Optimal qubit assignment and routing via integer programming

TL;DR

This paper presents an integer programming approach to optimize qubit assignment and routing in quantum circuits, improving fidelity and reducing errors on hardware with limited connectivity.

Contribution

It introduces a network flow-based integer linear programming model that jointly optimizes qubit placement, routing, and multiple cost functions, including fidelity and cross-talk.

Findings

Reduces CNOT count compared to SABRE.

Improves circuit fidelity on hardware.

Simultaneously optimizes error rate and depth.

Abstract

We consider the problem of mapping a logical quantum circuit onto a given hardware with limited two-qubit connectivity. We model this problem as an integer linear program, using a network flow formulation with binary variables that includes the initial allocation of qubits and their routing. We consider several cost functions: an approximation of the fidelity of the circuit, its total depth, and a measure of cross-talk, all of which can be incorporated in the model. Numerical experiments on synthetic data and different hardware topologies indicate that the error rate and depth can be optimized simultaneously without significant loss. We test our algorithm on a large number of quantum volume circuits, optimizing for error rate and depth; our algorithm significantly reduces the number of CNOTs compared to Qiskit's default transpiler SABRE, and produces circuits that, when executed on hardware, exhibit higher fidelity.