(
+                params,
+                self._tensor_prep_circuits_v,
+                self._op_for_generating_tensor_circuits,
+                self._ansatz,
+                self._is_sv_sim,
+            )
+
+            # Combine all tensor circuits into a single list
+            tensor_circuits_to_execute = (
+                tensor_circuits_to_execute_u + tensor_circuits_to_execute_v
+            )
+
+            if len(self._pauli_names_for_superpos_states) > 0:
+                # Get superposition circuits associated with ansatz U
+                superpos_circuits_to_execute_u = prepare_circuits_to_execute(
+                    params,
+                    self._superpos_prep_circuits_u,
+                    self._op_for_generating_superpos_circuits,
+                    self._ansatz,
+                    self._is_sv_sim,
+                )
+                # Get superposition circuits associated with ansatz V
+                superpos_circuits_to_execute_v = prepare_circuits_to_execute(
                     params,
-                    self._superpos_prep_circuits,
-                    op_for_generating_superpos_circuits,
+                    self._superpos_prep_circuits_v,
+                    self._op_for_generating_superpos_circuits,
                     self._ansatz,
                     self._is_sv_sim,
                 )
+
+                # Combine all superposition circuits into a single list
+                superpos_circuits_to_execute = (
+                    superpos_circuits_to_execute_u + superpos_circuits_to_execute_v
+                )
+
             else:
+                superpos_circuits_to_execute_u = []
+                superpos_circuits_to_execute_v = []
                 superpos_circuits_to_execute = []
+
             if params_idx == 0:
                 Log.log(
                     "inferred number of pauli groups for tensor statepreps:",
-                    len(tensor_circuits_to_execute) / len(self._tensor_prep_circuits),
+                    len(tensor_circuits_to_execute)
+                    / (
+                        len(self._tensor_prep_circuits_u)
+                        + len(self._tensor_prep_circuits_v)
+                    ),
                 )
-                if self._superpos_prep_circuits:
+                if self._superpos_prep_circuits_u:
                     Log.log(
                         "inferred number of pauli groups for superposition statepreps:",
                         len(superpos_circuits_to_execute)
-                        / len(self._superpos_prep_circuits),
+                        / (
+                            len(self._superpos_prep_circuits_u)
+                            + len(self._superpos_prep_circuits_v)
+                        ),
                     )
             circuits_to_execute += (
                 tensor_circuits_to_execute + superpos_circuits_to_execute
@@ -510,15 +634,17 @@ def _evaluate_forged_operator(
             for is_bootstrap_index, res in enumerate([result] + bootstrap_results):
                 results_extrap, results_raw = eval_forged_op_with_result(
                     res,
-                    w_ij_tensor_states,
-                    w_ij_superpos_states,
+                    self._w_ij_tensor_states,
+                    self._w_ab_superpos_states,
                     params,
-                    self._bitstrings_s,
-                    op_for_generating_tensor_circuits,
-                    op_for_generating_superpos_circuits,
+                    self._bitstrings_s_u,
+                    self._op_for_generating_tensor_circuits,
+                    self._op_for_generating_superpos_circuits,
                     self._zero_noise_extrap,
+                    bitstrings_s_v=self._bitstrings_s_v,
                     hf_value=hf_value,
                     statevector_mode=self._is_sv_sim,
+                    hybrid_superpos_coeffs=self._hybrid_superpos_coeffs,
                     add_this_to_mean_values_displayed=add_this_to_mean_values_displayed,
                     no_bs0_circuits=self._no_bs0_circuits,
                 )
diff --git a/entanglement_forging/utils/forging_subroutines.py b/entanglement_forging/utils/forging_subroutines.py
index 1eb8232..f19b5eb 100644
--- a/entanglement_forging/utils/forging_subroutines.py
+++ b/entanglement_forging/utils/forging_subroutines.py
@@ -16,8 +16,11 @@
 
 import inspect
 import warnings
+import re
+from typing import Iterable, Dict, Tuple
 
 import numpy as np
+from qiskit import QuantumCircuit
 from qiskit.algorithms import optimizers
 from qiskit.algorithms.optimizers import SPSA
 from qiskit.algorithms.optimizers.spsa import powerseries
@@ -27,60 +30,101 @@
 from .log import Log
 from .prepare_bitstring import prepare_bitstring
 from .pseudorichardson import richardson_extrapolate
+from .legacy.weighted_pauli_operator import WeightedPauliOperator
 
 
+Matrix = Iterable[Iterable[float]]
+
 # pylint: disable=too-many-locals,too-many-arguments,too-many-branches,invalid-name
-def make_stateprep_circuits(bitstrings, no_bs0_circuits=True):
+def make_stateprep_circuits(
+    bitstrings: Iterable[Iterable[int]], no_bs0_circuits: bool = True, suffix: str = ""
+):
     """Builds the circuits preparing states |b_n> and |phi^p_nm>
-    as defined in <https://arxiv.org/abs/2104.10220>.
+    as defined in <https://arxiv.org/abs/2104.10220>. Also returns
+    a list of tuples which describe any superposition terms which
+    carry a coefficient.
 
