qiskit-qrack-provider

qiskit-qrack-provider

This repository contains a Qrack provider for Qiskit. You must install PyQrack to use it.

Qrack is a GPU-accelerated simulator with optional pure-CPU and CPU/GPU-hybrid simulation, scaling from minimalist systems to multiple GPUs for high performance, with novel near-Clifford, Schmidt-decomposition, quantum binary decision diagram (QBDD), light-cone optimization, state vector, and other simulation techniques. This repository provides the Qrack QasmSimulator.

This provider is based on and adapted from work by the IBM Qiskit Team and QCGPU's creator, Adam Kelly. Attribution is noted in content files, where appropriate. Original contributions and adaptations were made by Daniel Strano of the VM6502Q/Qrack Team.

To use, in Qiskit:

from qiskit.providers.qrack import Qrack

backend = Qrack.get_backend('qasm_simulator')

For example, for use with unitaryfund/mitiq, creating a (noiseless) executor can be as simple as follows:

from qiskit.providers.qrack import Qrack

def executor(circuit, shots=1000):
    """Executes the input circuit and returns the noisy expectation value <A>, where A=|0><0|.
    """
    # Use the Qrack QasmSimulator backend, (but it's specifically noiseless)
    ideal_backend = Qrack.get_backend('qasm_simulator')

    # Append measurements
    circuit_to_run = circuit.copy()
    circuit_to_run.measure_all()

    # Run and get counts
    print(f"Executing circuit with {len(circuit)} gates using {shots} shots.")
    job = ideal_backend.run(circuit_to_run, shots=shots)
    counts = job.result().get_counts()

    # Compute expectation value of the observable A=|0><0|
    return counts["0"] / shots

Generally, you will need to adapt the above executor snippet to your particular purpose.

(Happy Qracking! You rock!)

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License: Apache License Version 2.0, Copyright (c) Daniel Strano and the Qrack contributors 2017-2024. All rights reserved.