Repository: qiskit-community/qiskit-machine-learning (language: python)

Issue:
Fix QNN for input and weights ordering
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### Summary

Fixes #727

Reassigns QNN circuit parameters so the order of circuit.parameters, which is alphanumerically ordered when returned, comes out as as desired for the primitive and the data (parameters) as ordered there which is inputs followed by weights in the given array.

### Details and comments

I added a method to reassign parameters in the circuit so it's a new circuit copy from that supplied, but whose parameters are in the order needed by the data passed to the primitive when it runs. This I put in `self._circuit` so that internally its using this circuit now when running etc. As there are parameter and circuit getters, I left these all returning what was passed in originally ( as before) so the original circuit is assigned to a different named instance var which is returned by the getter. 

So what is run is equivalent but not the circuit as that passed. An alternative would be to alter the data order so it corresponds to the order of the parameters. This seemed less desirable on initial discussion since it changes the data. 

This PR still needs a release note but I figured lets settle that this is how we want to proceed before I do that. Not that I imagine it would change the text thereof though. **_Update_**: I added one anyway...

- [x] Add release note
- [x] Address change in tutorial `11_quantum_convolutional_neural_networks` (see comment below in this page)

Fixes #678

In updating the tutorial with a new initial point I saw the issue described in #678 where "c1" was not a latin c from the overall parameter order (indeed searching for c in the text did not locate it) So this addresses that issue too.
As part of the update I also set a random_state to the test_train_split so the result in the notebook in the same each time its run. Otherwise the split is different each time which made the results different each time.

This is one of the tutorials, per issue #725, where the circuit.draw("mpl") has deprectation messages so since this is being modified I corrected that as well, just for this tutorial, and used the clifford style which keeps the same look as before
Fix QNN for input and weights ordering
<!--
⚠️ If you do not respect this template, your pull request will be closed.
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⚠️ Also, please add it in the CHANGELOG file under Unreleased section.
⚠️ If your pull request fixes an open issue, please link to the issue.

✅ I have added the tests to cover my changes.
✅ I have updated the documentation accordingly.
✅ I have read the CONTRIBUTING document.
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### Summary

Fixes #727

Reassigns QNN circuit parameters so the order of circuit.parameters, which is alphanumerically ordered when returned, comes out as as desired for the primitive and the data (parameters) as ordered there which is inputs followed by weights in the given array.

### Details and comments

I added a method to reassign parameters in the circuit so it's a new circuit copy from that supplied, but whose parameters are in the order needed by the data passed to the primitive when it runs. This I put in `self._circuit` so that internally its using this circuit now when running etc. As there are parameter and circuit getters, I left these all returning what was passed in originally ( as before) so the original circuit is assigned to a different named instance var which is returned by the getter. 

So what is run is equivalent but not the circuit as that passed. An alternative would be to alter the data order so it corresponds to the order of the parameters. This seemed less desirable on initial discussion since it changes the data. 

This PR still needs a release note but I figured lets settle that this is how we want to proceed before I do that. Not that I imagine it would change the text thereof though. **_Update_**: I added one anyway...

- [x] Add release note
- [x] Address change in tutorial `11_quantum_convolutional_neural_networks` (see comment below in this page)

Fixes #678

In updating the tutorial with a new initial point I saw the issue described in #678 where "c1" was not a latin c from the overall parameter order (indeed searching for c in the text did not locate it) So this addresses that issue too.
As part of the update I also set a random_state to the test_train_split so the result in the notebook in the same each time its run. Otherwise the split is different each time which made the results different each time.

This is one of the tutorials, per issue #725, where the circuit.draw("mpl") has deprectation messages so since this is being modified I corrected that as well, just for this tutorial, and used the clifford style which keeps the same look as before
Update tutorials for deprecated circuit draw style
A number of Tutorials have this deprecation warning message. The drawing should be updated to avoid this.

`qc.draw("mpl")`

>/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/qiskit/visualization/circuit/matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to "iqp" in a following release. To silence this warning, specify the current default explicitly as style="clifford", or the new default as style="iqp".
  self._style, def_font_ratio = load_style(self._style)

These are the tutorials - I just put the number not the full name. There can be multiple places within each that needs fixing.

- [x] 01
- [x] 02
- [x] 02a
- [x] 04
- [x] 05
- [x] 10
- [x] 11 (updated by #728)
- [x] 12



Discussion / hints:
## Pull Request Test Coverage Report for [Build 7451895217](https://coveralls.io/builds/64942462)


* **0** of **0**   changed or added relevant lines in **0** files are covered.
* No unchanged relevant lines lost coverage.
* Overall coverage decreased (**-0.09%**) to **92.567%**

---



|  Totals | [![Coverage Status](https://coveralls.io/builds/64942462/badge)](https://coveralls.io/builds/64942462) |
| :-- | --: |
| Change from base [Build 7451842530](https://coveralls.io/builds/64942291): |  -0.09% |
| Covered Lines: | 1868 |
| Relevant Lines: | 2018 |

---
##### 💛  - [Coveralls](https://coveralls.io)

A note:

As the standard feature map is mostly used in tests and tutorials with "x" as the name and the standard ansatzes used with greek theta the order was naturally as needed so no change in tests was needed and the tutorials look the same as before etc....

But with the exception of 11_quantum_convolutional_neural_networks whose behavior is now different and may need to be reviewed. In it it says

> Since model training may take a long time we have already pre-trained the model for some iterations and saved the pre-trained weights. We’ll continue training from that point by setting initial_point to a vector of pre-trained weights.

The plot no longer looks flat like it did so most likely the initial point needs reviewing and the accuracy is lower now - most likely because maxiter is limited to 200 and it no longer reaches a more optimal solution

![image](https://github.com/qiskit-community/qiskit-machine-learning/assets/40241007/876df951-adae-4cf2-9f71-1025ec5e4c80)

## Pull Request Test Coverage Report for [Build 7451895217](https://coveralls.io/builds/64942462)


* **0** of **0**   changed or added relevant lines in **0** files are covered.
* No unchanged relevant lines lost coverage.
* Overall coverage decreased (**-0.09%**) to **92.567%**

---



|  Totals | [![Coverage Status](https://coveralls.io/builds/64942462/badge)](https://coveralls.io/builds/64942462) |
| :-- | --: |
| Change from base [Build 7451842530](https://coveralls.io/builds/64942291): |  -0.09% |
| Covered Lines: | 1868 |
| Relevant Lines: | 2018 |

---
##### 💛  - [Coveralls](https://coveralls.io)

A note:

As the standard feature map is mostly used in tests and tutorials with "x" as the name and the standard ansatzes used with greek theta the order was naturally as needed so no change in tests was needed and the tutorials look the same as before etc....

But with the exception of 11_quantum_convolutional_neural_networks whose behavior is now different and may need to be reviewed. In it it says

> Since model training may take a long time we have already pre-trained the model for some iterations and saved the pre-trained weights. We’ll continue training from that point by setting initial_point to a vector of pre-trained weights.

The plot no longer looks flat like it did so most likely the initial point needs reviewing and the accuracy is lower now - most likely because maxiter is limited to 200 and it no longer reaches a more optimal solution

![image](https://github.com/qiskit-community/qiskit-machine-learning/assets/40241007/876df951-adae-4cf2-9f71-1025ec5e4c80)



