Alice & Bob Qiskit provider

This project contains a provider that allows access to Alice & Bob QPUs and emulators using the Qiskit framework.

Full documentation is available here and sample notebooks using the provider are available here.

Installation

You can install the provider using pip:

pip install qiskit-alice-bob-provider

pip will handle installing all the python dependencies automatically and you will always install the latest (and well-tested) version.

Remote execution on Alice & Bob QPUs: use your API key

To obtain an API key, get a Felis Cloud subscription on the Google Cloud Marketplace or contact Alice & Bob.

You can initialize the Alice & Bob remote provider using your API key locally with:

from qiskit_alice_bob_provider import AliceBobRemoteProvider
ab = AliceBobRemoteProvider('MY_API_KEY')

Where MY_API_KEY is your API key to the Alice & Bob API.

print(ab.backends())
backend = ab.get_backend('EMU:1Q:LESCANNE_2020')

The backend can then be used like a regular Qiskit backend:

from qiskit import QuantumCircuit

c = QuantumCircuit(1, 2)
c.initialize('+', 0)
c.measure_x(0, 0)
c.measure(0, 1)
job = backend.run(c)
res = job.result()
print(res.get_counts())

Local emulation of cat qubit processors

This project contains multiple emulators of multi cat qubit processors.

from qiskit_alice_bob_provider import AliceBobLocalProvider
from qiskit import QuantumCircuit, transpile

provider = AliceBobLocalProvider()
print(provider.backends())
# EMU:6Q:PHYSICAL_CATS, EMU:40Q:PHYSICAL_CATS, EMU:1Q:LESCANNE_2020

The EMU:nQ:PHYSICAL_CATS backends are theoretical models of quantum processors made up of physical cat qubits. They can be used to study the properties of error correction codes implemented with physical cat qubits, for different hardware performance levels (see the parameters of class PhysicalCatProcessor).

The EMU:1Q:LESCANNE_2020 backend is an interpolated model simulating the processor used in the seminal paper by Raphaël Lescanne in 2020. This interpolated model is configured to act as a digital twin of the cat qubit used in this paper. It does not represent the current performance of Alice & Bob's cat qubits.

The example below schedules and simulates a Bell state preparation circuit on a EMU:6Q:PHYSICAL_CATS processor, for different values of parameters average_nb_photons and kappa_2.

from qiskit_alice_bob_provider import AliceBobLocalProvider
from qiskit import QuantumCircuit, transpile

provider = AliceBobLocalProvider()

circ = QuantumCircuit(2, 2)
circ.initialize('0+')
circ.cx(0, 1)
circ.measure(0, 0)
circ.measure(1, 1)

# Default 6-qubit QPU with the ratio of memory dissipation rates set to
# k1/k2=1e-5 and cat size, average_nb_photons, set to 16.
backend = provider.get_backend('EMU:6Q:PHYSICAL_CATS')

print(transpile(circ, backend).draw())
# *Displays a timed and scheduled circuit*

print(backend.run(circ, shots=100000).result().get_counts())
# {'11': 49823, '00': 50177}

# Changing the cat size from 16 (default) to 4 and k1/k2 to 1e-2.
backend = provider.get_backend(
    'EMU:6Q:PHYSICAL_CATS', average_nb_photons=4, kappa_2=1e4
)
print(backend.run(circ, shots=100000).result().get_counts())
# {'01': 557, '11': 49422, '10': 596, '00': 49425}

Setting Up Development Environment (for contributors only)

You need uv. Then run:

uv sync                        # create .venv/ with dev dependencies
uv run pre-commit install      # install the git hooks
uv run pytest                  # run the tests

Commit messages follow Conventional Commits: release version numbers are computed from them.