     Assumes that the operator amplitudes are real,
     thus does not construct superposition states with odd p.
     """
+
+    # If empty, just return
+    if len(bitstrings) == 0:
+        return [], [], []
+
     # If the spin-up and spin-down spin orbitals are together a 2*N qubit system,
     # the bitstring should be N bits long.
     bitstrings = np.asarray(bitstrings)
     tensor_prep_circuits = [
-        prepare_bitstring(bs, name="bs" + str(bs_idx))
+        prepare_bitstring(bs, name="bs" + suffix + str(bs_idx))
         for bs_idx, bs in enumerate(bitstrings)
     ]
+
     if no_bs0_circuits:
         # Drops the HF bitstring, which is assumed to be first,
         # since hops in our ansatz are chosen to leave it unchanged! (IMPORTANT)
         tensor_prep_circuits = tensor_prep_circuits[1:]
+
     superpos_prep_circuits = []
+    hybrid_superpos_coeffs = {}
     for bs1_idx, bs1 in enumerate(bitstrings):
         for bs2_relative_idx, bs2 in enumerate(bitstrings[bs1_idx + 1 :]):
             diffs = np.where(bs1 != bs2)[0]
-            i = diffs[0]
+
             # TODO implement p -> -p as needed for problems with complex amplitudes  # pylint: disable=fixme
-            if bs1[i]:
-                x = bs2
-                y = bs1  # pylint: disable=unused-variable
+            if len(diffs) > 0:
+                i = diffs[0]
+                if bs1[i]:
+                    x = bs2
+                    y = bs1  # pylint: disable=unused-variable
+                else:
+                    x = bs1
+                    y = bs2  # pylint: disable=unused-variable
+                S = np.delete(diffs, 0)
+                qcirc = prepare_bitstring(np.concatenate((x[:i], [0], x[i + 1 :])))
+                qcirc.h(i)
+                psi_xplus, psi_xmin = [
+                    qcirc.copy(
+                        name=f"bs{suffix}{bs1_idx}bs{suffix}{bs1_idx+1+bs2_relative_idx}{name}"
+                    )
+                    for name in ["xplus", "xmin"]
+                ]
+                psi_xmin.z(i)
+                for psi in [psi_xplus, psi_xmin]:
+                    for target in S:
+                        psi.cx(i, target)
+                    superpos_prep_circuits.append(psi)
+
+            # If the two bitstrings are equivalent -- bn==bm
             else:
-                x = bs1
-                y = bs2  # pylint: disable=unused-variable
-            S = np.delete(diffs, 0)
-            qcirc = prepare_bitstring(np.concatenate((x[:i], [0], x[i + 1 :])))
-            qcirc.h(i)
-            psi_xplus, psi_xmin = [
-                qcirc.copy(name=f"bs{bs1_idx}bs{bs1_idx+1+bs2_relative_idx}{name}")
-                for name in ["xplus", "xmin"]
-            ]
-            psi_xmin.z(i)
-            for psi in [psi_xplus, psi_xmin]:
-                for target in S:
-                    psi.cx(i, target)
-                superpos_prep_circuits.append(psi)
-    return tensor_prep_circuits, superpos_prep_circuits
+                qcirc = prepare_bitstring(bs1)
+                psi_xplus, psi_xmin = [
+                    qcirc.copy(
+                        name=f"bs{suffix}{bs1_idx}bs{suffix}{bs1_idx+1+bs2_relative_idx}{name}"
+                    )
+                    for name in ["xplus", "xmin"]
+                ]
+                hybrid_superpos_coeffs[
+                    (suffix, str(bs1_idx), str(bs1_idx + 1 + bs2_relative_idx))
+                ] = True
+                superpos_prep_circuits += [psi_xplus, psi_xmin]
+    return tensor_prep_circuits, superpos_prep_circuits, hybrid_superpos_coeffs
 
