# PyQuil 1.9 is now available

7 June 2018

by Matt Harrigan

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.`PauliTerm.id()`

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 `Programs`

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`

.

*Originally published at **rigetticomputing.github.io** on June 7, 2018.*