projectq 0.8.0
ProjectQ - An open source software framework for quantum computing
License: Apache License Version 2.0
Requires Python: >=3.7

ProjectQ - An open source software framework for quantum computing
==================================================================

.. image:: https://img.shields.io/pypi/pyversions/projectq?label=Python
   :alt: PyPI - Python Version

.. image:: https://badge.fury.io/py/projectq.svg
   :target: https://badge.fury.io/py/projectq

.. image:: https://github.com/ProjectQ-Framework/ProjectQ/actions/workflows/ci.yml/badge.svg
   :alt: CI Status
   :target: https://github.com/ProjectQ-Framework/ProjectQ/actions/workflows/ci.yml

.. image:: https://coveralls.io/repos/github/ProjectQ-Framework/ProjectQ/badge.svg
   :alt: Coverage Status
   :target: https://coveralls.io/github/ProjectQ-Framework/ProjectQ

.. image:: https://readthedocs.org/projects/projectq/badge/?version=latest
   :target: http://projectq.readthedocs.io/en/latest/?badge=latest
   :alt: Documentation Status


ProjectQ is an open source effort for quantum computing.

It features a compilation framework capable of
targeting various types of hardware, a high-performance quantum computer
simulator with emulation capabilities, and various compiler plug-ins.
This allows users to

-  run quantum programs on the IBM Quantum Experience chip, AQT devices, AWS Braket, Azure Quantum, or IonQ service provided devices
-  simulate quantum programs on classical computers
-  emulate quantum programs at a higher level of abstraction (e.g.,
   mimicking the action of large oracles instead of compiling them to
   low-level gates)
-  export quantum programs as circuits (using TikZ)
-  get resource estimates

Examples
--------

**First quantum program**

.. code-block:: python

    from projectq import MainEngine  # import the main compiler engine
    from projectq.ops import (
        H,
        Measure,
    )  # import the operations we want to perform (Hadamard and measurement)

    eng = MainEngine()  # create a default compiler (the back-end is a simulator)
    qubit = eng.allocate_qubit()  # allocate a quantum register with 1 qubit

    H | qubit  # apply a Hadamard gate
    Measure | qubit  # measure the qubit

    eng.flush()  # flush all gates (and execute measurements)
    print(f"Measured {int(qubit)}")  # converting a qubit to int or bool gives access to the measurement result


ProjectQ features a lean syntax which is close to the mathematical notation used in quantum physics. For example, a rotation of a qubit around the x-axis is usually specified as:

.. image:: docs/images/braket_notation.svg
    :alt: Rx(theta)|qubit>
    :width: 100px

The same statement in ProjectQ's syntax is:

.. code-block:: python

    Rx(theta) | qubit

The **|**-operator separates the specification of the gate operation (left-hand side) from the quantum bits to which the operation is applied (right-hand side).

**Changing the compiler and using a resource counter as a back-end**

Instead of simulating a quantum program, one can use our resource counter (as a back-end) to determine how many operations it would take on a future quantum computer with a given architecture. Suppose the qubits are arranged on a linear chain and the architecture supports any single-qubit gate as well as the two-qubit CNOT and Swap operations:

.. code-block:: python

    from projectq import MainEngine
    from projectq.backends import ResourceCounter
    from projectq.ops import QFT
    from projectq.setups import linear

    compiler_engines = linear.get_engine_list(num_qubits=16, one_qubit_gates='any', two_qubit_gates=(CNOT, Swap))
    resource_counter = ResourceCounter()
    eng = MainEngine(backend=resource_counter, engine_list=compiler_engines)
    qureg = eng.allocate_qureg(16)
    QFT | qureg
    eng.flush()

    print(resource_counter)

    # This will output, among other information,
    # how many operations are needed to perform
    # this quantum fourier transform (QFT), i.e.,
    #   Gate class counts:
    #       AllocateQubitGate : 16
    #       CXGate : 240
    #       HGate : 16
    #       R : 120
    #       Rz : 240
    #       SwapGate : 262


