pytket-quantinuum#

pytket-quantinuum is an extension to pytket that allows pytket circuits to be executed on Quantinuum’s quantum devices.

pytket-quantinuum is available for Python 3.10, 3.11 and 3.12, on Linux, MacOS and Windows. To install, run:

pip install pytket-quantinuum

Note

Running circuits remotely on the QuantinuumBackend requires a Quantinuum account. The user will be prompted for their login credentials when making API calls such as calling the process_circuits method or querying backend_info.

Web Interface: https://um.qapi.quantinuum.com/

Available Backends#

The pytket-quantinuum extension allows the user to access the following quantum devices, emulators and syntax checkers. These backends can be initialised by passing the device name as a string to the QuantinuumBackend class. The available devices are:

  • H1-1, H2-1: Quantum devices, submit specifically to H1-1 or H2-1 by using the device name.

  • H1-1E, H2-1E: Device-specific emulators of H1-1 and H2-1. These emulators run remotely on servers and require credentials.

  • H1-1SC, H2-1SC: Device-specific syntax checkers. These check compilation of a quantum circuit against device-specific instructions, and return status “completed” if the syntax is correct (along with the H-Series Quantum Credits (HQCs)), or status “failed” if the syntax is incorrect (along with the error).

  • H1-1LE, a version of the H1-1E emulator that runs locally. For running simulations locally see the docs on Local Emulators.

There are also optional initialisation parameters label (for naming circuits), group (identifier for a collection of jobs) and simulator (see below).

The H-series devices and emulators produce shots-based results and therefore require measurements. It is also possible to use a stabilizer simulator by specifying simulator='stabilizer'. This option may be preferable for simulating Clifford circuits.

By default the emulators use noise models based on the real devices. It is possible to perform a noiseless simulation by specifying noisy_simulation=False.

For examples demonstrating the QuantinuumBackend see the example notebooks .

Default Compilation#

Every Backend in pytket has its own default_compilation_pass method. This method applies a sequence of optimisations to a circuit depending on the value of an optimisation_level parameter. This default compilation will ensure that the circuit meets all the constraints required to run on the Backend.

The default pass can be applied in place as follows

from pytket import Circuit
from pytket.extensions.quantinuum import QuantinuumBackend

circ = Circuit(2).H(0).CX(0, 1).CZ(0, 1)
backend = QuantinuumBackend('H1-1E')

# Compile the circuit in place. The optimisation level is set to 2 by default.
backend.default_compilation_pass().apply(circ)

Alternatively the default pass can be applied using the get_compiled_circuit method.

compiled_circ = backend.get_compiled_circuit(circ)

The passes applied by different levels of optimisation are specified in the table below.

Default compilation pass for the QuantinuumBackend#

optimisation_level = 0

optimisation_level = 1

optimisation_level = 2 [1]

DecomposeBoxes

DecomposeBoxes

DecomposeBoxes

self.rebase_pass [2]

SynthesiseTket

FullPeepholeOptimise [3]

FlattenRelabelRegistersPass

NormaliseTK2

NormaliseTK2

DecomposeTK2

DecomposeTK2

self.rebase_pass [2]

self.rebase_pass [2]

ZZPhaseToRz

RemoveRedundancies

RemoveRedundancies

auto_squash_pass [4]

auto_squash_pass [4]

FlattenRelabelRegistersPass

FlattenRelabelRegistersPass

RemoveRedundancies

RemoveRedundancies

  • [1] If no value is specified then optimisation_level defaults to a value of 2.

  • [2] self.rebase_pass is a rebase that converts the circuit to the Quantinuum native gate set (e.g. {Rz, PhasedX, ZZMax, ZZPhase}).

  • [3] FullPeepholeOptimise has the argument target_2qb_gate=OpType.TK2.

  • [4] auto_squash_pass has arguments auto_squash_pass({OpType.PhasedX, OpType.Rz})

Note

If optimisation_level = 0 the device constraints are solved but no additional optimisation is applied. Setting optimisation_level = 1 applies some light optimisations to the circuit. More intensive optimisation is applied by level 2 at the expense of increased runtime.

Note

The pass ZZPhaseToRz is left out of optimisation_level=2 as the passes applied by FullPeepholeOptimise will already cover these optimisations.

Target Two-Qubit Gate#

Backends may offer several alternatives as the native two-qubit gate: the current possibilities are ZZMax, ZZPhase and TK2. The set of supported gates may be queried using the QuantinuumBackend.two_qubit_gate_set property. Each device also has a default two-qubit gate, which may be queried using the QuantinuumBackend.default_two_qubit_gate property. Currently, the default two-qubit gate for all devices is ZZPhase.

The default compilation pass and rebase pass will target the default gate by default. This may be overridden using the method QuantinuumBackend.set_compilation_config_target_2qb_gate() or by passing a QuantinuumBackendCompilationConfig when constructing the backend.

If desired, backends may be allowed to rebase to a different two-qubit gate judged to have better fidelity before being run. The default is to run with the gate provided in the submitted circuit, but this behaviour may be overridden using the optional allow_2q_gate_rebase argument to process_circuit(), process_circuits() or submit_program().

Device Predicates#

Circuits must satisfy the following predicates in order to run on the QuantinuumBackend.

  • NoSymbolsPredicate: Parameterised gates must have numerical parameters when the circuit is executed.

  • GateSetPredicate: To view supported Ops run QuantinuumBackend.backend_info.gate_set.

  • MaxNQubitsPredicate: H1-1, H1-1E and H1-1SC all support a maximum of 20 qubits. The H2-1, H2-1E and H2-1SC support a maximum of 32 qubits.

Job Statuses#

When using the Quantinuum Backend to run circuits there are several possible circuit statuses.

  • queued - The job has been queued but has not yet been run.

