Execution on Alice and Bob Quantum Cloud

The Classiq executor supports execution on Alice and Bob logical hardwares.

Usage

Alice and Bob provide multiple logical targets that we can use. Execution on Alice and Bob requires a valid API key. Additionally, Alice and Bob introduces several customizable hardware parameters (Available through SDK only):

average_nb_photons

Bit-flip probability decreases exponentially with this parameter, phase-flip probability increases linearly.

kappa_1

The rate at which the cat qubit loses one photon, creating a bit-flip. Lower values mean lower error rates.

kappa_2

The rate at which the cat qubit is stabilized using two-photon dissipation. Higher values mean lower error rates.

distance

The number of times information is duplicated in the repetition code. Phase-flip probability decreases exponentially with this parameter, bit-flip probability increases linearly.

Execution

Setting up Backend Preferences

from classiq import AliceBobBackendNames, AliceBobBackendPreferences

backend_preferences = AliceBobBackendPreferences(
    backend_service_provider="Alice & Bob",
    api_key="<API KEY>",
    backend_name=AliceBobBackendNames.LOGICAL_TARGET,
    kappa_1=10.0,  # OR Remove it to keep default value
    kappa_2=1000,  # OR Remove it to keep default value
    distance=3,  # OR Remove it to keep default value
    average_nb_photons=7,  # OR Remove it to keep default value
)

Setting up execution preferences and adding number of shots

from classiq import ExecutionPreferences

execution_preferences = ExecutionPreferences(
    num_shots=1000,
    backend_preferences=backend_preferences,
)

Prepare for Hardware-aware synthesis

from classiq import Preferences

preferences = Preferences(
    backend_service_provider=ProviderVendor.ALICE_AND_BOB,
    backend_name=AliceBobBackendNames.LOGICAL_TARGET,
)

Execute

At this point you should just create your model, set execution preferences and perform Hardware-aware synthesize then execute.

quantum_program = synthesize(your_model, preferences=preferences)
quantum_program = set_quantum_program_execution_preferences(
    quantum_program, execution_preferences
)

result = execute(quantum_program).result()

Supported Backends

“LOGICAL_EARLY”
“LOGICAL_TARGET”
“LOGICAL_NOISELESS”

Supported Cloud Providers Execution on Amazon Braket Cloud

⌘I

​Usage

​average_nb_photons

​kappa_1

​kappa_2

​distance

​Execution

​Setting up Backend Preferences

​Setting up execution preferences and adding number of shots

​Prepare for Hardware-aware synthesis

​Execute

​Supported Backends