**Running a quantum program on IBM's QE chips**

To run a program on the IBM Quantum Experience chips, all one has to do is choose the `IBMBackend` and the corresponding setup:

.. code-block:: python

    import projectq.setups.ibm
    from projectq.backends import IBMBackend

    token = 'MY_TOKEN'
    device = 'ibmq_16_melbourne'
    compiler_engines = projectq.setups.ibm.get_engine_list(token=token, device=device)
    eng = MainEngine(
        IBMBackend(token=token, use_hardware=True, num_runs=1024, verbose=False, device=device),
        engine_list=compiler_engines,
    )


**Running a quantum program on AQT devices**

To run a program on the AQT trapped ion quantum computer, choose the `AQTBackend` and the corresponding setup:

.. code-block:: python

    import projectq.setups.aqt
    from projectq.backends import AQTBackend

    token = 'MY_TOKEN'
    device = 'aqt_device'
    compiler_engines = projectq.setups.aqt.get_engine_list(token=token, device=device)
    eng = MainEngine(
        AQTBackend(token=token, use_hardware=True, num_runs=1024, verbose=False, device=device),
        engine_list=compiler_engines,
    )


**Running a quantum program on a AWS Braket provided device**

To run a program on some of the devices provided by the AWS Braket service,
choose the `AWSBraketBackend`. The currend devices supported are Aspen-8 from Rigetti,
IonQ from IonQ and the state vector simulator SV1:

.. code-block:: python

    from projectq.backends import AWSBraketBackend

    creds = {
        'AWS_ACCESS_KEY_ID': 'your_aws_access_key_id',
        'AWS_SECRET_KEY': 'your_aws_secret_key',
    }

    s3_folder = ['S3Bucket', 'S3Directory']
    device = 'IonQ'
    eng = MainEngine(
        AWSBraketBackend(
            use_hardware=True,
            credentials=creds,
            s3_folder=s3_folder,
            num_runs=1024,
            verbose=False,
            device=device,
        ),
        engine_list=[],
    )


.. note::

   In order to use the AWSBraketBackend, you need to install ProjectQ with the 'braket' extra requirement:

   .. code-block:: bash

       python3 -m pip install projectq[braket]

   or

   .. code-block:: bash

       cd /path/to/projectq/source/code
       python3 -m pip install -ve .[braket]


**Running a quantum program on a Azure Quantum provided device**

To run a program on devices provided by the `Azure Quantum <https://azure.microsoft.com/en-us/services/quantum/>`_.

Use `AzureQuantumBackend` to run ProjectQ circuits on hardware devices and simulator devices from providers `IonQ` and `Quantinuum`.

.. code-block:: python

    from projectq.backends import AzureQuantumBackend

    azure_quantum_backend = AzureQuantumBackend(
        use_hardware=False, target_name='ionq.simulator', resource_id='<resource-id>', location='<location>', verbose=True
    )

.. note::

   In order to use the AzureQuantumBackend, you need to install ProjectQ with the 'azure-quantum' extra requirement:

   .. code-block:: bash

       python3 -m pip install projectq[azure-quantum]

   or

   .. code-block:: bash

       cd /path/to/projectq/source/code
       python3 -m pip install -ve .[azure-quantum]

**Running a quantum program on IonQ devices**

To run a program on the IonQ trapped ion hardware, use the `IonQBackend` and its corresponding setup.

Currently available devices are:

* `ionq_simulator`: A 29-qubit simulator.
* `ionq_qpu`: A 11-qubit trapped ion system.

