v2.3 (January 28, 2019)

PyQuil 2.3 is the latest release of pyQuil, Rigetti’s toolkit for constructing and running quantum programs. A major new feature is the release of a new suite of simulators:

  • We’re proud to introduce the first iteration of a Python-based quantum virtual machine (QVM) called PyQVM. This QVM is completely contained within pyQuil and does not need any external dependencies. Try using it with get_qc("9q-square-pyqvm") or explore the pyquil.pyqvm.PyQVM object directly. Under-the-hood, there are three quantum simulator backends:
    • ReferenceWavefunctionSimulator uses standard matrix-vector multiplication to evolve a statevector. This includes a suite of tools in pyquil.unitary_tools for dealing with unitary matrices.
    • NumpyWavefunctionSimulator uses numpy’s tensordot functionality to efficiently evolve a statevector. For most simulations, performance is quite good.
    • ReferenceDensitySimulator uses matrix-matrix multiplication to evolve a density matrix.
  • Matrix representations of Quil standard gates are included in pyquil.gate_matrices (gh-552).
  • The density simulator has extremely limited support for Kraus-operator based noise models. Let us know if you’re interested in contributing more robust noise-model support.
  • This functionality should be considered experimental and may undergo minor API changes.

Important changes to note:

  • Quil math functions (like COS, SIN, …) used to be ambiguous with respect to case sensitivity. They are now case-sensitive and should be uppercase (gh-774).
  • In the next release of pyQuil, communication with quilc will happen exclusively via the rpcq protocol. LocalQVMCompiler and LocalBenchmarkConnection will be removed in favor of a unified QVMCompiler and BenchmarkConnection. This change should be transparent if you use get_qc and get_benchmarker, respectively. In anticipation of this change we recommend that you upgrade your version of quilc to 1.3, released Jan 30, 2019 (gh-730).
  • When using a paramaterized gate, the QPU control electronics only allowed multiplying parameters by powers of two. If you only ever multiply a parameter by the same constant, this isn’t too much of a problem because you can fold the multiplicative constant into the definition of the parameter. However, if you are multiplying the same variable (e.g. gamma in QAOA) by different constants (e.g. weighted maxcut edge weights) it doesn’t work. PyQuil will now transparently handle the latter case by expanding to a vector of parameters with the constants folded in, allowing you to multiply variables by whatever you want (gh-707).

As always, this release contains bug fixes and improvements:

  • The CZ gate fidelity metric available in the Specs object now has its associated standard error, which is accessible from the method Specs.fCZ_std_errs (gh-751).
  • Operator estimation code now correctly handles identity terms with coefficients. Previously, it would always estimate these terms as 1.0 (gh-758).
  • Operator estimation results include the total number of counts (shots) taken.
  • Operator estimation JSON serialization uses utf-8. Please let us know if this causes problems (gh-769).
  • The example quantum die program now can roll dice that are not powers of two (gh-749).
  • The teleportation and Meyer penny game examples had a syntax error (gh-778, gh-772).
  • When running on the QPU, you could get into trouble if the QPU name passed to get_qc did not match the lattice you booked. This is now validated (gh-771).

We extend thanks to community member estamm12 for their contribution to this release.

v2.2 (January 4, 2019)

PyQuil 2.2 is the latest release of pyQuil, Rigetti’s toolkit for constructing and running quantum programs. Bug fixes and improvements include:

  • pauli.is_zero and paulis.is_identity would sometimes return erroneous answers (gh-710).
  • Parameter expressions involving addition and subtraction are now converted to Quil with spaces around the operators, e.g. theta + 2 instead of theta+2. This disambiguates subtracting two parameters, e.g. alpha - beta is not one variable named alpha-beta (gh-743).
  • T1 is accounted for in T2 noise models (gh-745).
  • Documentation improvements (gh-723, gh-719, gh-720, gh-728, gh-732, gh-742).
  • Support for PNG generation of circuit diagrams via LaTeX (gh-745).
  • We’ve started transitioning to using Gitlab as our continuous integration provider for pyQuil (gh-741, gh-752).