 
 def prepare_circuits_to_execute(
-    params, stateprep_circuits, op_for_generating_circuits, var_form, statevector_mode
+    params: Iterable[float],
+    stateprep_circuits: Iterable[QuantumCircuit],
+    op_for_generating_circuits: WeightedPauliOperator,
+    var_form: QuantumCircuit,
+    statevector_mode: bool,
 ):
     """Given a set of variational parameters and list of 6qb state-preps,
     this function returns all (unique) circuits that must be run to evaluate those samples.
     """
+    # If circuits are empty, just return
+    if len(stateprep_circuits) == 0:
+        return []
+
     circuits_to_execute = []
     # Generate the requisite circuits:
     # pylint: disable=unidiomatic-typecheck
@@ -109,18 +153,20 @@ def prepare_circuits_to_execute(
 # pylint: disable=unbalanced-tuple-unpacking
 def eval_forged_op_with_result(
     result,
-    w_ij_tensor_states,
-    w_ab_superpos_states,
-    params,
-    bitstrings_s,
-    op_for_generating_tensor_circuits,
-    op_for_generating_superpos_circuits,
-    richardson_stretch_factors,
-    statevector_mode,
-    hf_value,
-    add_this_to_mean_values_displayed,
-    no_bs0_circuits=True,
-    verbose=False,
+    w_ij_tensor_states: Matrix,
+    w_ab_superpos_states: Matrix,
+    params: Iterable[float],
+    bitstrings_s_u: np.ndarray,
+    op_for_generating_tensor_circuits: WeightedPauliOperator,
+    op_for_generating_superpos_circuits: WeightedPauliOperator,
+    richardson_stretch_factors: Iterable[float],
+    statevector_mode: bool,
+    hf_value: float,
+    add_this_to_mean_values_displayed: float,
+    bitstrings_s_v: np.ndarray = None,
+    hybrid_superpos_coeffs: Dict[Tuple[int, int, str], bool] = None,
+    no_bs0_circuits: bool = True,
+    verbose: bool = False,
 ):
     """Evaluates the forged operator.
 
@@ -129,56 +175,81 @@ def eval_forged_op_with_result(
     (Hamiltonian in the basis of determinants/bitstrings).
     For reference, also computes mean value obtained without Richardson
     """
-    tensor_state_prefixes = [f"bs{idx}" for idx in range(len(bitstrings_s))]
-    pauli_vals_tensor_states_raw = _get_pauli_expectations_from_result(
+    if bitstrings_s_v is None:
+        bitstrings_s_v = []
+
+    tensor_state_prefixes_u = [f"bsu{idx}" for idx in range(len(bitstrings_s_u))]
+    tensor_state_prefixes_v = []
+
+    if len(bitstrings_s_v) > 0:
+        tensor_state_prefixes_v = [f"bsv{idx}" for idx in range(len(bitstrings_s_v))]
+
+    tensor_state_prefixes = tensor_state_prefixes_u + tensor_state_prefixes_v
+    tensor_expvals = _get_pauli_expectations_from_result(
         result,
         params,
         tensor_state_prefixes,
         op_for_generating_tensor_circuits,
         richardson_stretch_factors=richardson_stretch_factors,
+        hybrid_superpos_coeffs=hybrid_superpos_coeffs,
         statevector_mode=statevector_mode,
         no_bs0_circuits=no_bs0_circuits,
     )
-    pauli_vals_tensor_states_extrap = richardson_extrapolate(
-        pauli_vals_tensor_states_raw, richardson_stretch_factors, axis=2
+    tensor_expvals_extrap = richardson_extrapolate(
+        tensor_expvals, richardson_stretch_factors, axis=2
     )
-    superpos_state_prefixes = []
-    superpos_state_indices = []
+
+    superpos_state_prefixes_u = []
+    superpos_state_prefixes_v = []
     lin_combos = ["xplus", "xmin"]  # ,'yplus','ymin']
-    num_bitstrings = len(bitstrings_s)
+    # num_bitstrings is the number of bitstring combos we have
+    num_bitstrings = len(bitstrings_s_u)
     for x in range(num_bitstrings):
         for y in range(num_bitstrings):
             if x == y:
                 continue
-            bs_string = f"bs{min(x,y)}bs{max(x,y)}"
-            superpos_state_prefixes += [
-                bs_string + lin_combo for lin_combo in lin_combos
+            bsu_string = f"bsu{min(x,y)}bsu{max(x,y)}"
+            superpos_state_prefixes_u += [
+                bsu_string + lin_combo for lin_combo in lin_combos
             ]
-            superpos_state_indices += [(x, y)]
-    raw_states = _get_pauli_expectations_from_result(
+
+            # Determine whether we are handling the two subsystems separately
+            asymmetric_bitstrings = False
+            if len(bitstrings_s_v) > 0:
+                asymmetric_bitstrings = True
+                bsv_string = f"bsv{min(x,y)}bsv{max(x,y)}"
+                superpos_state_prefixes_v += [
+                    bsv_string + lin_combo for lin_combo in lin_combos
+                ]
+
+    superpos_state_prefixes = superpos_state_prefixes_u + superpos_state_prefixes_v
+
+    superpos_expvals = _get_pauli_expectations_from_result(
         result,
         params,
         superpos_state_prefixes,
         op_for_generating_superpos_circuits,
+        hybrid_superpos_coeffs=hybrid_superpos_coeffs,
         richardson_stretch_factors=richardson_stretch_factors,
         statevector_mode=statevector_mode,
         no_bs0_circuits=no_bs0_circuits,
     )
-    pauli_vals_superpos_states_extrap = richardson_extrapolate(
-        raw_states, richardson_stretch_factors, axis=2
+
+    superpos_expvals_extrap = richardson_extrapolate(
+        superpos_expvals, richardson_stretch_factors, axis=2
     )
 
