Suppressing quantum circuit errors due to system variability

TL;DR

This paper introduces a quantum circuit optimization method that accounts for system variability, improving fidelity by nearly 40% through heuristic cost functions and multi-processor qubit placement, with minimal overhead.

Contribution

The authors develop a post-compilation optimization technique that leverages calibration data to select better qubits, enhancing quantum circuit fidelity across noisy platforms.

Findings

Recovered nearly 40% of missing fidelity using heuristic cost functions.

Achieved additional gains by considering qubit placement across multiple processors.

Overhead of the method is minimal compared to other compilation steps.

Abstract

We present a quantum circuit optimization technique that takes into account the variability in error rates that is inherent across present day noisy quantum computing platforms. This method can be run post qubit routing or post-compilation, and consists of computing isomorphic subgraphs to input circuits and scoring each using heuristic cost functions derived from system calibration data. Using an independent standard algorithmic test suite we show that it is possible to recover on average nearly 40% of missing fidelity using better qubit selection via efficient to compute cost functions. We demonstrate additional performance gains by considering qubit placement over multiple quantum processors. The overhead from these tools is minimal with respect to other compilation steps, such as qubit routing, as the number of qubits increases. As such, our method can be used to find qubit mappings for problems at the scale of quantum advantage and beyond.