.. code-block:: python

    import projectq.setups.ionq
    from projectq import MainEngine
    from projectq.backends import IonQBackend

    token = 'MY_TOKEN'
    device = 'ionq_qpu'
    backend = IonQBackend(
        token=token,
        use_hardware=True,
        num_runs=1024,
        verbose=False,
        device=device,
    )
    compiler_engines = projectq.setups.ionq.get_engine_list(
        token=token,
        device=device,
    )
    eng = MainEngine(backend, engine_list=compiler_engines)


**Classically simulate a quantum program**

ProjectQ has a high-performance simulator which allows simulating up to about 30 qubits on a regular laptop. See the `simulator tutorial <https://github.com/ProjectQ-Framework/ProjectQ/blob/feature/update-readme/examples/simulator_tutorial.ipynb>`__ for more information. Using the emulation features of our simulator (fast classical shortcuts), one can easily emulate Shor's algorithm for problem sizes for which a quantum computer would require above 50 qubits, see our `example codes <http://projectq.readthedocs.io/en/latest/examples.html#shor-s-algorithm-for-factoring>`__.


The advanced features of the simulator are also particularly useful to investigate algorithms for the simulation of quantum systems. For example, the simulator can evolve a quantum system in time (without Trotter errors) and it gives direct access to expectation values of Hamiltonians leading to extremely fast simulations of VQE type algorithms:

.. code-block:: python

    from projectq import MainEngine
    from projectq.ops import All, Measure, QubitOperator, TimeEvolution

    eng = MainEngine()
    wavefunction = eng.allocate_qureg(2)
    # Specify a Hamiltonian in terms of Pauli operators:
    hamiltonian = QubitOperator("X0 X1") + 0.5 * QubitOperator("Y0 Y1")
    # Apply exp(-i * Hamiltonian * time) (without Trotter error)
    TimeEvolution(time=1, hamiltonian=hamiltonian) | wavefunction
    # Measure the expection value using the simulator shortcut:
    eng.flush()
    value = eng.backend.get_expectation_value(hamiltonian, wavefunction)

    # Last operation in any program should be measuring all qubits
    All(Measure) | qureg
    eng.flush()



Getting started
---------------

To start using ProjectQ, simply follow the installation instructions in the `tutorials <http://projectq.readthedocs.io/en/latest/tutorials.html>`__. There, you will also find OS-specific hints, a small introduction to the ProjectQ syntax, and a few `code examples <http://projectq.readthedocs.io/en/latest/examples.html>`__. More example codes and tutorials can be found in the examples folder `here <https://github.com/ProjectQ-Framework/ProjectQ/tree/develop/examples>`__ on GitHub.

Also, make sure to check out the `ProjectQ
website <http://www.projectq.ch>`__ and the detailed `code documentation <http://projectq.readthedocs.io/en/latest/>`__.

How to contribute
-----------------

For information on how to contribute, please visit the `ProjectQ
website <http://www.projectq.ch>`__ or send an e-mail to
info@projectq.ch.

Please cite
-----------

When using ProjectQ for research projects, please cite

-  Damian S. Steiger, Thomas Haener, and Matthias Troyer "ProjectQ: An
   Open Source Software Framework for Quantum Computing"
   `Quantum 2, 49 (2018) <https://doi.org/10.22331/q-2018-01-31-49>`__
   (published on `arXiv <https://arxiv.org/abs/1612.08091>`__ on 23 Dec 2016)
-  Thomas Haener, Damian S. Steiger, Krysta M. Svore, and Matthias Troyer
   "A Software Methodology for Compiling Quantum Programs" `Quantum Sci. Technol. 3 (2018) 020501 <https://doi.org/10.1088/2058-9565/aaa5cc>`__
   (published on `arXiv <http://arxiv.org/abs/1604.01401>`__ on 5 Apr 2016)

Authors
-------

The first release of ProjectQ (v0.1) was developed by `Thomas
Haener <http://www.comp.phys.ethz.ch/people/person-detail.html?persid=179208>`__
and `Damian S.
Steiger <http://www.comp.phys.ethz.ch/people/person-detail.html?persid=165677>`__
in the group of `Prof. Dr. Matthias
Troyer <http://www.comp.phys.ethz.ch/people/troyer.html>`__ at ETH
Zurich.

ProjectQ is constantly growing and `many other people <https://github.com/ProjectQ-Framework/ProjectQ/graphs/contributors>`__ have already contributed to it in the meantime.

License
-------

ProjectQ is released under the Apache 2 license.