     forged_op_results_w_and_wo_extrapolation = []
-    for (pauli_vals_tensor_states, pauli_vals_superpos_states) in [
-        [pauli_vals_tensor_states_extrap, pauli_vals_superpos_states_extrap],
-        [pauli_vals_tensor_states_raw[:, :, 0], raw_states[:, :, 0]],
+    for (tensor_expvals_real, superpos_expvals_real) in [
+        [tensor_expvals_extrap, superpos_expvals_extrap],
+        [tensor_expvals[:, :, 0], superpos_expvals[:, :, 0]],
     ]:
         h_schmidt = compute_h_schmidt(
-            pauli_vals_tensor_states,
-            pauli_vals_superpos_states,
+            tensor_expvals_real,
+            superpos_expvals_real,
             w_ij_tensor_states,
             w_ab_superpos_states,
-            superpos_state_indices,
+            asymmetric_bitstrings,
         )
         if no_bs0_circuits:
             # IMPORTANT: ASSUMING HOPGATES CHOSEN S.T. HF BITSTRING
@@ -200,10 +271,12 @@ def eval_forged_op_with_result(
         op_mean = evals[0]
         op_std = None
         forged_op_results_w_and_wo_extrapolation.append([op_mean, op_std, schmidts])
+
     (
         forged_op_results_extrap,
         forged_op_results_raw,
     ) = forged_op_results_w_and_wo_extrapolation  # pylint: disable=unbalanced-tuple-unpacking
+
     return forged_op_results_extrap, forged_op_results_raw
 
 
@@ -213,6 +286,7 @@ def _get_pauli_expectations_from_result(
     stateprep_strings,
     op_for_generating_circuits,
     statevector_mode,
+    hybrid_superpos_coeffs=None,
     richardson_stretch_factors=None,
     no_bs0_circuits=True,
 ):
@@ -240,7 +314,22 @@ def _get_pauli_expectations_from_result(
     )
     pauli_names_temp = [p[1].to_label() for p in op_for_generating_circuits.paulis]
     for prep_idx, prep_string in enumerate(stateprep_strings):
-        if no_bs0_circuits and prep_string == "bs0":
+        suffix = prep_string[2]
+        if suffix not in ["u", "v"]:
+            raise ValueError(f"Invalid stateprep circuit name: {prep_string}")
+        bitstring_pair = [0, 0]
+        tensor_circuit = True
+        num_bs_terms = prep_string.count("bs")
+        if (num_bs_terms > 2) or (num_bs_terms == 0):
+            raise ValueError(f"Invalid stateprep circuit name: {prep_string}")
+        elif num_bs_terms == 2:
+            tensor_circuit = False
+            prep_string_digits = [
+                int(float(s)) for s in re.findall(r"-?\d+\.?\d*", prep_string)
+            ]
+            bitstring_pair = [prep_string_digits[0], prep_string_digits[1]]
+
+        if no_bs0_circuits and (prep_string == "bsu0" or prep_string == "bsv0"):
             # IMPORTANT: ASSUMING HOPGATES CHOSEN S.T.
             # HF BITSTRING (FIRST BITSTRING) IS UNAFFECTED,
             # ALLOWING CORRESPONDING ENERGY TO BE FIXED AT H
@@ -258,9 +347,6 @@ def _get_pauli_expectations_from_result(
                     np.einsum("i,Mij,j->M", np.conj(psi), op_matrices, psi)
                 )
             else:
-                # pcov_each_richardson = np.zeros(len(richardson_stretch_factors),,
-                # len(op_for_generating_circuits.paulis),
-                # len(op_for_generating_circuits.paulis))+np.nan
                 pauli_vals_temp, _ = _eval_each_pauli_with_result(
                     tpbgwpo=op_for_generating_circuits,
                     result=result,
@@ -278,6 +364,17 @@ def _get_pauli_expectations_from_result(
                     "imaginary part which will be discarded."
                 )
             pauli_vals[prep_idx, :, rich_idx, 0] = np.real(pauli_vals_alphabetical)
+        key = (suffix, str(bitstring_pair[0]), str(bitstring_pair[1]))
+        if key in hybrid_superpos_coeffs.keys():
+            if prep_string[-4:] == "xmin":
+                pauli_vals[prep_idx] *= 0
+            elif prep_string[-5:] == "xplus":
+                pass
+            else:
+                raise ValueError(f"Invalid circuit name: {prep_string}")
+        elif not tensor_circuit:
+            pauli_vals[prep_idx] *= 1 / 2
+
     return pauli_vals
 
 
@@ -311,35 +408,81 @@ def _eval_each_pauli_with_result(
 
 
 def compute_h_schmidt(
-    pauli_vals_tensor_states,
-    pauli_vals_superpos_states,
-    w_ij_tensor_states,
-    w_ab_superpos_states,
-    superpos_state_indices,
+    tensor_expvals,
+    superpos_expvals,
+    w_ij_tensor_weights,
+    w_ab_superpos_weights,
+    asymmetric_bitstrings,
 ):
     """Computes the schmidt decomposition of the Hamiltonian. TODO checkthis.  # pylint: disable=fixme
 
