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Qubit Mapping package and tools

Project description

Qubit Mapping for NISQ-Era Quantum Devices

Introduction

The goal of this project was to implement the paper Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices by Gushu Li, Yufei Ding, and Yuan Xie.

Purpose

Due to limited connections between physical qubits, most two-qubit gates cannot be directly implemented on Noisy Intermediate-Scale Quantum (NISQ) devices. A dynamic remapping of logical to physical qubits is needed to enable execution of two qubit gates in a quantum algorithm on a NISQ device. This project implements a SWAP-based BidiREctional heuristic search algorithm (SABRE), proposed in the given paper that is applicable to NISQ devices with arbitrary qubit connections

Problem Statement

Given an input quantum circuit and the coupling graph of a quantum device, find an initial mapping and the intermediate qubit mapping transition (by inserting SWAPs) to satisfy all two-qubit constraints and try to minimize the number of additional gates and circuit depth in the final hardware-compliant circuit.

References

Usage

An example of how to use this package is illustrated in example.py.

  • Construct a pyquil program:
    from pyquil import Program
    from pyquil.gates import CNOT, Gate, H, SWAP
    original_circuit = Program()
    original_circuit.inst(CNOT(0, 1))
    original_circuit.inst(CNOT(2, 3))
    original_circuit.inst(CNOT(1, 3))
    original_circuit.inst(CNOT(1, 2))
    original_circuit.inst(CNOT(2, 3))
    original_circuit.inst(CNOT(0, 3))
    
  • Define a coupling graph (the coupling graph can also be a predefined one based on the underlying chip architecture):
    import networkx as nx
    coupling_graph = nx.Graph()
    coupling_graph.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
    
  • Apply preprocessing on the circuit and the coupling graph to generate a random initial mapping and a distance matrix:
    from sabre_tools.circuit_preprocess import preprocess_input_circuit, get_initial_mapping, get_distance_matrix
    initial_mapping = get_initial_mapping(circuit=original_circuit, coupling_graph=coupling_graph)
    distance_matrix = get_distance_matrix(coupling_graph=coupling_graph)
    
  • Execute the SABRE algorithm on the circuit in forward-backward-forward passes where final mapping output of each pass is provided as the initial mapping of the reverse circuit in the next pass
    from sabre_tools.sabre import SABRE
    for iteration in range(3):
    front_layer_gates, circuit_dag = preprocess_input_circuit(circuit=temp_circuit)
    final_program, final_mapping = sabre_proc.execute_sabre_algorithm(front_layer_gates = front_layer_gates, qubit_mapping = temp_mapping, circuit_dag = circuit_dag)
    
    reversed_ins = reversed(temp_circuit.instructions)
    temp_circuit = Program()
    for ins in reversed_ins:
        temp_circuit.inst(ins)
    temp_mapping = final_mapping.copy()
    
  • To check if SABRE algorithm was able to insert SWAPs in the circuit so that all 2-qubit gates were executed successfully, call the rewiring_correctness() function:
    forbidden_gates = sabre_proc.rewiring_correctness(final_program, final_mapping)
    if forbidden_gates:
        print("", forbidden_gates)
    else:
        print("All gates have been executed")
    
    This function scans the logical to physical qubit mapping and the SWAP inserted circuit to determine if there are gates are not executable and returns the non-executable gate if true, otherwise returns an empty dictionary. This function can also be used to check if the original circuit requires the use of SABRE in the first place
  • Count the number of 2 qubit gates in the original or final circuit to determine the circuit depth and number of gates:
    two_qubit_gate_count = sabre_proc.cnot_count(program)
    

Future Scope

This project has been developed using Rigetti's quantum programming framework Pyquil. A future scope of this project is to make it platform independent so that SABRE can be applied to a quantum program written in any framework. Another possible scope of research is to implement other algorithms in this field and perform a comparison based on number of gates reduction, scalability, runtime speedup, algorithm performance on large circuits etc.

QC Mentorship Program

This project has been initiated and completed as part of the QC Mentorship Program under Quantum Open Source Foundation (QOSF) in collaboration with Unitary Fund.

This work has been completed with constant guidance and motivation by my mentor Petar Korponaić (LinkedIn).

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