HAMMR-L: Noise Reduction in Quantum Outcomes Using a Richardson-Lucy Deconvolution Algorithm for Quantum State Graphs

TL;DR

HAMMR-L is a novel post-processing method that enhances quantum computer output fidelity by applying Richardson-Lucy deconvolution to measurement outcome graphs, outperforming existing methods.

Contribution

Introduces HAMMR-L, a circuit- and hardware-agnostic noise reduction technique using image deconvolution on quantum measurement graphs, showing superior performance over prior Hamming-based methods.

Findings

HAMMR-L outperforms QBEEP in fidelity improvement.

The method is circuit and hardware agnostic.

Potential for future enhancements is demonstrated.

Abstract

Current quantum computers present significant noise, especially as circuit depth and qubit count increase. Prior work has demonstrated that erroneous outcomes exhibit some behavior in Hamming space, enabling improvements in the output distributions of NISQ-era computers. We present HAMMR-L: a principled post-processing technique for improving the fidelity of output distributions by applying Richardson-Lucy image deconvolution on a state graph of measurement results connected by Hamming distance. We show that this preliminary implementation of HAMMR-L outperforms existing cutting-edge Hamming-based post-processors such as QBEEP while being circuit and hardware agnostic, which QBEEP is not. HAMMR-L also demonstrates clear potential for future improvements and we discuss how such improvements might be realized while highlighting the strengths, limitations, and generality of the underlying concept.