     Pauli val arrays contain expectation values <x|P|x> and their standard deviations.
     Axes are [x_idx, P_idx, mean_or_variance]
-    W_ij: Coefficients W_ij. Axes: [index of Pauli string for eta, index of Pauli string for tau].
+    Coefficients w_ij/w_ab: Axes: [index of Pauli string for eta, index of Pauli string for tau].
+    asymmetric_bitstrings: A boolean which signifies whether the U and V subsystems have
+                            different ansatze.
     """
+    # Number of tensor stateprep circuits
+    num_tensor_terms = int(np.shape(tensor_expvals)[0])
+
+    if asymmetric_bitstrings:
+        num_tensor_terms = int(
+            num_tensor_terms / 2
+        )  # num_tensor_terms should always be even here
+        tensor_exp_vals_u = tensor_expvals[:num_tensor_terms, :, 0]
+        tensor_exp_vals_v = tensor_expvals[num_tensor_terms:, :, 0]
+    else:
+        # Use the same expectation values for both subsystem calculations
+        tensor_exp_vals_u = tensor_expvals[:, :, 0]
+        tensor_exp_vals_v = tensor_expvals[:, :, 0]
+
+    # Calculate the schmidt summation over the U and V subsystems and diagonalize the values
     h_schmidt_diagonal = np.einsum(
         "ij,xi,xj->x",
-        w_ij_tensor_states,
-        pauli_vals_tensor_states[:, :, 0],
-        pauli_vals_tensor_states[:, :, 0],
+        w_ij_tensor_weights,
+        tensor_exp_vals_u,
+        tensor_exp_vals_v,
     )
     h_schmidt = np.diag(h_schmidt_diagonal)
+
     # If including the +/-Y superpositions (omitted at time of writing
     # since they typically have 0 net contribution) would change this to 4 instead of 2.
     num_lin_combos = 2
-    p_plus_x = pauli_vals_superpos_states[0::num_lin_combos, :, 0]
-    p_minus_x = pauli_vals_superpos_states[1::num_lin_combos, :, 0]
-    p_delta_x = p_plus_x - p_minus_x
-    # -(1/4)*np.einsum('ij,xyi,xyj->xy',W_ij_array,PdeltaY,PdeltaY,optimize=True))
-    h_schmidt_off_diagonals = (1 / 4) * np.einsum(
-        "ab,xa,xb->x", w_ab_superpos_states, p_delta_x, p_delta_x
+
+    num_superpos_terms = int(np.shape(superpos_expvals)[0])
+    if asymmetric_bitstrings:
+        num_superpos_terms = int(
+            num_superpos_terms / 2
+        )  # num_superpos_terms should always be even here
+        pvss_u = superpos_expvals[:num_superpos_terms, :, 0]
+        pvss_v = superpos_expvals[num_superpos_terms:, :, 0]
+    else:
+        pvss_u = superpos_expvals[:, :, 0]
+        pvss_v = superpos_expvals[:, :, 0]
+
+    # Calculate delta for U subsystem
+    p_plus_x_u = pvss_u[0::num_lin_combos, :]
+    p_minus_x_u = pvss_u[1::num_lin_combos, :]
+    p_delta_x_u = p_plus_x_u - p_minus_x_u
+
+    # Calculate delta for V subsystem
+    if asymmetric_bitstrings:
+        p_plus_x_v = pvss_v[0::num_lin_combos, :]
+        p_minus_x_v = pvss_v[1::num_lin_combos, :]
+        p_delta_x_v = p_plus_x_v - p_minus_x_v
+    else:
+        p_delta_x_v = p_delta_x_u
+
+    h_schmidt_off_diagonals = np.einsum(
+        "ab,xa,xb->x", w_ab_superpos_weights, p_delta_x_u, p_delta_x_v
     )
+
+    superpos_state_indices = [
+        (x, y)
+        for x in range(h_schmidt.shape[0])
+        for y in range(h_schmidt.shape[0])
+        if x != y
+    ]
     for element, indices in zip(h_schmidt_off_diagonals, superpos_state_indices):
         h_schmidt[indices] = element
     return h_schmidt  # , H_schmidt_vars)
diff --git a/setup.cfg b/setup.cfg
index 2f9048e..65ffa72 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = entanglement_forging
-version = 0.1.0
+version = 0.2.0
 url = https://github.com/qiskit-community/prototype-entanglement-forging
 maintainer = Caleb Johnson
 maintainer_email = caleb.johnson@ibm.com
diff --git a/tox.ini b/tox.ini
index ab693c4..af3e431 100644
--- a/tox.ini
+++ b/tox.ini
@@ -7,4 +7,4 @@ extras = test
 commands =
   python -m black --check entanglement_forging tests
   python -m pytest tests
-  python -m treon docs/ --threads 5
+  python -m treon docs/


