v2.10 (July 31, 2019)

Improvements and Changes:

  • Rewrote the README, adding a more in-depth overview of the purpose of pyQuil as a library, as well as two badges – one for PyPI downloads and another for the Forest Slack workspace. Also, included an example section for how to get started with running a simple Bell state program on the QVM (gh-949).
  • The test suite for pyquil.operator_estimation now has an (optional) faster version that uses fixed random seeds instead of averaging over several experiments. This can be enabled with the –use-seed command line option when running pytest (gh-928).
  • Deleted the deprecated modules and (gh-957).
  • Updated the examples README. Removed an outdated notebook. Updated remaining notebooks to use MemoryReference, and fix any parts that were broken (gh-820).
  • The AbstractCompiler.quil_to_native_quil() function now accepts a protoquil keyword which tells the compiler to restrict both input and output to protoquil (i.e. Quil code executable on a QPU). Additionally, the compiler will return a metadata dictionary that contains statistics about the compiled program, e.g. its estimated QPU runtime. See the compiler docs for more information (gh-940).
  • Updated the QCS and Slack invite links on the index.rst docs page (gh-965).
    • Provided example code for reading out the QPU runtime estimation for a program (gh-963).


  • unitary_tools.lifted_gate() was not properly handling modifiers such as DAGGER and CONTROLLED (gh-931).

v2.9.1 (June 28, 2019)


  • Relaxed the requirement for a quilc server to exist when users of the QuantumComputer object only want to do simulation work with a QVM or pyQVM backend (gh-934).

v2.9 (June 25, 2019)


  • PyQuil now has a Pull Request Template, which contains a checklist of things that must be completed (if applicable) before a PR can be merged (gh-921).

Improvements and Changes:

  • Removed a bunch of logic around creating inverse gates from user-defined gates in Program.dagger() in favor of a simpler call to Gate.dagger() (gh-887).
  • The RESET instruction now works correctly with QubitPlaceholder objects and the address_qubits function (gh-910).
  • ReferenceDensitySimulator can now have a state that is persistent between rounds of run or run_and_measure (gh-920).


  • Small negative probabilities were causing ReferenceDensitySimulator to fail (gh-908).
  • The dagger function was incorrectly dropping gate modifiers like CONTROLLED (gh-914).
  • Negative numbers in classical instruction arguments were not being parsed (gh-917).
  • Inline math rendering was not working correctly in intro.rst (gh-927).

Thanks to community member jclapis for the contributions to this release!

v2.8 (May 20, 2019)

Improvements and Changes:

  • PyQuil now verifies that you are using the correct version of the QVM and quilc (gh-913).
  • Added support for defining permutation gates for use with the latest version of quilc (gh-891).


  • Preserve modifiers during address_qubits (gh-907).

v2.7.2 (May 3, 2019)


  • An additional backwards-incompatible change from gh-870 snuck through 2.7.1, and is addressed in this patch release.

v2.7.1 (April 30, 2019)


  • The changes to operator estimation (gh-870, gh-896) were not made in a backwards-compatible fashion, and therefore this patch release aims to remedy that. Going forward, there will be much more stringent requirements around backwards compatibility and deprecation.

v2.7 (April 29, 2019)

Improvements and Changes:

  • Standard deviation -> standard error in operator estimation (gh-870).
  • Update what pyQuil expects from quilc in terms of rewiring pragmas – they are now comments rather than distinct instructions (gh-878).
  • Allow users to deprioritize QPU jobs – mostly a Rigetti-internal feature (gh-877).
  • Remove the qubits field from the TomographyExperiment dataclass (gh-896).


  • Ensure that shots aren’t lost when passing a Program through address_qubits (gh-895).

v2.6 (March 29, 2019)

Improvements and Changes:

  • Added a CODEOWNERS file for default reviewers (gh-855).
  • Bifurcated the QPUCompiler endpoint parameter into two – quilc_endpoint and qpu_compiler_endpoint – to reflect changes in Quantum Cloud Services (gh-856).
  • Clarified documentation around the DELAY pragma (gh-862).
  • Added information about the local_qvm context manager to the getting started documentation (gh-851).


  • Added a non-None default timeout to the QVMCompiler object (gh-850) and the get_benchmarker function (gh-854).
  • Fixed the docstring for the apply_clifford_to_pauli function (gh-836).
  • Allowed the apply_clifford_to_pauli function to now work with the Identity as input (gh-849).
  • Updated a stale link to the Rigetti Forest Slack workspace (gh-860).
  • Fixed a notation typo in the documentation for noise (gh-861).

Special thanks to willzeng for all the contributions this release!

v2.5 (March 6, 2019)

Improvements and Changes:

  • PyQuil’s Gate objects now expose .controlled(q) and .dagger() modifiers, which turn a gate respectively into its controlled variant, conditional on the qubit q, or into its inverse.
  • The operator estimation suite’s measure_observables method now exposes a readout_symmetrize argument, which helps mitigate a machine’s fidelity asymmetry between recognizing a qubit in the ground state versus the excited state.
  • The MEASURE instruction in pyQuil now has a mandatory second argument. Previously, the second argument could be omitted to induce “measurement for effect”, without storing the readout result to a classical register, but users found this to be a common source of accidental error and a generally rude surprise. To ensure the user really intends to measure only for effect, we now require that they supply an explicit None as the second argument.


  • Some stale tests have been brought into the modern era.

v2.4 (February 14, 2019)


  • The Quil Compiler (quilc) and the Quantum Virtual Machine (QVM), which are part of the Forest SDK, have been open sourced! In addition to downloading the binaries, you can now build these applications locally from source, or run them via the Docker images rigetti/quilc and rigetti/qvm. These Docker images are now used as the services in the GitLab CI build plan YAML (gh-792, gh-794, gh-795).

Improvements and Changes:

  • The WavefunctionSimulator now supports the use of parametric Quil programs, via the memory_map parameter for its various methods (gh-787).
  • Operator estimation data structures introduced in v2.2 have changed. Previously, ExperimentSettings had two members: in_operator and out_operator. The out_operator is unchanged, but in_operator has been renamed to in_state and its data type is now TensorProductState instead of PauliTerm. It was always an abuse of notation to interpret pauli operators as defining initial states. Analogous to the Pauli helper functions sI, sX, sY, and sZ, TensorProductState objects are constructed by multiplying together terms generated by the helper functions plusX, minusX, plusY, minusY, plusZ, and minusZ. This functionality enables process tomography and process DFE (gh-770).
  • Operator estimation now offers a “greedy” method for grouping tomography-like experiments that share a natural tensor product basis (ntpb), as an alternative to the clique cover version (gh-754).
  • The quilc endpoint for rewriting Quil parameter arithmetic has been changed from resolve_gate_parameter_arithmetic to rewrite_arithmetic (gh-802).
  • The difference between ProtoQuil and QPU-supported Quil is now better defined (gh-798).


  • Resolved an issue with post-gate noise in the pyQVM (gh-801).
  • A TypeError with a useful error message is now raised when a Program object is run on a QPU-backed QuantumComputer, rather than a confusing AttributeError (gh-799).

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.