This release includes a new module for facilitating the estimation of quantum observables/operators (gh-682). First-class support for estimating observables should make it easier to express near-term algorithms. This release includes:

  • data structures for expressing tomography-like experiments and their results
  • grouping of experiment settings that can be simultaneously estimated
  • functionality to executing a tomography-like experiment on a quantum computer

Please look forward to more features and polish in future releases. Don’t hesitate to submit feedback or suggestions as GitHub issues.

We extend thanks to community member petterwittek for their contribution to this release.

Bugfix release 2.2.1 was released January 11 to maintain compatibility with the latest version of the quilc compiler (gh-759).

v2.1 (November 30, 2018)

PyQuil 2.1 is an incremental release of pyQuil, Rigetti’s toolkit for constructing and running quantum programs. Changes include:

  • Major documentation improvements.
  • accepts an optional memory_map parameter to facilitate running parametric executables (gh-657).
  • QuantumComputer.reset() will reset the state of a QAM to recover from an error condition (gh-703).
  • Bug fixes (gh-674, gh-696).
  • Quil parser improvements (gh-689, gh-685).
  • Optional interleaver argument when generating RB sequences (gh-673).
  • Our GitHub organization name has changed from rigetticomputing to rigetti (gh-713).

v2.0 (November 1, 2018)

PyQuil 2.0 is a major release of pyQuil, Rigetti’s toolkit for constructing and running quantum programs. This release contains many major changes including:

  1. The introduction of Quantum Cloud Services. Access Rigetti’s QPUs from co-located classical compute resources for minimal latency. The web API for running QVM and QPU jobs has been deprecated and cannot be accessed with pyQuil 2.0
  2. Advances in classical control systems and compilation allowing the pre-compilation of parametric binary executables for rapid hybrid algorithm iteration.
  3. Changes to Quil—our quantum instruction language—to provide easier ways of interacting with classical memory.

The new QCS access model and features will allow you to execute hybrid quantum algorithms several orders of magnitude (!) faster than the previous web endpoint. However, to fully exploit these speed increases you must update your programs to use the latest pyQuil features and APIs. Please read New in Forest 2 - Other for a comprehensive migration guide.

An incomplete list of significant changes:

  • Python 2 is no longer supported. Please use Python 3.6+
  • Parametric gates are now normal functions. You can no longer write RX(pi/2)(0) to get a Quil RX(pi/2) 0 instruction. Just use RX(pi/2, 0).
  • Gates support keyword arguments, so you can write RX(angle=pi/2, qubit=0).
  • All async methods have been removed from QVMConnection and QVMConnection is deprecated. QPUConnection has been removed in accordance with the QCS access model. Use pyquil.get_qc() as the primary means of interacting with the QVM or QPU.
  • WavefunctionSimulator allows unfettered access to wavefunction properties and routines. These methods and properties previously lived on QVMConnection and have been deprecated there.
  • Classical memory in Quil must be declared with a name and type. Please read New in Forest 2 - Other for more.
  • Compilation has changed. There are now different Compiler objects that target either the QPU or QVM. You must explicitly compile your programs to run on a QPU or a realistic QVM.

Version 2.0.1 was released on November 9, 2018 and includes documentation changes only. This release is only available as a git tag. We have not pushed a new package to PyPI.

v1.9 (June 6, 2018)

We’re happy to announce the release of pyQuil 1.9. PyQuil is Rigetti’s toolkit for constructing and running quantum programs. This release is the latest in our series of regular releases, and it’s filled with convenience features, enhancements, bug fixes, and documentation improvements.

Special thanks to community members sethuiyer, vtomole, rht, akarazeev, ejdanderson, markf94, playadust, and kadora626 for contributing to this release!

Qubit placeholders

One of the focuses of this release is a re-worked concept of “Qubit Placeholders”. These are logical qubits that can be used to construct programs. Now, a program containing qubit placeholders must be “addressed” prior to running on a QPU or QVM. The addressing stage involves mapping each qubit placeholder to a physical qubit (represented as an integer). For example, if you have a 3 qubit circuit that you want to run on different sections of the Agave chip, you now can prepare one Program and address it to many different subgraphs of the chip topology. Check out the QubitPlaceholder example notebook for more.