Test patch:
diff --git a/tests/wrappers/test_data/CH3_one_body.npy b/tests/wrappers/test_data/CH3_one_body.npy
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diff --git a/tests/wrappers/test_data/CN_one_body.npy b/tests/wrappers/test_data/CN_one_body.npy
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index 0000000..3c44338
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diff --git a/tests/wrappers/test_data/O2_one_body.npy b/tests/wrappers/test_data/O2_one_body.npy
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index 0000000..c338db5
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diff --git a/tests/wrappers/test_data/O2_two_body.npy b/tests/wrappers/test_data/O2_two_body.npy
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diff --git a/tests/wrappers/test_data/OH_one_body.npy b/tests/wrappers/test_data/OH_one_body.npy
new file mode 100644
index 0000000..66e661b
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diff --git a/tests/wrappers/test_data/OH_two_body.npy b/tests/wrappers/test_data/OH_two_body.npy
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index 0000000..dce300d
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diff --git a/tests/wrappers/test_data/TS_one_body.npy b/tests/wrappers/test_data/TS_one_body.npy
new file mode 100644
index 0000000..6eb87c5
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diff --git a/tests/wrappers/test_data/TS_two_body.npy b/tests/wrappers/test_data/TS_two_body.npy
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index 0000000..d6ef3eb
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diff --git a/tests/wrappers/test_data/two_body_alpha_alpha.npy b/tests/wrappers/test_data/two_body_alpha_alpha.npy
new file mode 100644
index 0000000..1423405
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diff --git a/tests/wrappers/test_entanglement_forged_ground_state_eigensolver.py b/tests/wrappers/test_entanglement_forged_ground_state_eigensolver.py
index 811c0c9..44d2431 100644
--- a/tests/wrappers/test_entanglement_forged_ground_state_eigensolver.py
+++ b/tests/wrappers/test_entanglement_forged_ground_state_eigensolver.py
@@ -13,6 +13,7 @@
 """Integration tests for EntanglementForgedVQE module."""
 # pylint: disable=wrong-import-position
 import unittest
+import os
 
 import numpy as np
 from qiskit import BasicAer
@@ -27,6 +28,9 @@
     GroundStateEigensolver,
     NumPyMinimumEigensolverFactory,
 )
+from qiskit_nature.transformers.second_quantization.electronic.active_space_transformer import (
+    ActiveSpaceTransformer,
+)
 from qiskit_nature import settings
 
 settings.dict_aux_operators = True
@@ -45,8 +49,56 @@ class TestEntanglementForgedGroundStateEigensolver(unittest.TestCase):
     def setUp(self):
         np.random.seed(42)
         self.backend = BasicAer.get_backend("statevector_simulator")
+        self.config = EntanglementForgedConfig(
+            backend=self.backend,
+            maxiter=0,
+            initial_params=[0.0],
+            optimizer_name="COBYLA",
+        )
+
+        # TS
+        self.mock_ts_ansatz = self.create_mock_ansatz(4)
+        self.hcore_ts = np.load(
+            os.path.join(os.path.dirname(__file__), "test_data", "TS_one_body.npy")
+        )
+        self.eri_ts = np.load(
+            os.path.join(os.path.dirname(__file__), "test_data", "TS_two_body.npy")
+        )
+        self.energy_shift_ts = -264.7518219120776
+
+        # O2
+        self.mock_o2_ansatz = self.create_mock_ansatz(8)
+        self.hcore_o2 = np.load(
+            os.path.join(os.path.dirname(__file__), "test_data", "O2_one_body.npy")
+        )
+        self.eri_o2 = np.load(
+            os.path.join(os.path.dirname(__file__), "test_data", "O2_two_body.npy")
+        )
+        self.energy_shift_o2 = -99.83894101027317
+
+        # CH3
+        self.mock_ch3_ansatz = self.create_mock_ansatz(6)
+        self.hcore_ch3 = np.load(
+            os.path.join(os.path.dirname(__file__), "test_data", "CH3_one_body.npy")
+        )
+        self.eri_ch3 = np.load(
+            os.path.join(os.path.dirname(__file__), "test_data", "CH3_two_body.npy")
+        )
+        self.energy_shift_ch3 = -31.90914780401554
+
+    def create_mock_ansatz(self, num_qubits):
+        n_theta = 1
+        theta = Parameter("θ")
+        mock_gate = QuantumCircuit(1, name="mock gate")
+        mock_gate.rz(theta, 0)
 