Fix patch:
diff --git a/docs/tutorials/11_qcnn_initial_point.json b/docs/tutorials/11_qcnn_initial_point.json
index 142b3c452..e9fedaa57 100644
--- a/docs/tutorials/11_qcnn_initial_point.json
+++ b/docs/tutorials/11_qcnn_initial_point.json
@@ -1,1 +1,1 @@
-[1.930057091422052, 0.2829424508139703, 0.35555636265939633, 0.1750006532903061, 0.3002103666790018, 0.6641911912373437, 1.3310981300850042, 0.5022717197547227, 0.44912874128880675, 0.40236963192983266, 0.3459537084665159, 0.9786311288435154, 0.48716712269991697, -0.007081389738930712, 0.21570815199311827, 0.07334182375267477, 0.6907887498355103, 0.21771166428570735, 1.087665977608006, 1.2571463700739218, 1.0866597360102666, 2.126145551821481, 0.8914518096731741, 1.5053260036617715, 0.44798876926441555, 0.9498701675467225, 0.15490304396579338, 0.1338674031994701, -0.6938374500039391, 0.029396385425104116, -0.09785818314088227, -0.31198441382224246, 0.20004568516690807, 1.848494069662786, -0.028371899054628447, -0.15229494459622284, 0.7653870524298326, 0.6881492316484289, 0.6759011152318357, 1.6028387103546868, 0.47711915171800057, -0.26162053028790294, -0.12898443497061718, 0.5281303751714184, 0.4957555866394333, 1.6095784010055925, 0.5685823964468215, 1.2812276175594062, 0.3032325725579015, 1.4291081956286258, 0.7081163438891277, 1.8291375321912147, -0.11047287562207528, 0.2751308409529747, 0.2834764252747557, 0.29668607404725605, 0.008300790063532154, 0.6707732056265118, 0.5325267632509095, 0.7240676576317691, 0.08123934531343553, -0.0038536767244725153, -0.1001165849018211]
\ No newline at end of file
+[1.68270961, 0.11605051, 0.34864916, 0.74675878, 1.87124355, 1.49219533, -0.38654013, 1.6794744, 1.46546974, 2.16547249, 1.05274095, 2.2565039, 0.31246977, -0.0977787, 0.26751274, -0.24319314, 0.28359516, 0.17431664, 0.86434056, 1.08183541, 1.64600062, 2.17350294, 0.17430376, 0.08381051, 0.30748524, 1.6671458, 0.23076889, 0.40720057, -0.38243368, -0.28842447, 0.08507067, 1.34472166, -0.08210173, 1.10931829, 0.15418569, 0.65755067, 3.09541972, 0.41647156, 1.12894435, 1.03898584, 0.64931532, -0.43442102, 0.24324246, 0.98370841, 0.57256531, 0.25832156, 0.42749823, 1.78949614, 0.27749909, 0.07237166, 0.05920573, 0.41896919, 0.66868785, 0.73035314, 0.00984019, 0.72243278, 1.10299638, 0.80821682, 0.39530007, 1.03814133, 0.41697893, 0.53016156, 1.13594375]
diff --git a/docs/tutorials/11_quantum_convolutional_neural_networks.ipynb b/docs/tutorials/11_quantum_convolutional_neural_networks.ipynb
index 719b0952d..df378bc6a 100644
--- a/docs/tutorials/11_quantum_convolutional_neural_networks.ipynb
+++ b/docs/tutorials/11_quantum_convolutional_neural_networks.ipynb
@@ -244,7 +244,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 507.852x144.48 with 1 Axes>"
       ]
@@ -272,7 +272,7 @@
     "# Let's draw this circuit and see what it looks like\n",
     "params = ParameterVector(\"θ\", length=3)\n",
     "circuit = conv_circuit(params)\n",
-    "circuit.draw(\"mpl\")"
+    "circuit.draw(\"mpl\", style=\"clifford\")"
    ]
   },
   {
@@ -297,7 +297,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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zNQDYnx3n6tzjB/X02/cUOxZVO0b3DXiq2DVNdN0m2r5/fdF1jdXXNBTAqrj6taRbk4yvU2elP803jo+7RYqpa5/igCR1bNVHn6yerd4Jd2jDjnTFRLWSx20M4dhm3bVw5Uz9rP/vtW7bp+rQoqfFaWGlxvWlodca3x/Plya9Z3z/8EBjXFu92qskew5uUYuYBBV6C+VyubVu2xJ1bXeTJKmwsEDPzrlXo2+fpqg6MRYnBQAAAFCVRdWJ0Qup6ZcdX7n5g2LXNJKKrmvscE3DLZAoUrPahe9bRtur+CVJrWI6KswTrodnpSjME66IarWKXqsf2UCdWvfTuJf7aMf369U7YZB1QWErdS66ZbdZlD2LX5K0+4ctatkwQecKzuho3iGt2/apYpt2kyRlbJyvrH2r9dpHv9fDs1L07W72uAMAAABgL5de0xw5fkBZ2WsU27SbLa5pWAEGRxl16zNF3y/fuEBzlz2rJtHtlNQmWff0/4Pu6f+Hotfzz+Tp2Tn3Kq5ZdyuiAhUy/IY/FX3/2sOblLFhntxu43cU13cepus7D7MqWoVlZmZaHQEA4AfmawCwPyfN1Zde00hS3053y+122+KahhVgcKx+iYM186E1SmqTXOLrNapHakbaCqXeMT3IyYArl5w01OoIlTZvnv33KQMAMF8DgBM4fa6203UNK8BgG7UbVM33RmiriuP68ccf19Ch9vmLDgBQMuZrALA/q+bqULyOoQAG24i73uoEgPkY1wAAAACcJhSvY7gFEgAAAAAAACGNAhgAwFQzZ860OgIAwA/M1wBgf8zV5qEABgAwVUJCgtURAAB+YL4GAPtjrjYPBTAAgKmSk0t+MisAwF6YrwHA/pirzUMBDAAAAAAAACGNAhgAAAAAAABCGgUwAICpunfvbnUEAIAfmK8BwP6Yq81DAQwAYKrVq1dbHQEA4AfmawCwP+Zq81AAAwAAAAAAQEijAAYAAAAAAICQRgEMAGCqBQsWWB0BAOAH5msAsD/mavNQAAMAAAAAAEBIowAGADDV4MGDrY4AAPAD8zUA2B9ztXnCrA5gd98tlU4csua9azeQ4q635r2tQF8HD30NAAAQ2vi8h1DEuMaVoABWjhOHpKPZVqeoGujr4KGvAQAAQhuf9xCKGNe4EtwCCQAwVVpamtURAAB+YL4GAPtjrjYPBTAAgKnGjh1rdQQAgB+YrwHA/pirzcMtkECQ+HzSth+kjXulvbkXjj+6QGpUT2p+ldStlfE94GT9+vXT8uXLrY4BACgH83VgHD4hrd4l7Tksff+jlH9O8ril6NpSsyipY1OpQyPJzVIEOITPJ+09Iq3bI+07Ih08Jp0rlMI9UkxdqdlVUucWUourJJfL6rShh7naPBTATPLwrBRl7vlSHk+43G6PYuq30vAbJio5aYjV0UKOE/t6S7b0wTfSoeOXv5Z3xiiMbftB+uxbqW1D6e5u1hfCnNjPsIecnByrIwAA/MB8ba7DJ6R/r5W27L/8tXOFUnau8fXldql+Lem2JKlrS+sLBnzmQ1l25UjvrZH25V7+2rlCaWeO8ZWx1Sjw3tVVat0g+DkvFmpjmrnaPBTATDTixsc04sZHVVhYoA9WvqRn3hmutk06q0l0W6ujhRyn9PW5Qmn+19LXO/3/me0/SNMWGx+K+new9kORU/oZAADASqt2GEWCswX+nf/jSemtldKGvdKI3lJEeGDzlYfPfLiU1yt9uEFa9q3k8/Nn9uVKf1kipXSQfnKNtascGdMoCQtvA8DjCdPAa3+tQm+Bdny/3uo4Ic3OfX2uUHotvWLFr/MKvdLCb6T/fGMsObaanfsZ9hMfH291BACAH5ivzbH0W2nuKv+LXxfblC29/KmUf9b8XJXBZz5IRvHr7S+NsV3RSxGfpGWZRoHX6w1EuooJhTHNXG0eCmABcK7grD5cOUuS1DQ61uI0oc3OfT3/aynrYOmvzxhhfJVlaabxG0Wr2bmfYT/vvvuu1REAAH5gvr5ym/YZv7QsS3mf+fblSm9+YY9fevKZD5L0383S2t1ln1PeuF63R/p4k6mxKiUUxjRztXm4BdJE73z2tOZnTFP+mRPyeMI1Ycjf1LpxoiRp8dev69O1bxadeyB3pzq16qs/Dn/bqriOVlZfT317uK7vPFw942+XJD0+e5B+0muMusXdHLR8W/ZXbuVXSd5fK7VvZOwVEWx272fY06RJk/Tkk09aHQMAUA7m6ytz8ow072tz2vr2e+Oz47VtzGmvoriOwXnZudKSzea09ekWqVNTY5P8YAulMc1cbR5brwDzer2aNm2a2rVrp4iICCUlJSkjI0NxcXEaPXq01fEuM/yGiXp/ylEteOKwerS/VRu2Lyt6bWCPUXohNV0vpKZr4oi5iqhWSyMHPG1h2uIKCqU1uy78+dWl0rrdxnE7KquvU++codn/fUz5Z/L0+ab3VCuiblCLMj6ftHCdee2dKZD+a9FvT+zcz/44dUZK33rhz/9YYazKs8NvWEPZ/PnzrY4AAPAD8/WVSc+UTpw2r72P1hvbYFjBydcxXq+xEu9vGdLzi4xbSr/cbnyGRsV9uF7ymvRZ2esz2rOCk8f0pZirzWPrAtioUaM0ZcoUPfDAA1q8eLGGDh2qYcOGaefOneratavV8UpVu2Z9TRjyN3219SOt3PxBsde8Xq+emTNCowY+o5ioltYEvMTRU9Jzi4z7tM/bekD65xfSCx9Lx/Oty1aekvq6fmQD3dXnIb38wYN657On9Js7pgc10/ZD0g8lPO3xSqzdLZ2ycG8IO/ZzefYclqYsNFbQnbd+rzTzM+n15fYt7gIAAPsrKJS+NHmbiuOnjUKOlZx2HZN/VvrLp8Znuy3Z0v4fjQdK/esr6Zn/lPwEdpQu54RxHWim7w5KORb+d3DamEZg2bYANmfOHM2ePVsLFy7UI488ov79+2vixInq1auXCgoK1KVLF6sjlqlOzSjd3XeC3vj4T/JetPvfm0smq1VMJ13XcZB14S7i9UqvLrt8Ujpf9D941NjI3c4rZkrq61u636fsnCwNuu5B1akZFdQ8G/ea3+a5Qmnr9+a3WxF26+eynMiXXlkqnb6kaHh+HG/Olt5be/nPAQAA+GNnjpRn4uqv8zZYXACTnHMdIxl7p+3KMb4/f7ly/p/H8o1ffFbm4QRVVaAKsBstHtdOGtMILNsWwKZOnaoBAwYoOTm52PG2bdsqPDxciYnG/buTJk1SbGys3G63FixYYEXUUt3V9yHlHj+gJWv/KUlat+0zrc36RL++7TmLk12w9YB04GjpT/fwydiYc/sPQQxVCZf2tSQ1vqqtJY+53ZfrrHYrwk79XJYvt0v558p+as2q7ebetoALMjIyrI4AAPAD83XlZYfw5z3JGdcx3/9o7J1WGp/PuNNlfQB+OR2qqtJ1jB3HdGmYq83j8vnst7YnOztbzZo10+uvv67777+/2GvDhg3T1q1b9c03xuNWVq5cqZiYGN1///0aO3asBg8e7Nd7uFwuv86b9ptlSmqTUqH8Jck9flCPvNpfU0ct9nt55YYd6Xrklf5X/N5luWn039X+unvl9pT+PASvt1Bblr2mpX9PDWgWs/r6vOfm3qdbr/2VOrbqU+65Zvb16Fk5qlE7uujP5T3psTTjLtmDcee6hfrPi3deQbILzOzrivSzFJxxfe+zmxTVOF4ud9k1/s9eH63Ny14LaJZQM378+HLP2bFjh9q0KXsX3+nT7XXLLACEGubrwLrhV6+pY8qvih270s98Pp9PLpdLf/5FmHzeK9+rIdSvY3rePVk9Bj1W5nWd11uovRv/qw+m3RbQLKFi2FNr1aBl8TutKjOuL72OydmzXu9M7HwFyS4wY1xXZkxLgRvX5c3XzNVlq0hJy5ZPgczOzpYkxcTEFDuen5+vjIwMDRw4sOhY7969g5qtst76dIpOnj6m5/91X9GxZlfHadzgV60LJalazbrln+Tz+XceJEnusGqOajcUVa9Zr9zil+Tn+EeFLVy40K8LLwCAtZivK8/jMf9z2flCjtsTrkITCmBmse11TI068vm8crk8pZ7jdntUrWa94IVyOHcAxrUkecKqB6TdyrLrmC4Nc7V5bLkCbPv27WrXrp2mT5+ucePGFR2fPHmynnjiCb388ssaM2ZMsZ9JSUmp0Aowf62ZKx3NNrVJv9VrKnW7J7Dv8e4aacV3Zd8qJknXx0t3mFO0L1Wo9PWk9/x7cMD536Zc+huS0iQ1k0b2q3yui4VKX5dmxn+lPUfK37vuF9dJXVoGNkuo2bp1a7nndOjQQZmZmWWe0759e7MiAQBKwHwdWPO/lr7Y5t+5FfnM53ZJ04YZ/7xSof55b1mm9EE5T153SbqmhfRL/25UqPKmf2x8hvZHRcZ186ukCQMqn+tioTiuy5uvmatLlpKSIklKT0/3+2dsuQKsdevWSkxM1NSpUxUVFaUmTZpowYIFWrRokSTZ+gmQTnNta+nz7/w7D/5pVC8wT85sVN/8NkNVr7bS7sNln1MjXOrULDh5AABAaGlcL3DtmlH8qgq6tpT+843kLeMXnj5JPcu+cwwXaVzf/wJYhdqtZ36bQGXYchN8t9ut+fPnKyEhQampqRo5cqSio6OVlpYmj8dTtAE+rlzTKOMvj7L0bCM15E4xv7W4ylnthqLOLaQm9Y3f+pXmtmuk8NJXzOMKTJ482eoIAAA/MF9XXvPo8s+pVLt83vNbnRrSDfFlnxMbI7WLKfscXBCo8ce4vjLM1eaxZQFMkmJjY7Vs2TKdPHlSe/fu1ZQpU7Rp0ybFx8erRo0aVscLKcN6GitmLi0WuFxSn3bSkB6WxHKsrq3Mb7NODeMvcPinWpiUer3UruHlr4V7pLu6Sn1ig5+rqhg6dKjVEQAAfmC+rrym9aWYAPyCuDt3XVTIwCTp5o4lr5q7prk0KpkVdRWR1Mz8XxCHeaSk5ua2WdUwV5vHlrdAlmbNmjXq2bNnsWOPPfaY/v73vysnJ0ebNm3SuHHjlJGRUe5TEnBBmEf62bXGXx7f7JHyTku1axiraOrVtDqd8zSsI8U1kr47YF6bvdtJHtuWq+0pMkIac6PxiOyN2dLZAik60tjzKyLc6nShzZ99CgAA1mO+rjyXS+obK81fbV6bTetLLQO0sixUuV3SrUlSvzhp7W7p32uN4xPvkK6ubWk0R6pZ3bg7aNUO89rs1lKqZa898B2Hudo8jimA5eXlKSsr67LN76dMmaIpU6ZYlKpiDh/brxfn/0onTx+Ty+VWXLPuSr3DPo8rrV/L2Ozezl5f9Edt2f2FElpep6ZXx2nusmc07u6/KqFlb02Y2U+7Dm7SK+PXq0l0W+WfydPv/3qjmlzVVv83/K2g5vxpV+n5RVKB98rbiq4tXd/hytupqNL6ukH95npu7i/kkkvRdZvqD8PelMft0aNv3K68/KOakbYi+GHL0Li+8QUAAGCmnm2NQsG+3CtvyyVpcA+jsBZMpX3eS2qTrLVZSzR36TPy+rx64CcvqNnVcZZ9ti5PZISU3P5CAYziV+Xddo20KVs6eebK26pVTbot6crbMcvhY/s1L/159e88TK8sHG/La3IElmPWlERGRqqwsFC//e1vrY5SaWuzluiGLvfq+QeWakbaCh3NO6RdBzZZHcsxdh3crJOnj+vFMct1/NQRnT57UkOSf6ekNsnyuMM0+b731bfThaeA1qgeqYkj5lqStWFd6Y4uZZ8z7u3yn5ricUvDexq39AVTWX0dGVFPT438UC+OWa6YqFb6eqvxcIqn7v8wuCEBAAAs5HFLw3tJ1cv5nObPZ76bOgZ/9VdZn/fOnMvXR6te1bOjl+iF1HTFNu1q6WdrBE/tCOPuoPL4M66HXmvcWWQXa7OWqGvsTWpYrwXX5FWUY1aAOcmGHel64h93qXWjJB3M3aU2ja/RkyM/0MadGRo76C+qFh4hSfK4w+V2swu3vzbvWqFusTdLkrq0u0nHT114RInL5VL92iVs+GShfnHG7aSfbK7cz3vc0n19pNYNzM3lj7L6unbNC8upwjzhcrsYwyju/COJAQD2xnx95RrVk36dIr2WLp0pqFwbvdtJAy14xldZn/e+3fOlXC63/vS3gapfu6EeuvsV1ahWK/ghYYnEZtLQHtL8r40naVbGkO7W7v1V0jV57ZpRGjvoL6pRPbLoPCdckzNXm8cxK8CcpFOrfopr1kMvpKYrsXWyHvzpTPl8Pp0+e7Lof7ad32/UsZM5atHQ5vcc2siJU7n6xyeP6+FZKXrns6d14pQJ680D7NYk4zeDFd1z6ura0tgbpU7NApOrPP709eFj32tt1pKiD07AebNmzbI6AgDAD8zX5mjbUHroZuMJ1BUR7pF+2s0oFAT71kep7M97P574QbknDmjqrxYroUVvffTlq8EPCEv1bmcUd+tWcAVXnRrGz11n8QOnSromv/h6XHLONTlztXlYAXYFco8f1NNv31PsWFTtGN034Ck1ijIe4ZJzbJ+i6zbR9v3r1bqxcQP08VO5eun9sXr03nlBz+xktWtG6Ze3PKneCXdo1bcfKudYttWR/NKjtfEExyWbpTW7yv7tYN0a0nXtpJQOwb/t8WLl9fXZgjN6/l+/1IQhr8njYRpBcampqfxFDQAOwHxtnsb1pQkDpM+/kz7Pko7klX5umEfq0sK47dHKvarK+rxXK6KuOrbsI4/bo2vaXq/5GdOsCwrLxDeR/nCb9Om30lc7yt4XrGY1qWcb6cYEYzN9qx3I3VnsmvxoXk7R9bjkrGty5mrzcOV6BaLqxOiF1PTLjq/c/IFaxCSo0Fsol8tYZLdu2xJ1bXeTCgsL9OycezX69mmKqhMT5MTO1rFVH32yerZ6J9yhDTvSFRPVSh63M4ZwvZrSkB7STzpLWw9I+45IOSekgkKj0NW4vtT8KqldQ3s87bG8vp6xYLTu6J1m+9+WwBrp6elWRwAA+IH52lwet/FLzH7tpZ2HpD2Hpf0/Suv2GK/37yA1izKeFm6Hp+KV9Xkvrll3Lfrqr5KkHd+vV0xUKyujwkI1q0t3dDZu0/3ugPHQh4NHpQ37jNdv6WSM69gYa3+Bf6k9B7cUuyY/fz0uyXHX5MzV5rHREA0du3/YovgWvXSu4IyO5h3SkeMHlJW9RoP7Paz0Df9S1r7Veu2j30uSRg18RvEte1mc2BlaxXRUmCdcD89KUXyLXoqoVkuF3gvLqaa8OVSbd6/Q/sPb9LOU36t3xzstTFuyiHDpmubGl52V1dff7v5SKza/px9+3KP3Pp+hu/o8pD6d7rI4MQAAgD24XcZtkW3/tz3t+QLYneU8ICnYyvq8Vy/yaiW2TtaEmf1UPbym/jjiHYvTwmrhHqljU+NLurABvhX71/nj0mvydds+1eB+D0uSMjbO55q8iqIAFgDDb/hT0fevPWw8UaJvp7vldrt1fedhur7zMKuiOd6oW58p+n75xgWau+xZNYlup6Q2yXrs58WXr+afydOzc+5VXLPuwY4ZEsrq64VPnbjs/EffuF1RdRoFMyIAAACuQFmf9+7uN1539xtf9DqfreEkl16TZ2yYJ7fbuNWGa/KqiwJYkCQnDbU6QsjplzhY/RIHl/p6jeqRmpG2IoiJQld5fS1JT93/YZDSwO4yMzOtjgAA8APzNS7GZ2uEMidfjzNXm4cCWDlqN6ia720F+jp46GsE0rx58zR0qHM/ZABAVcF8Hdr4vIdQVBXHNXO1eSiAlSPueqsTVB30dfDQ1wikxx9/nL+kAcABmK9DG5/3EIqq4rhmrjaPDZ43BwAAAAAAAAQOBTAAAAAAAACENApgAABTzZw50+oIAAA/MF8DgP0xV5uHAhgAwFQJCQlWRwAA+IH5GgDsj7naPBTAAACmSk5OtjoCAMAPzNcAYH/M1eahAAYAAAAAAICQRgEMAGCq7t27Wx0BAOAH5msAsD/mavNQAAMAmGr16tVWRwAA+IH5GgDsj7naPBTAAAAAAAAAENIogAEAAAAAACCkUQADAJhqwYIFVkcAAPiB+RoA7I+52jwUwAAAAAAAABDSKIABAEw1ePBgqyMAAPzAfA0A9sdcbZ4wqwMAAABn+W6pdOJQ8N+3dgMp7vrgvy+qBsZ1cFjVzxJ9HUz0dfBUtb4GrgQFMAAAUCEnDklHs61OAZiLcR0c9HPw0NfBQ18DzsAtkAAAU6WlpVkdAQDgB+ZrALA/5mrzUAADAJhq7NixVkcAAPiB+RoA7I+52jzcAgkAMFW/fv20fPlyq2OEpEKvdOCotC9XOnrKOFarutS0vtQ0SqrG3+pwoLMFUnaulP2jdPKMcaxeTalZlNSonuTh17UBw3yNUHDitLTviPT90QvH1u+Rml0lRdWSXC7LogGmYK42Dx+VAQCmysnJsTpCyDmWL634Tvpyh5R3uuRzqoVJ3VpJ/eKkmLrBzQdUxsFjUsZWae1uowhWktoRUq+2Up9YqU6NoMarEpiv4VQ+n/TtfunzLGnrgctfn73C+GfTKKlPO+PvxzBPcDMCZmGuNg8FMAAAbMrnk77eKf17rXT6XNnnni2QVm6TVm2Xbu4o3Zhg7Yf9h2elKHPPl/J4wuV2exRTv5WG3zBRyUlDrAsFWygolJZskZZslry+ss89cVr6ZLP0+XfST7sZF7FWruZgXAcPfR08TuvrY6ekeV9LW/aXf252rjT3K2l5ljS8p1EQs5LT+hoINSwqBwCYKj4+3uoIIcHrk+Z/Lc1ZVX7x69Kf+3iT9Ooy6UwFfi4QRtz4mP7zdJ7ee+KIbul+n555Z7j2H95ubShY6vQ5adZS6b+byi9+XSz/nPT2l9KC1RX7uUAIpXFt9/k6lPra7pzS19//KD2/yL/i16U/N/1jacPewOSqCKf0NezD7nO1k1AAAwCY6t1337U6Qkh4f6208go+D2/7QXp9ubFvmNU8njANvPbXKvQWaMf3662OA4sUeqXXM6QdhyrfxhfbpIXrzMt0JUJhXDtlvg6FvnYKO/f14RPSy59JeWcq9/OFPukfK6TM783NVVl27mvYi1PmaiegAAYAMNWkSZOsjuB43+6Xln9X9jkzRhhfZck6KC3LNC9XZZ0rOKsPV86SJDWNjrU4Dazy2bdGYbYs/ozr9K32uIANhXHtlPk6FPraKeza116vsQr0ZDnFr/LmEK9PesePdoLBrn0N+3HKXO0Ett4DzOv16sUXX9Srr76qffv2KS4uTn/+8581evRoJScn669//avVEQEAl5g/f76efPJJq2M4VkGh9K+vzGtv8Uapcwvpqkjz2vTXO589rfkZ05R/5oQ8nnBNGPI3tW6caOT6+nV9uvbNonMP5O5Up1Z