To support this idea, we’ve refactored parts of Pyquil to remove the assumption that qubits can be “sorted”. While true for integer qubit labels, this probably isn’t true in general. A notable change can be found in the construction of a PauliSum: now terms will stay in the order they were constructed.

  • PauliTerm now remembers the order of its operations. sX(1)*sZ(2) will compile to different Quil code than sZ(2)*sX(1), although the terms will still be equal according to the __eq__ method. During PauliSum combination of like terms, a warning will be emitted if two terms are combined that have different orders of operation.
  • takes an optional argument sort_ops which defaults to True for backwards compatibility. However, this function should not be used for comparing term-type like it has been used previously. Use PauliTerm.operations_as_set() instead. In the future, sort_ops will default to False and will eventually be removed.
  • Program.alloc() has been deprecated. Please instantiate QubitPlaceholder() directly or request a “register” (list) of n placeholders by using the class constructor QubitPlaceholder.register(n)().
  • Programs must contain either (1) all instantiated qubits with integer indexes or (2) all placeholder qubits of type QubitPlaceholder. We have found that most users use (1) but (2) will become useful with larger and more diverse devices.
  • Programs that contain qubit placeholders must be explicitly addressed prior to execution. Previously, qubits would be assigned “under the hood” to integers 0…N. Now, you must use address_qubits() which returns a new program with all qubits indexed depending on the qubit_mapping argument. The original program is unaffected and can be “readdressed” multiple times.
  • PauliTerm can now accept QubitPlaceholder in addition to integers.
  • QubitPlaceholder is no longer a subclass of Qubit. LabelPlaceholder is no longer a subclass of Label.
  • QuilAtom subclasses’ hash functions have changed.

Randomized benchmarking sequence generation

Pyquil now includes support for performing a simple benchmarking routine - randomized benchmarking. There is a new method in the CompilerConnection that will return sequences of pyquil programs, corresponding to elements of the Clifford group. These programs are uniformly randomly sampled, and have the property that they compose to the identity. When concatenated and run as one program, these programs can be used in a procedure called randomized benchmarking to gain insight about the fidelity of operations on a QPU.

In addition, the CompilerConnection has another new method, apply_clifford_to_pauli() which conjugates PauliTerms by Program that are composed of Clifford gates. That is to say, given a circuit C, that contains only gates corresponding to elements of the Clifford group, and a tensor product of elements P, from the Pauli group, this method will compute $PCP^{dagger}$. Such a procedure can be used in various ways. An example is predicting the effect a Clifford circuit will have on an input state modeled as a density matrix, which can be written as a sum of Pauli matrices.

Ease of Use

This release includes some quality-of-life improvements such as the ability to initialize programs with generator expressions, sensible defaults for Program.measure_all(), and sensible defaults for classical_addresses in run() methods.

  • Program can be initiated with a generator expression.
  • Program.measure_all() (with no arguments) will measure all qubits in a program.
  • classical_addresses is now optional in QVM and QPU run() methods. By default, any classical addresses targeted by MEASURE will be returned.
  • QVMConnection.pauli_expectation() accepts PauliSum as arguments. This offers a more sensible API compared to QVMConnection.expectation().
  • pyQuil will now retry jobs every 10 seconds if the QPU is re-tuning.
  • CompilerConnection.compile() now takes an optional argument isa that allows per-compilation specification of the target ISA.
  • An empty program will trigger an exception if you try to run it.

Supported versions of Python

We strongly support using Python 3 with Pyquil. Although this release works with Python 2, we are dropping official support for this legacy language and moving to community support for Python 2. The next major release of Pyquil will introduce Python 3.5+ only features and will no longer work without modification for Python 2.

Bug fixes

  • shift_quantum_gates has been removed. Users who relied on this functionality should use QubitPlaceholder and address_qubits() to achieve the same result. Users should also double-check data resulting from use of this function as there were several edge cases which would cause the shift to be applied incorrectly resulting in badly-addressed qubits.
  • Slightly perturbed angles when performing RX gates under a Kraus noise model could result in incorrect behavior.
  • The quantum die example returned incorrect values when n = 2^m.