-    def test_forged_vqe_for_hydrogen(self):
+        theta_vec = [Parameter("θ%d" % i) for i in range(1)]
+        ansatz = QuantumCircuit(num_qubits)
+        ansatz.append(mock_gate.to_gate({theta: theta_vec[0]}), [0])
+
+        return ansatz
+
+    def test_forged_vqe_H2(self):
         """Test of applying Entanglement Forged VQE to to compute the energy of a H2 molecule."""
         # setup problem
         molecule = Molecule(
@@ -76,7 +128,7 @@ def test_forged_vqe_for_hydrogen(self):
 
         self.assertAlmostEqual(forged_result.ground_state_energy, -1.1219365445030705)
 
-    def test_forged_vqe_for_water(self):  # pylint: disable=too-many-locals
+    def test_forged_vqe_H2O(self):  # pylint: disable=too-many-locals
         """Test of applying Entanglement Forged VQE to to compute the energy of a H20 molecule."""
         # setup problem
         radius_1 = 0.958  # position for the first H atom
@@ -143,7 +195,11 @@ def test_forged_vqe_for_water(self):  # pylint: disable=too-many-locals
         converter = QubitConverter(JordanWignerMapper())
 
         solver = EntanglementForgedGroundStateSolver(
-            converter, ansatz, reduced_bitstrings, config, orbitals_to_reduce
+            converter,
+            ansatz,
+            reduced_bitstrings,
+            config,
+            orbitals_to_reduce=orbitals_to_reduce,
         )
         forged_result = solver.solve(problem)
         self.assertAlmostEqual(forged_result.ground_state_energy, -75.68366174497027)
@@ -227,3 +283,222 @@ def test_ground_state_eigensolver_with_ef_driver(self):
         self.assertAlmostEqual(
             -1.137306026563, np.real(result.eigenenergies[0]) + repulsion_energy
         )
+
+    def test_TS_1(self):
+        driver = EntanglementForgedDriver(
+            hcore=self.hcore_ts,
+            mo_coeff=np.eye(4, 4),
+            eri=self.eri_ts,
+            num_alpha=2,
+            num_beta=2,
+            nuclear_repulsion_energy=self.energy_shift_ts,
+        )
+        problem = ElectronicStructureProblem(driver)
+        problem.second_q_ops()
+
+        converter = QubitConverter(JordanWignerMapper())
+
+        bitstrings_u = [[1, 1, 0, 0], [1, 0, 1, 0], [0, 1, 0, 1]]
+        bitstrings_v = [[1, 1, 0, 0], [1, 0, 1, 0], [0, 1, 0, 1]]
+
+        calc = EntanglementForgedGroundStateSolver(
+            converter,
+            self.mock_ts_ansatz,
+            bitstrings_u=bitstrings_u,
+            bitstrings_v=bitstrings_v,
+            config=self.config,
+            orbitals_to_reduce=[],
+        )
+        res = calc.solve(problem)
+        self.assertAlmostEqual(-267.5081390468769, res.ground_state_energy)
+
+    def test_TS_2(self):
+        driver = EntanglementForgedDriver(
+            hcore=self.hcore_ts,
+            mo_coeff=np.eye(4, 4),
+            eri=self.eri_ts,
+            num_alpha=2,
+            num_beta=2,
+            nuclear_repulsion_energy=self.energy_shift_ts,
+        )
+        problem = ElectronicStructureProblem(driver)
+        problem.second_q_ops()
+
+        converter = QubitConverter(JordanWignerMapper())
+
+        bitstrings_u = [
+            [1, 1, 0, 0],
+            [1, 0, 1, 0],
+            [0, 1, 1, 0],
+            [1, 0, 0, 1],
+            [0, 1, 0, 1],
+        ]
+        bitstrings_v = [
+            [1, 1, 0, 0],
+            [1, 0, 1, 0],
+            [1, 0, 0, 1],
+            [0, 1, 1, 0],
+            [0, 1, 0, 1],
+        ]
+
+        calc = EntanglementForgedGroundStateSolver(
+            converter,
+            self.mock_ts_ansatz,
+            bitstrings_u=bitstrings_u,
+            bitstrings_v=bitstrings_v,
+            config=self.config,
+            orbitals_to_reduce=[],
+        )
+        res = calc.solve(problem)
+        self.assertAlmostEqual(-267.51696723002567, res.ground_state_energy)
+
+    def test_TS_3(self):
+        driver = EntanglementForgedDriver(
+            hcore=self.hcore_ts,
+            mo_coeff=np.eye(4, 4),
+            eri=self.eri_ts,
+            num_alpha=2,
+            num_beta=2,
+            nuclear_repulsion_energy=self.energy_shift_ts,
+        )
+        problem = ElectronicStructureProblem(driver)
+        problem.second_q_ops()
+
+        converter = QubitConverter(JordanWignerMapper())
