Assertion-Based Optimization of Quantum Programs

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 $34\times$.

Accepted version (OOPSLA 2020)

References in corpus (2)

• Q#: Enabling scalable quantum computing and development with a high-level domain-specific language
• Automated optimization of large quantum circuits with continuous parameters

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• Twist: Sound Reasoning for Purity and Entanglement in Quantum Programs
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• Quantum simulation of fermionic systems using hybrid digital-analog quantum computing approach