99cfhbwc/aBly86S9ucb3La6S6teyNo9THT5h3PZoln99JT16hzX73IXCuD7P7vO10/va5zOe2nvemQKpuk2vvsrq66lvD9f1nYerZ/ztkqTHZw/ST3qNUbe4m4OW78sd0i6T9gE/cVr6zzfSPT3Naa+i7N7X5Sn0Fl/JW+jlab2BZve52klsOgUbRo0apffee0+PPfaYunbtqpUrV2rYsGHKycnRhAkTrI4HAIDp1u81nvpolkKv9EWWdEcX89r01/AbJmrEjY/qxKkf9cL8UdqwfZkG9hglSRrYY1TR97nHD+qRV/tr5ICngx+yFEdPGXuwfbtfOr/llEtSQlNpSHepbk0r0znPiixzb8c9ekrauE/q0tK8Nv3l5HHtNE7u6+8OGA8wOXjswrHH3pX6xkq3JtmvYFBWX6feOUN/fO0WJbVJ0ZqsT1Qrom5QCzI+n/HEWDOt3iXdfo0UGWFuu/6wc1+X58vt0scbi39Omfy+NKCT1LudZbEAv9ls6r1gzpw5mj17thYuXKhHHnlE/fv318SJE9WrVy8VFBSoSxcLPskDABBgq3aY3+ZXO43bR6xSu2Z9TRjyN3219SOt3PxBsde8Xq+emTNCowY+o5ioltYEvMSxfGnGf4sXvyTj+y3ZxmsnTCxShrpCr/E0U7MF4v+VinDauHYyp/V15vfGg0h+OFb8+NkC41bgN7+w/mEOpSmpr+tHNtBdfR7Syx88qHc+e0q/uWN6UDPtypEOHTe3zUKvtGaXuW1WlB37uixLvzVW3176S7rj+cZTOT/dYk0uoCJsWwCbOnWqBgwYoOTk5GLH27Ztq/DwcCUmJurHH3/U7bffrtjYWCUlJenmm2/W9u08QQMArJSRkWF1BMcq9Ep7Dpvf7skz0qET5rdbEXVqRunuvhP0xsd/kveiatybSyarVUwnXddxkHXhLvHfjdKxU8WLX+f5ZKw++u/mYKdyrkPHpVNnzW9392FrC7uSs8Z1SZw0Xzulr71eae4qY9VSaTWu9XulrTbYx640JfX1Ld3vU3ZOlgZd96Dq1IwKap6dJt36eKldAfj7tqLs1telOXZK+s/6ss/5aIPx9yPM56S52u5sWQDLzs7W5s2bNWTIkMte27t3rxISElS9enW5XC6NGzdOWVlZ2rBhg26//XaNHDnSgsQAgPO2bOFXgJV16Lh0rjAwbe/PLf+cQLur70PKPX5AS9b+U5K0bttnWpv1iX5923MWJ7vgzDnj1piyFmf4ZKxoOlMQrFTOlh2gsXe2QMqxuLArOWNcl8Zp87UT+jrzgLFCpqw5xOUynmhqZ5f2tSQ1vqqtmkS3DXqW/T8Gpt1AzU0VZae+Ls2qHUZRtyw+n7SKtSgB4bS52s5cPl95Qzn4Vq1apV69eumjjz7SrbfeWnQ8Pz9fbdq00cCBA/X6669f9nNr1qzRoEGDlJ2dXe57uFwuUzMDQFUwfvz4cs+ZPn16uedNn26fJf120jiuj4Y89nmxY+U9Ea804y7ZB3rZP8Zq45KXK5msuGm/WaakNilX1Mb5PXumjlrs921LG3ak65FX+l/R+5YnqnEH/fy5b/0695+/a68fD5TzuE7omlseVPLP/1+xY2aN63mTe+vAti8rmay4UBvXdp2vzehnyV59fbEutz2ivsOeL/e8Yzm7NHt864BmMauvz3tu7n269dpfqWOrPuWea2Zf3/WHT9S8003Fjpkxh5w++aNefcCcFVah0telGZA2R+16DJbbU/oW4t7CAm1f854W/+VnAc0SivyZh/lsXbqKlLRsuQl+dHS0JCkrK6tYAey5557TgQMH1LVr1xJ/bsaMGRo0aFAwIgIAYDqfN0DLvwLcdmW89ekUnTx9TM//676iY82ujtO4wa9aF0pSYaH/9+oVFgTgvr4Q5A3g2Atk25Vh13Ediuza114/5wVvwbkAJwkdXl9g/j+329+Ldub/uObvRdibLVeAeb1ede7cWQcOHNC0adPUpEkTLViwQIsWLdLevXu1atUqXXvttcV+ZvLkyVq8eLGWLl2qmjV5NBMABMLWreU/hqlDhw7KzMws85z27dubFSmkHD0lPfFv/849/9vvS1fElGZ0ihTfpFKxLrNmrnS0/MXWpqvXVOp2T2Dfw+uTnl4o5eaVfguTS9JVtaWJPzFuZULZNmdLf/Nz+5KKjusnfyrVqVG5XJcKtXFt1/naqn6WgjOH/HBMeubD8s9Lbi/dVfLv9E0TKn097ytppZ+31lVkDmkWJT08sPK5LhYqfV2atbukN1eWf969vaVurQKbJRSVN1/z2bpkKSkpkqT09HS/f8aWe4C53W7Nnz9fCQkJSk1N1ciRIxUdHa20tDR5PB4lJiYWO/+pp57Shx9+qI8//pjiFwBYbPLkyVZHcKy6NaTaAXokezN77KNre26XcWFa3h5gKe0pfvkrUGOvbg3zil9VFfO1+RrWleJijEJ5adwuqU+7oEVyvGZXBabdpvy96Lek5lKdiNLHtUtSZIR0TfNgpqo6mKvNY8sCmCTFxsZq2bJlOnnypPbu3aspU6Zo06ZNio+PV40aFz7tTJ48Wf/5z3+0ZMkS1atXz7rAAABJ0tChQ62O4Fgul9S+sfntNqkv1aZQ4Lc+sVKP/23Nc3GR6/z317aRenPx6re6NaVG9cxvt0MA/l+papivA+Pe3tLVdS4/7pJR/CrtdZSsvIJiZTGH+C/MI/26vxQRXvLrEeHGSvMwT1BjVRnM1eaxbQGsJGvWrCm2/9eWLVv0xBNP6MiRI0pJSdE111yja665xrqAAAB16NDB6giOFohVAX1izW8zlLld0rCe0i/7SK2iLxxvFS3d10e651rjHPgvEOP6OoqQV4z5OjBq15DG3yLd0Vm6qpZRvKkWJnVrLU0YIHVpaXVCZ4mKNO8W/vPq1pASTG4z1DWLkn5/m3R9B6lWdeNYrepS/w7G8eYBWqkH5moz2XIT/JLk5eUpKytLY8aMKTqWkJBQoR3/AQCwu+ZXGb+VzvzenPaiI6WuLc1pqypxuaTOLYyv83vJPHiztZmcrFtraWmmdCTPnPYSmgTutijADDWqSdfHG18+H7dMX6mbO0rf7i/79vQKtddJ8jhqKYg91K8l3dHF+GJcw4kcUwCLjIxUYSFP6gAAhDaXS/rZtdKzH0qnr/AhYS5Jw3oZKw+C7fVFf9SW3V8ooeV1anp1nOYue0bj7v6rWsYkaNLf75DHE65aEXX16L3/ktdbqN//9UY1uaqt/m/4W8EPi4CrHibd01N6+dMrb6tGuDS0x5W3Y6bDx/ZrXvrz6t95mF5ZOF4ul1txzbor9Y6q+1j6YDh8bL9enP8rnTx9zNZ9buciQWlzda2Iupq1cJwk6dCPe3RX34f0077j9Ogbtysv/6hmpK0Ias4W0VL/eGnpt1feVruGUu+2V95ORZXW151a9dVzc3+hQ0f3KsxTTRPvnatqYRG2/3vRzuMaKA11bwCAqc4/kQWVV6+mdF/fsn87Pe7t8p9yNair1KaBudn8sevgZp08fVwvjlmu46eO6PTZkxqS/DsltUlWZI36mj5mhV5MzVBsk65a9e2HqlE9UhNHzA1+UARVu4bSnV3KPqe8ce1xG/9v1LXZM4/WZi1R19ib1LBeCz3/wFLNSFuho3mHtOvAJqujlcnp8/XarCW6ocu9jupzOylrrm7b5Bq9kJquF1LT1apRoq7tcLsk6an7/XjEZYDcmii1b1T2OeXNIVfXln5xXfCLN2X19Y7v1yssrJpeHLNct3Qfqc/Wvc3fiyjG6XO1nThmBRgAwBlmzZpldYSQ0L6RsaHsP1ZIp85W7Gc9bumurtbt/bV51wp1izXuF+zS7iYdP3XkomwXdsgt9BWqSTQbOVUl/TtIYW7p/bVSYQXvZapVTfplXyk2JjDZ/LVhR7qe+Mddat0oSQdzd6lN42tUu2aUxg76i2pUjyw6z+MOl9tt7x2hnTJfl9TnT478QBt3ZmjsoL+oWrjx+Fwn9LmdlDVXn5d/9qR+PHFQTaItWDJ1iTCPNCpZeudL6Zs9Ff/5ZlHSr1KseShMWX0dXbeJvF7jTqe8/KOqU5P7u1GcU+ZqJ2AFGADAVKmpqVZHCBlxjaT/u11KbOb/zzS/SnpkoLUb3584lat/fPK4Hp6Vonc+e1onTuUWe33r3q815v910/rtS9UoqpVFKWGVvnHSwwMrtmFyUjPj/wWri1+S1KlVP8U166EXUtOV2DpZD/50pk6fPVms+LXz+406djJHLRrGW5i0fE6Zr0vqc5/PV6zfndLndlLeXC1Jq7cuVre4ARakK1m4x1jB9YvrpMjq/v1MmEe6NUkad4ux+b0VyurrOrWideZcvu5/voM+/HKW+nT6qTUhYVtOmaudgBVgAABTpaenWx0hpNSpId3fTzp4TPpim5R1QDp0vPhGwPVrSW2ulnq1k1pfbf2+HLVrRumXtzyp3gl3aNW3HyrnWHax19s376GZD63R/IwX9PHqN3R3v/EWJYVVGtc3npK385C0cru0M0f68eSF112SGtSRYhsZT3uMqWtZ1MscyN2pRlGtJUk5x/bpaF6OWjdOKnr9+KlcvfT+WD167zyrIvrNbvN17vGDevrte4odi6odo/sGPFWsz6PrNtH2/euL+t1JfW4n5c3VkvTF5n9raMrvLUhXOpfLeJJmp2bS+j3Sml3S3lwp/6LV0uEeY565prnUo/WFpxZapay+Xpv1ierWulpv/C5Tyzcu0PyMafr5TZMsTAu7sdtc7WQUwAAAcICYutLd3Yzvz5yT/vC/67ynB1v/wf5SHVv10SerZ6t3wh3asCNdMVGt5HEbHznOFZxVeFg1SVKtiDoq9PKAm6rK5ZLaNDS+JOnkGWniAuP7Z4dK1cOty1aWPQe3qEVMggq9hXK53Fq3bYm6trtJklRYWKBn59yr0bdPU1QdGyxXc5ioOjF6ITX9suMrN39QrM8lFfU7fV55Zc3VklRQeE57D2WqzUUFXjsJ90jdWxtfPp90LN/4+zHMY+ylaaenPJbV1z6fT3VqRkmS6taK1snTx6yMCoQ0G00LAADAHxcXBuxW/JKkVjEdFeYJ18OzUhTmCVdEtVpFr+34fr0mzErWI6/01+qtH+umrr+wMCns5OKxbNfilyTt/mGLWjZM0LmCMzqad0jrtn2q2KZGdTpj43xl7Vut1z76vR6elaJvd39pcdrQcGmfHzl+QFnZaxTbtBt9fgXKmqsl6ZvtS3VNm+stSlcxLpdR9GpYV7oq0l7FL6nsvu4We7P2HsrUw7NS9I//TtIdvcZYmBQIbawAAwCYKjMz0+oIsIFRtz5T9P3yjQs0d9mzahLdTkltkvViakaxc/PP5OnZOfcqrln3YMcEKmz4DX8q+v61hzcpY8M8ud3G1fb1nYfp+s7DrIpWYU6Zry/tc0nq2+luud1ux/W53ZQ1V3ePu0Xd424pdv6jb9yuqDrlPIoRJSqrryf9YkGxc/l7ERdzylztBBTAAACmmjdvnoYOHWp1DNhIv8TB6pc4uNTXa1SP1Iy0FUFMBJgnOcm5852T52sn97tdlTdXS9JT938YpDShjb8XURFOnqvthgIYAMBUjz/+OH9Jh7jaDarW+6JqqIrj2or52sp/36o2h9DXwUNfI5D4bG0eCmAAAKBC4pyxJQx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       "text/plain": [
        "<Figure size 1591.6x565.88 with 1 Axes>"
       ]
@@ -330,7 +330,7 @@
     "\n",
     "\n",
     "circuit = conv_layer(4, \"θ\")\n",
-    "circuit.decompose().draw(\"mpl\")"
+    "circuit.decompose().draw(\"mpl\", style=\"clifford\")"
    ]
   },
   {
@@ -373,7 +373,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 387.452x144.48 with 1 Axes>"
       ]
@@ -398,7 +398,7 @@
     "\n",
     "params = ParameterVector(\"θ\", length=3)\n",
     "circuit = pool_circuit(params)\n",