+
+        bitstrings_u = [
+            [1, 1, 0, 0],
+            [0, 0, 1, 1],
+            [1, 0, 1, 0],
+            [1, 0, 0, 1],
+            [0, 1, 1, 0],
+            [0, 1, 0, 1],
+            [1, 1, 0, 0],
+        ]
+        bitstrings_v = [
+            [1, 1, 0, 0],
+            [1, 1, 0, 0],
+            [1, 0, 1, 0],
+            [0, 1, 1, 0],
+            [1, 0, 0, 1],
+            [0, 1, 0, 1],
+            [0, 0, 1, 1],
+        ]
+
+        calc = EntanglementForgedGroundStateSolver(
+            converter,
+            self.mock_ts_ansatz,
+            bitstrings_u=bitstrings_u,
+            bitstrings_v=bitstrings_v,
+            config=self.config,
+            orbitals_to_reduce=[],
+        )
+        res = calc.solve(problem)
+        self.assertAlmostEqual(-267.5203802563949, res.ground_state_energy)
+
+    def test_TS_4(self):
+        driver = EntanglementForgedDriver(
+            hcore=self.hcore_ts,
+            mo_coeff=np.eye(4, 4),
+            eri=self.eri_ts,
+            num_alpha=2,
+            num_beta=2,
+            nuclear_repulsion_energy=self.energy_shift_ts,
+        )
+        problem = ElectronicStructureProblem(driver)
+        problem.second_q_ops()
+
+        converter = QubitConverter(JordanWignerMapper())
+
+        bitstrings_u = [[1, 1, 0, 0], [1, 0, 1, 0], [1, 1, 0, 0], [1, 0, 1, 0]]
+        bitstrings_v = [[1, 1, 0, 0], [1, 0, 1, 0], [1, 0, 1, 0], [1, 1, 0, 0]]
+
+        calc = EntanglementForgedGroundStateSolver(
+            converter,
+            self.mock_ts_ansatz,
+            bitstrings_u=bitstrings_u,
+            bitstrings_v=bitstrings_v,
+            config=self.config,
+            orbitals_to_reduce=[],
+        )
+        res = calc.solve(problem)
+        self.assertAlmostEqual(-267.49261903008824, res.ground_state_energy)
+
+    def test_O2_1(self):
+        driver = EntanglementForgedDriver(
+            hcore=self.hcore_o2,
+            mo_coeff=np.eye(8, 8),
+            eri=self.eri_o2,
+            num_alpha=6,
+            num_beta=6,
+            nuclear_repulsion_energy=self.energy_shift_o2,
+        )
+        problem = ElectronicStructureProblem(driver)
+        problem.second_q_ops()
+
+        converter = QubitConverter(JordanWignerMapper())
+
+        bitstrings_u = [
+            [1, 1, 1, 1, 1, 1, 0, 0],
+            [1, 1, 1, 1, 1, 0, 1, 0],
+            [1, 1, 1, 1, 0, 1, 1, 0],
+            [1, 1, 0, 1, 1, 1, 1, 0],
+        ]
+        bitstrings_v = [
+            [1, 1, 1, 1, 1, 0, 1, 0],
+            [1, 1, 1, 1, 1, 1, 0, 0],
+            [1, 1, 0, 1, 1, 1, 1, 0],
+            [1, 1, 1, 1, 0, 1, 1, 0],
+        ]
+
+        calc = EntanglementForgedGroundStateSolver(
+            converter,
+            self.mock_o2_ansatz,
+            bitstrings_u=bitstrings_u,
+            bitstrings_v=bitstrings_v,
+            config=self.config,
+            orbitals_to_reduce=[],
+        )
+        res = calc.solve(problem)
+        self.assertAlmostEqual(-147.63645235088566, res.ground_state_energy)
+
+    def test_CH3(self):
+        driver = EntanglementForgedDriver(
+            hcore=self.hcore_ch3,
+            mo_coeff=np.eye(6, 6),
+            eri=self.eri_ch3,
+            num_alpha=3,
+            num_beta=2,
+            nuclear_repulsion_energy=self.energy_shift_ch3,
+        )
+
+        problem = ElectronicStructureProblem(driver)
+        problem.second_q_ops()
+
+        converter = QubitConverter(JordanWignerMapper())
+
+        bitstrings_u = [
+            [1, 1, 1, 0, 0, 0],
+            [0, 1, 1, 0, 0, 1],
+            [1, 0, 1, 0, 1, 0],
+            [1, 0, 1, 1, 0, 0],
+            [0, 1, 1, 1, 0, 0],
+        ]
+        bitstrings_v = [
+            [1, 1, 0, 0, 0, 0],
+            [0, 1, 0, 0, 0, 1],
+            [1, 0, 0, 0, 1, 0],
+            [1, 0, 0, 1, 0, 0],
+            [0, 1, 0, 1, 0, 0],
+        ]
+
+        calc = EntanglementForgedGroundStateSolver(
+            converter,
+            self.mock_ch3_ansatz,
+            bitstrings_u=bitstrings_u,
+            bitstrings_v=bitstrings_v,
+            config=self.config,
+            orbitals_to_reduce=[],
+        )
+        res = calc.solve(problem)
+        self.assertAlmostEqual(-39.09031477502881, res.ground_state_energy)