Peephole Optimization for Quantum Approximate Synthesis

TL;DR

This paper introduces improved peephole optimization techniques for quantum circuits that incorporate error awareness and better approximation methods, leading to significant reductions in divergence metrics on benchmark circuits.

Contribution

The paper presents novel enhancements to quantum circuit peephole optimization, notably error-aware approximation and improved correctness estimation methods.

Findings

Average reduction of 18.2% in Total Variational Distance

Average reduction of 15.8% in Jensen-Shannon Divergence

Outperforms existing solutions with 11.4% and 9.0% improvements

Abstract

Peephole optimization of quantum circuits provides a method of leveraging standard circuit synthesis approaches into scalable quantum circuit optimization. One application of this technique partitions an entire circuit into a series of peepholes and produces multiple approximations of each partitioned subcircuit. A single approximation of each subcircuit is then selected to form optimized result circuits. We propose a series of improvements to the final phase of this architecture, which include the addition of error awareness and a better method of approximating the correctness of the result. We evaluated these proposed improvements on a set of benchmark circuits using the IBMQ FakeWashington simulator. The results demonstrate that our best-performing method provides an average reduction in Total Variational Distance (TVD) and Jensen-Shannon Divergence (JSD) of 18.2% and 15.8%, respectively, compared with the Qiskit optimizer. This also constitutes an improvement in TVD of 11.4% and JSD of 9.0% over existing solutions.