-    "circuit.draw(\"mpl\")"
+    "circuit.draw(\"mpl\", style=\"clifford\")"
    ]
   },
   {
@@ -421,7 +421,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 808.997x264.88 with 1 Axes>"
       ]
@@ -452,7 +452,7 @@
     "sources = [0, 1]\n",
     "sinks = [2, 3]\n",
     "circuit = pool_layer(sources, sinks, \"θ\")\n",
-    "circuit.decompose().draw(\"mpl\")"
+    "circuit.decompose().draw(\"mpl\", style=\"clifford\")"
    ]
   },
   {
@@ -540,7 +540,7 @@
    "id": "a5b3ca82",
    "metadata": {},
    "source": [
-    "Let's now create our dataset below and split it into our test and training datasets."
+    "Let's now create our dataset below and split it into our test and training datasets. We pass a `random_state` so the split will be the same each time this notebook is run so the final results do not vary."
    ]
   },
   {
@@ -555,7 +555,7 @@
     "images, labels = generate_dataset(50)\n",
     "\n",
     "train_images, test_images, train_labels, test_labels = train_test_split(\n",
-    "    images, labels, test_size=0.3\n",
+    "    images, labels, test_size=0.3, random_state=246\n",
     ")"
    ]
   },
@@ -577,7 +577,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 720x432 with 4 Axes>"
       ]
@@ -636,7 +636,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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r+phCWloaFouFrKwsYmNjKS0tJSUlhdraWpYsWaJ2eW4VZScwaNQkxiZnejSuNbsO57Dbmo+PqScDQobx+tz3CA8ZpnZZbkmvO4dmQ6GwsJCCggJKSkqIj48HIDExkfLyciwWC2PGjFG5Qn2a/dgKUidp+z8mvdBqrzW7+5Cbm0tSUpIrEG4bNmwYPj4+REdHc/XqVaZNm0ZkZCQxMTFMnjyZs2fPqlSxEPqgyVCorq6msrKS5557rt17VVVVREVF4evri8Fg4OWXX8Zms/HXv/6VadOmMW/ePBUqFkI/NBsKAKGhoYrxxsZGrFara9chKCiISZMmud6Pi4vj/PnzHi3DYDB49LJaS7yu//jeHDamBylel22feD0fq7XE4zo7Wmtn6or6pde3eFK/pzR5TCEkJAQAm83G1KlTXeN5eXnU1NQQGxvr9nPr1q0jOTm5K0q8o7HTV7g9+KV1b2WUqF2C16TX954mQyEiIoLo6Ghyc3MJDg4mPDycoqIiDhw4AOA2FFatWsXZs2c5cuSIR8vw9GHbb3+k3uW88fEJFGV7/lDwsne1dTlvfHwCbRs9r1963XHe9vpONLn7YDQa2b17N1FRUWRkZDBv3jxCQkJYsGABJpOJ6OhoxfTZ2dns27ePgwcP0qtXL5WqFkIfNLmlABAZGUlxcbFibM6cOYwcORI/Pz/X2KpVqzhw4AAfffQRQUFBXVylEPqj2VBwp6ysjHHjxrn+PnXqFCtXrmTo0KEkJCS4xk+cONH1xf2nZzNLvBoXHSe97hzdJhTsdjs2m4358+e7xqKiojw+NiCE8Iwmjym44+/vj8PhYNGiRWqX0i2crvqMxevjeHnDBDa+/4riva+vX2bZpoksXh9Hue0QbW1t1DdeZ/uHK3E4HTQ01bmd5/SsPrxz4DUA1hW9xOL143l5wwTOXT4JwJ5P1zNjVSiXvr6/TiDTW6+7TSgI7/QPGsybLx1h3YJPuGa/orj67g/Fa3nh8dWs/fmH7DycTavjJvuObebUhVJ2HlqNvema23kOCR1N2tQ1AMyc+Cr/c+Gn/GrGNnZ8tAqA5PELeXhEUqevm9bordcSCjoVHBhKTx8zACbjrRt53Hb+qwqiHojDz9efXr4BtLQ2tTu5JWdnCmcvneB8TQVv7Gh/ZmlY8BAAepiU874f6a3X3eaYguiYc5dPcr2+lsH9R7rGnE6H64vZ29wHe+NVnngknaaWelInZdHc0sD8J9eRu2s2RoOJZTML/uH83/nX13hqwi87ezW6Bb30WkJBx240fMv6PQvJfP6PinGD4bsNxPrmG/j79aW3OZC5k1cC0MscQC9zAGHBERgNRkL6DHA7f8vH6xjcbySjhkzotHXoLvTUa9l90CmHo5W1hc+TPi2f4EDlNSQRYdF8fuEojS31NDTdoLc5sN3nz10+SWNzHdfsV7h45Uy798vOfMipC6WavPS3q+mt17KloFPWk7uxXfwzW/YvByBtyhqOnNjFwuS3mZGwnLx359J8s5G5k1e1+6zD6WDzvqX8etYOWh0trLP8gpwX9yum2bB3Eb18A/nVpkT+yz+N4OVnN3fJemmR3nptaJMf+u9IzfPxh/aDRT/1fPrOPh//xbwHGT/qKddR8R/a8+l69h/dRHbafvr3HUzQQHh4lufzl15/p7N7fSeypSA8tnX5F3d8P3n8QpLHL+yiavRNzV5LKNxFeN/us+yAfp1TR0d5W4/0uuPuZT2y+yCEUJBfH4QQChIKQggFCQUhhIKEghBCQUJBCKEgoSCEUJBQEEIoSCgIIRQkFIQQChIKQggFCQUhhIKEghBCQUJBCKEgl07fhaUMLl1VZ9nhfeHphz2f/swRqFPpJiXuBPSDERM9n1563XHe9vpOJBTu4tJV9e4G5K26K9p6ErK3pNfaILsPQggFCQUhhIKEghBCQUJBCKEgBxqFy9KNCZz+8iim/3xmYVhwBKmPZfKT6GfULk13tNxr2VIQCqmTsvggx45l5TckPpRC9s6ZVNfa1C5Ll7TaawkF4ZbJ1IMn4+bjdDoUj1YX957Wei2hINy62drC3tIN9DD5EDEgRu1ydE1rvdZ0KDidTvLz8xk+fDhms5mYmBisVisjRowgPT1d7fLcKspO4PiebI/HtWbX4RySs4KYnT2Qo6f28vrc9wgPGaZ2WW5JrzuHpg80pqWlYbFYyMrKIjY2ltLSUlJSUqitrWXJkiVql6dLsx9bIU+S7iJa7bVmQ6GwsJCCggJKSkqIj48HIDExkfLyciwWC2PGjFG5QiH0SbO7D7m5uSQlJbkC4bZhw4bh4+NDdHQ0AMnJyURHR/PjH/+YsWPHcujQITXKFUI3NLmlUF1dTWVlJa+88kq796qqqoiKisLX1xeAgoICgoKCAPj3f/93EhIS+PbbbzGZTF1ZssLxvTn85UC+Yuxmk51BoyapVJF+Sa/vPU1uKVRX37r8LDQ0VDHe2NiI1WpV7DrcDgSA69evYzAY8OSZuQaDwaOX1Vridf1jp68g43fXFK8BkRO8no/VWuJxnR2t9fveyii5p/u4XVG/9PoWT+r3lCZDISQkBACbTXkiR15eHjU1NcTGxirGFyxYQEREBM888wzvvfcePXpocgNIiG5Bk6EQERFBdHQ0ubm5bN++ncOHD5ORkcHWrVsB2oXChg0bOHfuHBaLhWXLlmG32++6jLa2No9e8fEJnbGKHomPT/C4TrVrdac71d+danXHk/o9pclQMBqN7N69m6ioKDIyMpg3bx4hISEsWLAAk8nkOsj4Q/Hx8RiNRj799NMurlgI/dDsdnZkZCTFxcWKsTlz5jBy5Ej8/PwAsNvtfPPNNwwePBi4daDxb3/7Gz/60Y+6vN7bns0s8WpcdJz0unNoNhTcKSsrY9y4ca6/6+vrmTlzJna7nR49emA2m/n973/PoEGDVKxSiO5Nk7sP7tjtdmw2m+KXh/79+3Ps2DEqKys5ceIEx44d44knnlCxSu04XfUZi9fH8fKGCWx8X/nT7tfXL7Ns00QWr4+j3HaItrY26huvs/3DlTicDhqa6tzOc3pWH9458BoAG/YuZsnGeBb9yyNUnr+1u7btYCbJWUE4HK2du3Iao7ded5stBX9/fxwOh9pldBv9gwbz5ktH6OljZs2uVM7XVDAkbDQAfyheywuPr2bogBgyt05jdMSj7Du2mVMXStl5aDVJY9PoZQ5oN88hoaNJm7oGgJem5dPD5MPfr37Jv1jmk5O2n3lJ2VSe/6RL11ML9NbrbrOlILwTHBhKTx8zACbjrRt53Hb+qwqiHojDz9efXr4BtLQ2tfsdO2dnCmcvneB8TQVv7Hiu3fx7mHwAaGy2a+LKPjXprdfdZktBdMy5yye5Xl/L4P4jXWNOp8P1xext7oO98SpPPJJOU0s9qZOyaG5pYP6T68jdNRujwcSymQVu572y4Cm+uHicX6fs6IpV0Ty99FpCQcduNHzL+j0LyXz+j4pxg+G7DcT65hv4+/WltzmQuZNXAtDLHEAvcwBhwREYDUZC+gxwO/+VP/sTtdeqeWPHs7y96FinrUd3oKdey+6DTjkcrawtfJ70afkEBypPF48Ii+bzC0dpbKmnoekGvc2B7T5/7vJJGpvruGa/wsUrZ9q939LaDICfrz/mnr07ZyW6Cb31WrYUdMp6cje2i39my/7lAKRNWcORE7tYmPw2MxKWk/fuXJpvNjJ38qp2n3U4HWzet5Rfz9pBq6OFdZZfkPPifsU0Ob+fib3xGs42B2lT1nTJOmmV3nptaPPm/Mf70Nsfqfcos6H9YNFPPZ++7N3OfZTZi3kPMn7UU66j4j+07WAmH58sYsuvTmEymggaCA/P8nz+0uvvdHav70RC4S7ki9pxEgpd516Gguw+3EV43+6z7IB+nVNHR3lbj/S64+5lPbKlIIRQkF8fhBAKEgpCCAUJBSGEgoSCEEJBQkEIoSChIIRQkFAQQihIKAghFCQUhBAKEgpCCAUJBSGEgoSCEEJBQkEIoSCXTt+FpQwuXVVn2eF94emHPZ/+zBGoU+l+BO4E9IMREz2fXnrdcd72+k4kFO7i0lX1bvzhrbor2rrxh7ek19oguw9CCAUJBSGEgoSCEEJBQkEIoSAHGoXL0o0JnP7yKCbTrechhgVHkPpYJj+Jfkbt0nRHy72WLQWhkDopiw9y7FhWfkPiQylk75xJda1N7bJ0Sau9llAQbplMPXgybj5Op4PzNRVql6NrWuu1hIJw62ZrC3tLN9DD5HPfP2q+s2mt15oOBafTSX5+PsOHD8dsNhMTE4PVamXEiBGkp6erXZ4u7TqcQ3JWELOzB3L01F5en/se4SHD1C5Ll7Taa00faExLS8NisZCVlUVsbCylpaWkpKRQW1vLkiVL1C7PraLsBAaNmsTY5EyPxrVm9mMrSJ2k7Rpvk153Ds2GQmFhIQUFBZSUlBAfHw9AYmIi5eXlWCwWxowZo3KFQuiTZncfcnNzSUpKcgXCbcOGDcPHx4fo6GjF+O9+9zsMBgNFRUVdWaYQuqPJUKiurqayspLnnnuu3XtVVVVERUXh6+vrGvuP//gPtm3bxrhx47qyTCF0SbOhABAaGqoYb2xsxGq1KnYdWltbefHFF9m4caMiKO7GYDB49LJaS7yu//jeHDamBylel22feD0fq7XE4zo7Wuv3vZVRck/3cbuifun1LZ7U7ylNHlMICQkBwGazMXXqVNd4Xl4eNTU1xMbGusZWr17NlClTeOihh7q6zH9o7PQVbg9+iXtPen3vaTIUIiIiiI6OJjc3l+DgYMLDwykqKuLAgQMArlD47LPPOHLkCCUlJV4vo62tzaPp3v5IvWv84+MTKMr2rE6Asne1dY1/fHwCbRs9r1963XHe9vpONLn7YDQa2b17N1