Assertion-Based Optimization of Quantum Programs

TL;DR

This paper introduces an assertion-based optimization method for quantum programs that leverages subsystem entanglement annotations to improve resource efficiency, outperforming classical-inspired techniques especially across subroutine boundaries.

Contribution

It presents a formal, correct optimization approach using entanglement annotations, significantly reducing qubit requirements without manual intervention.

Findings

Reduced qubit requirements by 34x in benchmarks

Formal proof of correctness for the optimization methodology

Outperforms classical-inspired quantum optimization methods

Abstract

Quantum computers promise to perform certain computations exponentially faster than any classical device. Precise control over their physical implementation and proper shielding from unwanted interactions with the environment become more difficult as the space/time volume of the computation grows. Code optimization is thus crucial in order to reduce resource requirements to the greatest extent possible. Besides manual optimization, previous work has adapted classical methods such as constant-folding and common subexpression elimination to the quantum domain. However, such classically-inspired methods fail to exploit certain optimization opportunities across subroutine boundaries, limiting the effectiveness of software reuse. To address this insufficiency, we introduce an optimization methodology which employs annotations that describe how subsystems are entangled in order to exploit these optimization opportunities. We formalize our approach, prove its correctness, and present benchmarks: Without any prior manual optimization, our methodology is able to reduce, e.g., the qubit requirements of a 64-bit floating-point subroutine by $34\times$.