Hardware-Aware Synthesis¶

Quantum computers differ from one other in many significant parameters, such as basis gates, connectivity, and error rates. The device specifications determine the possibility of executing the quantum program, and logically equivalent programs might require different implementations to optimize the probability of success.

The Classiq platform allows you to provide information about the hardware you want to use to run your quantum program. The synthesis engine takes the parameters of this hardware into account. For example, the engine could choose the implementation of a function that requires the least number of swaps, given the connectivity of the hardware.

Specifying a Backend¶

To synthesize your quantum program for a specific backend, specify the backend provider and the name of the backend.

The Classiq platform supports these backend providers:

Amazon Braket: All gate-based backends in Amazon Braket including all Rigetti devices, Lucy and IonQ Device.
Azure Quantum: ionq and quantinuum.
IBM Quantum: Those listed on IBM Quantum's official website. Note that you should specify the name of the backend without the ibmq_ prefix.

Textual ModelSDK

{
  "body": [],
  "preferences": {
    "backend_service_provider": "IBM Quantum",
    "backend_name": "ibmq_kolkata"
  }
}

from classiq.model import Preferences

from classiq import Model, set_preferences, synthesize

model = Model()
serialized_model = model.get_model()
preferences = Preferences(
    backend_service_provider="IBM Quantum", backend_name="ibmq_kolkata"
)
serialized_model = set_preferences(serialized_model, preferences)
synthesize(serialized_model)

Custom Hardware Settings¶

To synthesize the quantum program for hardware that is not available in the Classiq platform, you can specify the custom settings of the desirable hardware. This includes the basis gate set and the connectivity map of the hardware.

Note that all hardware parameters are optional.

Basis Gate Set¶

These are the allowed gates:

Single-qubit gates: u1, u2, u, p, x, y, z, t, tdg, s, sdg, sx, sxdg, rx, ry, rz, id, h
Basic two-qubit gates: cx, cy, cz
Extra two-qubit gates: swap, rxx, ryy, rzz, rzx, ecr, crx, cry, crz, csx, cu1, cu, cp, ch
Three-qubit gates: ccx, cswap

If no gates are specified, the default set consists of all single-qubit gates and the basic two-qubit gates.

Connectivity Map¶

The connectivity map is given by a list of pairs of qubit IDs. Each pair in the list means that a two-qubit gate (e.g., cx) can be performed on the pair of qubits. If the coupling map is symmetric, then both qubits can act as control. If the coupling map is asymmetric, then the first qubit can act only as control, and the second qubit can act only as target. To determine whether the provided map is symmetric, set the is_symmetric_connectivity argument.

If the connectivity map is not specified, the engine assumes full connectivity.

Example¶

The following example specifies a backend with 6 qubits in a 2-by-3 grid, where each qubit connects to its immediate neighbors. The backend uses four basis gates: cx, rz, sx, and x.

Textual ModelSDK

{
  "functions": [
    {
      "name": "main",
      "body": [{
        "function": "QFT",
        "function_params": { "num_qubits": 6 }
      }]
    }
  ],
  "preferences": {
    "custom_hardware_settings": {
      "basis_gates": ["cx", "rz", "sx", "x"],
      "connectivity_map": [[0, 1], [0, 3], [1, 4], [1, 2], [2, 5], [3, 4], [4, 5]],
      "is_symmetric_connectivity": true
    }
  }
}

from classiq.model import Preferences, CustomHardwareSettings

from classiq import Model, synthesize, set_preferences
from classiq.builtin_functions import QFT

model = Model()
qft_params = QFT(num_qubits=6)
model.QFT(params=qft_params)

custom_hardware_settings = CustomHardwareSettings(
    basis_gates=["cx", "rz", "sx", "x"],
    connectivity_map=[(0, 1), (0, 3), (1, 4), (1, 2), (2, 5), (3, 4), (4, 5)],
    is_symmetric_connectivity=True,
)
preferences = Preferences(custom_hardware_settings=custom_hardware_settings)

serialized_model = model.get_model()
serialized_model = set_preferences(serialized_model, preferences)
synthesize(serialized_model)