FRUWRkZDBv3jxCQkJYsGABJpPJdZCxuLiYv/3tbwwdOpQHHniAY8eOMX/+fN566y2V10CI7kuTWwoAkZGRFBcXK8bmzJnDyJEj8fPzA+DVV1/l1Vdfdb2fkJDAwoULefbZZ7u0ViH0RLOh4E5ZWZnmf2F4NrPEq3HRcdLrztFtQsFut2Oz2Zg/f/4/nKYjxxaEEEqaPKbgjr+/Pw6Hg0WLFqldSrdwuuozFq+P4+UNE9j4/iuK976+fpllmyayeH0c5bZDtLW1Ud94ne0frsThdNDQVOd2ntOz+vDOgddcfzffbGTGqlDKbYcA2HYwk+SsIByO1s5bMQ3SW6+7TSgI7/QPGsybLx1h3YJPuGa/orgk9w/Fa3nh8dWs/fmH7DycTavjJvuObebUhVJ2HlqNvema23kOCR1N2tQ1rr//9bP/zZCw0a6/5yVlM3TAQ521Spqlt15LKOhUcGAoPX3MAJiMt+7uc9v5ryqIeiAOP19/evkG0NLa1O7klpydKZy9dILzNRW8saP9maU3W1s4XXWMqAfGd+6KdAN663W3OaYgOubc5ZNcr69lcP+RrjGn0+H6YvY298HeeJUnHkmnqaWe1ElZNLc0MP/JdeTumo3RYGLZzIJ28/2wrIDHxjzPF1WfddWqaJ5eei1bCjp2o+Fb1u9ZyNLn3lGMGwzf/bPXN9/A368vvf36MHfySkxGE73MAfQN6E9YcARhwUMI6TNA8XmHo5WyM/+XsQ9O6ZL16A701GsJBZ1yOFpZW/g86dPyCQ5UXkMSERbN5xeO0thST0PTDXqbA9t9/tzlkzQ213HNfoWLV84o3rtq/ztXrlXx2pYkDpf/nnf+9TXqGlR63psG6K3XsvugU9aTu7Fd/DNb9i8HIG3KGo6c2MXC5LeZkbCcvHfn0nyzkbmTV7X7rMPpYPO+pfx61g5aHS2ss/yCnBf3u94P6RPOhsV/BmD7hysZ9cAEAnr17ZoV0yC99drQ5ulFAPcpNc/HH9oPFv3U8+k7+3z8F/MeZPyopxRHxb9v28FMPj5ZxJZfncJkNBE0EB6e5fn8pdff6exe34mEwl3IF7XjJBS6zr0MBdl9uItwFbeKvV12QL/OqaOjvK1Het1x97Ie2VIQQijIrw9CCAUJBSGEgoSCEEJBQkEIoSChIIRQkFAQQihIKAghFCQUhBAKEgpCCAUJBSGEgoSCEEJBQkEIoSChIIRQkEun78JSBpdUutNYeF94+mHPpz9zBOpUuh+BOwH9YMREz6eXXnect72+EwmFu7h0Vb0bf3ir7oq2bvzhLem1NsjugxBCQUJBCKEgoSCEUJBQEEIoyIFG4bJ0YwKnvzyKyXTreYhhwRGkPpbJT6KfUbs03dFyr2VLQSikTsrigxw7lpXfkPhQCtk7Z1Jda1O7LF3Saq8lFIRbJlMPnoybj9PpUDxaXdx7Wuu1hIJw62ZrC3tLN9DD5EPEgBi1y9E1rfVa06HgdDrJz89n+PDhmM1mYmJisFqtjBgxgvT0dLXLc6soO4Hje7I9HteaXYdzSM4KYnb2QI6e2svrc98jPGSY2mW5Jb3uHJo+0JiWlobFYiErK4vY2FhKS0tJSUmhtraWJUuWqF2eLs1+bAWpkzLVLuO+oNVeazYUCgsLKSgooKSkhPj4eAASExMpLy/HYrEwZswYlSsUQp80Gwq5ubkkJSW5AuG2YcOG4ePjQ3R0NAAJCQl8+eWX9OnTB4CkpCTWrl3b5fUKoReaDIXq6moqKyt55ZVX2r1XVVVFVFQUvr6+rrE333yTZ599titLvKPje3P4y4F8xdjNJjuDRk1SqSL9kl7fe5o80Fhdfevys9DQUMV4Y2MjVqv1nuw6GAwGj15Wa4nX8x47fQUZv7umeA2InOD1fKzWEo/r7Git3/dWRsk93cftivql17d4Ur+nNBkKISEhANhsyhM58vLyqKmpITY2VjG+YsUKRo8ezfTp0zl58mSX1SmEHmkyFCIiIoiOjiY3N5ft27dz+PBhMjIy2Lp1K4AiFLZv384XX3xBRUUFKSkpPP7449TX1991GW1tbR694uMTOms17yo+PsHjOtWu1Z3uVH93qtUdT+r3lCZDwWg0snv3bqKiosjIyGDevHmEhISwYMECTCaT6yAjwKBBg1ybRrNmzaJnz56cOXNGrdKF6PY0eaARIDIykuLiYsXYnDlzGDlyJH5+fgA0NTVht9tduxuHDx+mrq6OYcPUOwHk2cwSr8ZFx0mvO4dmQ8GdsrIyxo0b5/r7xo0bTJkyhZaWFoxGI4GBgbz//vsEBgaqWKUQ3Zsmdx/csdvt2Gw2xS8P/fr14y9/+QsVFRX89a9/5eOPP2bCBO+PPOvR6arPWLw+jpc3TGDj+8qfdr++fpllmyayeH0c5bZDtLW1Ud94ne0frsThdNDQVOd2ntOz+vDOgdcAyHv3Zyz6l0dYujGBI/++C4A9n65nxqpQLn19tnNXTmP01utus6Xg7++Pw+FQu4xuo3/QYN586Qg9fcys2ZXK+ZoKhoSNBuAPxWt54fHVDB0QQ+bWaYyOeJR9xzZz6kIpOw+tJmlsGr3MAe3mOSR0NGlT17j+fnX2TsW5+snjF2K7WNb5K6cxeut1t9lSEN4JDgylp48ZAJPx1o08bjv/VQVRD8Th5+tPL98AWlqb2v2OnbMzhbOXTnC+poI3djzXbv4Gg4G8d+eStfW/8ferX3buymic3nrdbbYURMecu3yS6/W1DO4/0jXmdDpcX8ze5j7YG6/yxCPpNLXUkzopi+aWBuY/uY7cXbMxGkwsm1nQbr4v/be3COwVTOX5T9j8wVJen1vUVaukWXrptWwp6NiNhm9Zv2chS597RzFuMHz3z17ffAN/v7709uvD3MkrMRlN9DIH0DegP2HBEYQFDyGkz4B28w7sFQzAqCET+Lbuq85dkW5AT72WUNAph6OVtYXPkz4tn+BA5eniEWHRfH7hKI0t9TQ03aC3uf2vNecun6SxuY5r9itcvNL+vI/6phsAXLxyBn+/oE5Zh+5Cb72W3Qedsp7cje3in9myfzkAaVPWcOTELhYmv82MhOXkvTuX5puNzJ28qt1nHU4Hm/ct5dezdtDqaGGd5RfkvLhfMc3aXanUNV7FYDDwy6c3dsk6aZXeem1o8+b8x/vQ2x+p9yizof1g0U89n77s3c59lNmLeQ8yftRTiqPi37fn0/XsP7qJ7LT99O87mKCB8PAsz+cvvf5OZ/f6TmRLQXhs6/Iv7vh+8viFJI9f2EXV6JuavZZQuIvwvt1n2QH9OqeOjvK2Hul1x93LemT3QQihIL8+CCEUJBSEEAoSCkIIBQkFIYSChIIQQkFCQQihIKEghFCQUBBCKEgoCCEUJBSEEAoSCkIIBQkFIYSChIIQQkEunb4LSxlcuqrOssP7wtMPez79mSNQp9JNStwJ6AcjJno+vfS647zt9Z1IKNzFpavq3Q3IW3VXOvduQJ1Neq0NsvsghFCQUBBCKEgoCCEUJBSEEApyoFG4LN2YwOkvj2Iy3XoeYlhwBKmPZfKT6GfULk13tNxr2VIQCqmTsvggx45l5TckPpRC9s6ZVNfa1C5Ll7TaawkF4ZbJ1IMn4+bjdDo4X1Ohdjm6prVeSygIt262trC3dAM9TD5EDIhRuxxd01qvNR0KTqeT/Px8hg8fjtlsJiYmBqvVyogRI0hPT1e7PF3adTiH5KwgZmcP5Oipvbw+9z3CQ4apXZYuabXXmj7QmJaWhsViISsri9jYWEpLS0lJSaG2tpYlS5aoXZ5bRdkJDBo1ibHJmR6Na83sx1aQOknbNd4mve4cmg2FwsJCCgoKKCkpIT4+HoDExETKy8uxWCyMGTNG5QqF0CfN7j7k5uaSlJTkCoTbhg0bho+PD9HR0QC0tLSwZMkShg8fzujRo3n00UfVKFcI3dDklkJ1dTWVlZW88sor7d6rqqoiKioKX19fAH7zm99QV1fHF198gclkoqampqvLFUJXNBsKAKGhoYrxxsZGrFYrU6ZMAaChoYHNmzdz8eJFTCYTAGFhYR4tw2AweDTdMyuKGfijBA8rv+X43hz+ciBfMXazyc6gUZO8mo/VWsIvJyd6PH3+L4qJGZrg1TK+762Mkg5/1h2rtYT/muJ5/dLrjvOk154+S1qToRASEgKAzWZj6tSprvG8vDxqamqIjY0F4OzZs/Tp04ff/va3HDx4EKPRyJIlS5gxY4Yqdd82dvoKtwe/xL0nvb73NBkKERERREdHk5ubS3BwMOHh4RQVFXHgwAEAVyi0trZy6dIlwsLCOH78OBcuXCAuLo7hw4fz4x//+I7L8DQ13/5IvWv84+MTKMr2rE6Asne1dY1/fHwCbRs9r1963XHe9vpONHmg0Wg0snv3bqKiosjIyGDevHmEhISwYMECTCaT6yDjoEGDAHjhhRcAeOCBBxg/fjzHjx9XrXYhujtNhgJAZGQkxcXF1NfXU1VVxerVq6moqGDkyJH4+fkBt3YzkpKS2L9/PwDffPMNx48fJyZG/bPChOiuNLn78I+UlZUxbtw4xdimTZtIS0vjjTfeoK2tjVdffbXdNF3p2cwSr8ZFx0mvO0e3CQW73Y7NZmP+/PmK8cGDB3Po0CGVqhJCfzS7+/BD/v7+OBwOFi1apHYp3cLpqs9YvD6OlzdMYOP7yvM9vr5+mWWbJrJ4fRzltkO0tbVR33id7R+uxOF00NBU53ae07P68M6B1wC40fAtq3fMYNmmiew8nAPAtoOZJGcF4XC0du7KaYzeet1tthSEd/oHDebNl47Q08fMml2pnK+pYEjYaAD+ULyWFx5fzdABMWRuncboiEfZd2wzpy6UsvPQapLGptHLHNBunkNCR5M2dQ0AOz5axQuPv8Ggfg+63p+XlE3l+U+6ZgU1RG+97jZbCsI7wYGh9PQxA2Ay3rq7z23nv6og6oE4/Hz96eUbQEtrU7uTuXJ2pnD20gnO11Twxo7n2s3/wleVFB7O5VebEvn8wtHOXRmN01uvZUtB585dPsn1+loG9x/pGnM6Ha4vZm9zH+yNV3nikXSaWupJnZRFc0sD859cR+6u2RgNJpbNLGg3388vlPK/Xi4nsFcwq7Y/w7oF998Wwg/ppdcSCjp2o+Fb1u9ZSObzf1SMGwzfbSDWN9/A368vvc2BzJ28EoBe5gB6mQMIC47AaDAS0mdAu3kP/KdIBvf/EQBGg2xw6qnX8q+pUw5HK2sLnyd9Wj7BgcprSCLCovn8wlEaW+ppaLpBb3Ngu8+fu3ySxuY6rtmvcPHKmXbvh/9TJN/cqKGxpR6H8/46sPhDeuu1bCnolPXkbmwX/8yW/csBSJuyhiMndrEw+W1mJCwn7925NN9sZO7kVe0+63A62LxvKb+etYNWRwvrLL8g58X9imlemLyK3J0ptNxs5Pmf/o8uWSet0luvDW2eXgRwn1LzfPyh/WDRTz2fvrPPx38x70HGj3rKdVT8h7YdzOTjk0Vs+dUpTEYTQQPh4Vmez196/Z3O7vWdSCjchXxRO05Coevcy1CQ3Ye7CO/bfZYd0K9z6ugob+uRXnfcvaxHthSEEAry64MQQkFCQQihIKEghFCQUBBCKEgoCCEUJBSEEAoSCkIIBQkFIYSChIIQQkFCQQihIKEghFCQUBBCKEgoCCEU5NLpu7CUwaWr6iw7vC88/bDn0585AnUq3Y/AnYB+MGKi59NLrzvO217fiYTCXVy6qt6NP7xVd0VbN/7wlvRaG2T3QQihIKEghFCQUBBCKEgoCCEU5ECjcFm6MYHTXx7FZLr1PMSw4AhSH8vkJ9HPqF2a7mi517KlIBRSJ2XxQY4dy8pvSHwoheydM6mutaldli5ptdcSCsItk6kHT8bNx+l0cL6mQu1ydE1rvZZQEG7dbG1hb+kGeph8iBgQo3Y5uqa1Xms6FJxOJ/n5+QwfPhyz2UxMTAxWq5URI0aQnp6udnluFWUncHxPtsfjWrPrcA7JWUHMzh7I0VN7eX3ue4SHDFO7LLek151D0wca09LSsFgsZGVlERsbS2lpKSkpKdTW1rJkyRK1y9Ol2Y+tIHVSptpl3Be02mvNhkJhYSEFBQWUlJQQHx8PQGJiIuXl5VgsFsaMGaNyhULok2ZDITc3l6SkJFcg3DZs2DB8fHyIjo7m2rVrJCQkuN5raWnh9OnTnDx5ktGjR3dxxULogyZDobq6msrKSl555ZV271VVVREVFYWvry++vr6cOHHC9d727dv57W