  • running - The circuit is currently being run on the device/emulator.

  • completed - The job has finished.

  • failed - The job has failed.

  • cancelling - The job is in the process of being cancelled.

  • cancelled - The job has been cancelled.

The status of the job can be checked with by using the circuit_status method. To cancel a job simply use the cancel method and supply the job handle as a parameter.

Additional Backend Capabilities#

The Quantinuum Backend has a cost method. This calculates the cost (in HQCs) required to execute the circuit for the specified number of shots.

Every Quantinuum Backend supports mid-circuit measurements and fast classical feedforward.

The process_circuits method for the QuantinuumBackend accepts the following additional keyword arguments.

  • postprocess : boolean flag to allow classical postprocessing.

  • noisy_simulation : boolean flag to specify whether the simulator should perform noisy simulation with an error model (default value is True).

  • group : string identifier of a collection of jobs, can be used for usage tracking.

For the Quantinuum Backend, process_circuits returns a ResultHandle object containing a job_id and a postprocessing ( ppcirc) circuit if there is one.

The logout() method clears stored JSON web tokens and the user will have to sign in again to access the Quantinuum API.

Partial Results Retrieval#

The QuantinuumBackend also supports giving the user partial results from unfinished jobs. This can be done as follows.

from pytket.extensions.quantinuum import QuantinuumBackend

# Submit circuit to QuantinuumBackend
backend = QuantinuumBackend('H1-1')
compiled_circ = backend.get_compiled_circuit(circ) # circ defined elsewhere
handle = backend.process_circuit(compiled_circ, n_shots=3000)

# Retrieve partial shots:counts from the handle of an unfinished job
partial_result, job_status = backend.get_partial_result(handle)
print(partial_result.get_counts())

This feature has a number of potential use cases.

Firstly partial results can be used to diagnose potential faults with submitted jobs. For instance if the partial results look worse than expected this could indicate a bug in the submitted circuit(s). If this is the case the user may want to cancel the job to avoid using machine time and resubmit once the issue is resolved. If partial results indicate that a job is taking longer run than anticipated then the user can cancel the job and consider redesigning their experiment.

Also partial results enable users to quickly validate basic execution for very large jobs which may take days to complete.

Leakage Gadget Detection#

When running circuits on the :py:class:QuantinuumBackend, one source of error is “leakage”, where with some small probability a qubit will experience leakage into electronic states outside the qubit subspace. When this occurs, none of the remaining gates in the circuit will have any effect and so this leads to erroneous results. Such leakage errors can be detected at the circuit level by running a special circuit gadget between a data qubit and an ancilla qubit. We can then discard shots where a leakage error is detected using prune_shots_detected_as_leaky(). For a more detailed explanation we refer to Eliminating Leakage Errors in Hyperfine Qubits by D. Hayes, D. Stack, B. Bjork, A. C. Potter, C. H. Baldwin and R. P. Stutz and the corresponding notebook tutorial.

Batching#

Quantinuum backends (except syntax checkers) support batching of jobs (circuits). To create a batch of jobs, users submit the first job, then signal that subsequent jobs should be added to the same batch using the handle of the first. The backend queue management system will start the batch as soon as the first job reaches the front of the queue and ensure subsequent batch jobs are run one after the other, until the end of the batch is reached or there are no new jobs added to the batch for ~1 min (at which point the batch expires and any subsequent jobs will be added to the standard queue).

The standard process_circuits method no longer batches by default. To use batching first start the batch with start_batch, which has a similar interface to process_circuit but with an extra first argument max_batch_cost:

h1 = backend.start_batch(max_batch_cost=300, circuit=circuit, n_shots=100)

Add to the batch with subsequent calls of add_to_batch which takes as first argument the handle of the first job of the batch, and has the optional keyword argument batch_end to signal the end of a batch (default False).

h2 = backend.add_to_batch(h1, circuit_2, n_shots=100)
h3 = backend.add_to_batch(h1, circuit_3, n_shots=100, batch_end=True)

The batch feature on Quantinuum systems gives users the ability to create “ad-hoc” reservations. Circuits submitted together in a batch will run at one time. The benefit to users is that once a batch hits the front of the queue, jobs in a batch will run uninterrupted until they are completed.

Once a batch is submitted, jobs can continue to be added to the batch, ending either when the user signifies the end of a batch or after 1 minute of inactivity. Batches cannot exceed the maximum limit of 500 H-System Quantum Credits (HQCs) total.

If the total HQCs for jobs in a batch hit this limit or a smaller limit set by the user, those jobs will not be cancelled. Instead, they will continue to run as regular jobs in the queue instead of as a batch.

Local Emulators#

If pytket-quantinuum is installed with the pecos option:

pip install pytket-quantinuum[pecos]

then it is possible to run circuits on an emulator running on the local machine instead of using the remote emulator.

For example, the “H1-1” device would have a counterpart device called “H1-1LE”. Running circuits on this device would be similar to using the “H1-1” device or the remote emulator (“H1-1E”), but would not incur any cost in HQCs (and for small circuits would typically be faster).

Currently this emulation is noiseless, so if noisy emulation is required it is still necessary to use the remote emulators (such as “H1-1E”).

A few of the QuantinuumBackend methods (submit_program(), cancel(), and get_partial_result()) are not available for local-emulator backends.

Normally using the local emulator requires one initial online API query to retrieve the device information. To use it completely offline (with the caveat that this relies on hard-coded assumptions about the available devices), you can use the QuantinuumAPIOffline when constructing the backend:

from pytket.extensions.quantinuum import QuantinuumBackend, QuantinuumAPIOffline
api_offline = QuantinuumAPIOffline()
backend = QuantinuumBackend(device_name="H1-1LE", api_handler = api_offline)