9/q3ogHN+bw18O5CvGbjbZGTRqkkoV6Zf0+t7T5IHG6upbl5+FhoYqxhsbG7Farf9w12HLli0eH4A0GAwevazWEq/rHzt9BRm/u6Z4DYic4PV8rNYSj+vsaK3f91ZGyT3dx+2K+qXXt3hSv6c0GQohISEA2GzKEzny8vKoqakhNja23We++OILysvLSU1N7ZIahdArTYZCREQE0dHR5Obmsn37dg4fPkxGRgZbt24FcBsKv/vd75gxYwZ9+vTxaBltbW0eveLjE+7lqnklPj7B4zrVrtWd7lR/d6rVHU/q95QmQ8FoNLJ7926ioqLIyMhg3rx5hISEsGDBAkwmE9HR0Yrpm5ub2b59u2bPXRCiO9HkgUaAyMhIiouLFWNz5sxh5MiR+Pn5Kcb/9Kc/ERYWxj//8z93ZYluPZtZ4tW46DjpdefQ5JbCP1JWVuZ212HLli38/Oc/V6EiIfSn24SC3W7HZrO5/eXh8OHD/PKXv1ShKu06XfUZi9fH8fKGCWx8X/nT7tfXL7Ns00QWr4+j3HaItrY26huvs/3DlTicDhqa6tzOc3pWH9458BoAOb+fxdKNCfzy7X/mpd8+BMCeT9czY1Uol74+26nrpjV667Vmdx9+yN/fH4fDoXYZ3Ub/oMG8+dIRevqYWbMrlfM1FQwJu3X+xh+K1/LC46sZOiCGzK3TGB3xKPuObebUhVJ2HlpN0tg0epkD2s1zSOho0qauAWDF8+8C8EnFn/iPS38BIHn8QmwXy7poDbVDb73uNlsKwjvBgaH09DEDYDLeupHHbee/qiDqgTj8fP3p5RtAS2tTu9+xc3amcPbSCc7XVPDGjuf+4XI+rfwTE0Y93Tkr0U3ordfdZktBdMy5yye5Xl/L4P4jXWNOp8P1xext7oO98SpPPJJOU0s9qZOyaG5pYP6T68jdNRujwcSymQVu593quMn5ryoYPlCuQwH99FpCQcduNHzL+j0LyXz+j4pxg+G7DcT65hv4+/WltzmQuZNXAtDLHEAvcwBhwREYDUZC+gxwO/+//q2EmKEJnVV+t6KnXsvug045HK2sLXye9Gn5BAcqTxePCIvm8wtHaWypp6HpBr3Nge0+f+7ySRqb67hmv8LFK2fcLuPTyj8xftRTnVJ/d6K3Xkso6JT15G5sF//Mlv3LWboxgc8vHGX9nkUAzEhYzraDK/j15kmkTPxNu886nA4271tKxpPrmD/9f7Lxg1fanRHX1tbG518eZdQD3l9noDd667WhzZvzH+9Db3+k3qPMhvaDRT/1fPqydzv3UWYv5j3I+FFPuY6K/9CeT9ez/+gmstP207/vYIIGwsOzPJ+/9Po7nd3rO5FjCsJjW5d/ccf3k8cvJHn8wi6qRt/U7LWEwl2E9+0+yw7o1zl1dJS39UivO+5e1iO7D0IIBTnQKIRQkFAQQihIKAghFCQUhBAKEgpCCAUJBSGEgoSCEEJBQkEIoSChIIRQkFAQQihIKAghFCQUhBAKEgpCCAUJBSGEgoSCEEJBQkEIoSChIIRQkFAQQij8P1+aing0nZT3AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 327.397x505.68 with 1 Axes>"
       ]
@@ -648,7 +648,7 @@
    ],
    "source": [
     "feature_map = ZFeatureMap(8)\n",
-    "feature_map.decompose().draw(\"mpl\")"
+    "feature_map.decompose().draw(\"mpl\", style=\"clifford\")"
    ]
   },
   {
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "id": "cc478975",
    "metadata": {
     "scrolled": true
@@ -714,7 +714,7 @@
     "ansatz = QuantumCircuit(8, name=\"Ansatz\")\n",
     "\n",
     "# First Convolutional Layer\n",
-    "ansatz.compose(conv_layer(8, \"с1\"), list(range(8)), inplace=True)\n",
+    "ansatz.compose(conv_layer(8, \"c1\"), list(range(8)), inplace=True)\n",
     "\n",
     "# First Pooling Layer\n",
     "ansatz.compose(pool_layer([0, 1, 2, 3], [4, 5, 6, 7], \"p1\"), list(range(8)), inplace=True)\n",
@@ -749,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "id": "a4f6b6e7",
    "metadata": {
     "scrolled": false
@@ -757,18 +757,18 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1591.6x1047.48 with 1 Axes>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "circuit.draw(\"mpl\")"
+    "circuit.draw(\"mpl\", style=\"clifford\")"
    ]
   },
   {
@@ -781,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "d97cc662",
    "metadata": {
     "scrolled": true
@@ -812,7 +812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "id": "f2949fc6",
    "metadata": {
     "scrolled": true
@@ -842,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "id": "0219ff4a",
    "metadata": {
     "scrolled": false
@@ -850,7 +850,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -864,7 +864,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy from the train data : 88.57%\n"
+      "Accuracy from the train data : 97.14%\n"
      ]
     }
    ],
@@ -906,7 +906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "id": "7f2a34ae",
    "metadata": {
     "scrolled": false,
@@ -919,12 +919,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy from the test data : 80.0%\n"
+      "Accuracy from the test data : 93.33%\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x432 with 4 Axes>"
       ]
@@ -980,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "id": "220ffdcf",
    "metadata": {
     "scrolled": false
@@ -989,7 +989,7 @@
     {
      "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>0.22.1</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.1</td></tr><tr><td><code>qiskit-ignis</code></td><td>0.7.0</td></tr><tr><td><code>qiskit</code></td><td>0.33.0</td></tr><tr><td><code>qiskit-machine-learning</code></td><td>0.5.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.7.9</td></tr><tr><td>Python compiler</td><td>MSC v.1916 64 bit (AMD64)</td></tr><tr><td>Python build</td><td>default, Aug 31 2020 17:10:11</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>31.837730407714844</td></tr><tr><td colspan='2'>Fri Nov 04 00:37:45 2022 GMT Standard Time</td></tr></table>"
+       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.0.0.dev0+737f21b</td></tr><tr><td><code>qiskit_algorithms</code></td><td>0.3.0</td></tr><tr><td><code>qiskit_machine_learning</code></td><td>0.8.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.9.7</td></tr><tr><td>Python compiler</td><td>GCC 7.5.0</td></tr><tr><td>Python build</td><td>default, Sep 16 2021 13:09:58</td></tr><tr><td>OS</td><td>Linux</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>5.792198181152344</td></tr><tr><td colspan='2'>Thu Dec 14 13:53:25 2023 EST</td></tr></table>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1001,7 +1001,7 @@
     {
      "data": {
       "text/html": [
-       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2023.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1022,7 +1022,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1036,7 +1036,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.7"
   },
   "vscode": {
    "interpreter": {
diff --git a/qiskit_machine_learning/neural_networks/estimator_qnn.py b/qiskit_machine_learning/neural_networks/estimator_qnn.py
index 17997a578..b2f05e655 100644
--- a/qiskit_machine_learning/neural_networks/estimator_qnn.py
+++ b/qiskit_machine_learning/neural_networks/estimator_qnn.py
@@ -148,7 +148,7 @@ def __init__(
         if estimator is None:
             estimator = Estimator()
         self.estimator = estimator
-        self._circuit = circuit
+        self._org_circuit = circuit
         if observables is None:
             observables = SparsePauliOp.from_list([("Z" * circuit.num_qubits, 1)])
         if isinstance(observables, BaseOperator):
@@ -173,10 +173,12 @@ def __init__(
             input_gradients=input_gradients,
         )
 
+        self._circuit = self._reparameterize_circuit(circuit, input_params, weight_params)
+
     @property
     def circuit(self) -> QuantumCircuit:
         """The quantum circuit representing the neural network."""
-        return copy(self._circuit)
+        return copy(self._org_circuit)
 
     @property
     def observables(self) -> Sequence[BaseOperator] | BaseOperator:
diff --git a/qiskit_machine_learning/neural_networks/neural_network.py b/qiskit_machine_learning/neural_networks/neural_network.py
index dc3969d07..67459f109 100644
--- a/qiskit_machine_learning/neural_networks/neural_network.py
+++ b/qiskit_machine_learning/neural_networks/neural_network.py
@@ -16,9 +16,11 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from typing import Sequence
 
 import numpy as np
 
+from qiskit.circuit import Parameter, ParameterVector, QuantumCircuit
 import qiskit_machine_learning.optionals as _optionals
 from ..exceptions import QiskitMachineLearningError
 
@@ -264,3 +266,54 @@ def _backward(
         self, input_data: np.ndarray | None, weights: np.ndarray | None
     ) -> tuple[np.ndarray | SparseArray | None, np.ndarray | SparseArray | None]:
         raise NotImplementedError
+
+    def _reparameterize_circuit(
+        self,
+        circuit: QuantumCircuit,
+        input_params: Sequence[Parameter] | None = None,
+        weight_params: Sequence[Parameter] | None = None,
+    ) -> QuantumCircuit:
+        # As the data (parameter values) for the primitive is ordered as inputs followed by weights
+        # we need to ensure that the parameters are ordered like this naturally too so the rewrites
+        # parameters to ensure this. "inputs" as a name comes before "weights" and within they are
+        # numerically ordered.
+        if input_params and self.num_inputs != len(input_params):
+            raise ValueError(
+                f"input_params length {len(input_params)}"
+                f" mismatch with num_inputs (self.num_inputs)"
+            )
+        if weight_params and self.num_weights != len(weight_params):
+            raise ValueError(
+                f"weight_params length {len(weight_params)}"
+                f" mismatch with num_weights (self.num_weights)"
+            )
+
+        parameters = circuit.parameters
+
+        if len(parameters) != (self.num_inputs + self.num_weights):
+            raise ValueError(
+                f"Number of circuit parameters {len(parameters)}"
+                f" mismatch with sum of num inputs and weights"
+                f" {self.num_inputs + self.num_weights}"
+            )
+
+        new_input_params = ParameterVector("inputs", self.num_inputs)
+        new_weight_params = ParameterVector("weights", self.num_weights)
+
+        new_parameters = {}
+        if input_params:
+            for i, param in enumerate(input_params):
+                if param not in parameters:
+                    raise ValueError(f"Input param `{param.name}` not present in circuit")
+                new_parameters[param] = new_input_params[i]
+
+        if weight_params:
+            for i, param in enumerate(weight_params):
+                if param not in parameters:
+                    raise ValueError(f"Weight param {param.name} `not present in circuit")
+                new_parameters[param] = new_weight_params[i]
+
+        if new_parameters:
+            circuit = circuit.assign_parameters(new_parameters)
+
+        return circuit
diff --git a/qiskit_machine_learning/neural_networks/sampler_qnn.py b/qiskit_machine_learning/neural_networks/sampler_qnn.py
index 1e44acd35..9fc2d52d3 100644
--- a/qiskit_machine_learning/neural_networks/sampler_qnn.py
+++ b/qiskit_machine_learning/neural_networks/sampler_qnn.py
@@ -182,9 +182,7 @@ def __init__(
             gradient = ParamShiftSamplerGradient(self.sampler)
         self.gradient = gradient
 
-        self._circuit = circuit.copy()
-        if len(self._circuit.clbits) == 0:
-            self._circuit.measure_all()
+        self._org_circuit = circuit
 
         if isinstance(circuit, QNNCircuit):
             self._input_params = list(circuit.input_parameters)
@@ -207,10 +205,15 @@ def __init__(
             input_gradients=self._input_gradients,
         )
 
+        if len(circuit.clbits) == 0:
+            circuit = circuit.copy()
+            circuit.measure_all()
+        self._circuit = self._reparameterize_circuit(circuit, input_params, weight_params)
+
     @property
     def circuit(self) -> QuantumCircuit:
         """Returns the underlying quantum circuit."""
-        return self._circuit
+        return self._org_circuit
 
     @property
     def input_params(self) -> Sequence[Parameter]:
@@ -274,7 +277,7 @@ def _compute_output_shape(
                     "No interpret function given, output_shape will be automatically "
                     "determined as 2^num_qubits."
                 )
-            output_shape_ = (2**self._circuit.num_qubits,)
+            output_shape_ = (2**self.circuit.num_qubits,)
 
         return output_shape_
 
diff --git a/releasenotes/notes/fix_qnn_binding_order-74caef8a49ecffe5.yaml b/releasenotes/notes/fix_qnn_binding_order-74caef8a49ecffe5.yaml
new file mode 100644
index 000000000..e07559fd2
--- /dev/null
+++ b/releasenotes/notes/fix_qnn_binding_order-74caef8a49ecffe5.yaml
@@ -0,0 +1,13 @@
+---
+fixes:
+  - |
+    Fixes an issue for the Quantum Neural Networks where the binding order of the inputs
+    and weights might end up being incorrect. Though the params for the inputs and weights
+    are specified to the QNN, the code previously bound the inputs and weights in the order
+    given by the circuit.parameters. This would end up being the right order for the Qiskit
+    circuit library feature maps and ansatzes most often used, as the default parameter
+    names led to the order being as expected. However for custom names etc. this was not
+    always the case and then led to unexpected behavior. The sequences for the input and
+    weights parameters, as supplied, are now always used as the binding order, for the inputs
+    and weights respectively, such that the order of the parameters in the overall circuit
+    no longer matters.


Test patch:
diff --git a/test/neural_networks/test_estimator_qnn.py b/test/neural_networks/test_estimator_qnn.py
index b1bcd810f..fc80e8e1e 100644
--- a/test/neural_networks/test_estimator_qnn.py
+++ b/test/neural_networks/test_estimator_qnn.py
@@ -18,7 +18,7 @@
 
 import numpy as np
 from qiskit.circuit import Parameter, QuantumCircuit
-from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes
+from qiskit.circuit.library import ZZFeatureMap, RealAmplitudes, ZFeatureMap
 from qiskit.quantum_info import SparsePauliOp
 from qiskit_machine_learning.circuit.library import QNNCircuit
 
@@ -447,6 +447,26 @@ def test_qnn_qc_circui_construction(self):
             # Test if weights grad is identical
             np.testing.assert_array_almost_equal(backward_qc[1], backward_qnn_qc[1])
 
+    def test_binding_order(self):
+        """Test parameter binding order gives result as expected"""
+        qc = ZFeatureMap(feature_dimension=2, reps=1)
+        input_params = qc.parameters
+        weight = Parameter("weight")
+        for i in range(qc.num_qubits):
+            qc.rx(weight, i)
+
+        observable1 = SparsePauliOp.from_list([("Z" * qc.num_qubits, 1)])
+        estimator_qnn = EstimatorQNN(
+            circuit=qc, observables=observable1, input_params=input_params, weight_params=[weight]
+        )
+
+        estimator_qnn_weights = [3]
+        estimator_qnn_input = [2, 33]
+        res = estimator_qnn.forward(estimator_qnn_input, estimator_qnn_weights)
+        # When parameters were used in circuit order, before being assigned correctly, so inputs
+        # went to input params, weights to weight params, this gave 0.00613403
+        self.assertAlmostEqual(res[0][0], 0.00040017)
+
 
 if __name__ == "__main__":
     unittest.main()