Software Pipelining for Quantum Loop Programs

TL;DR

This paper introduces a novel method for optimizing quantum loop programs through software pipelining, significantly reducing program depth and size by exploiting parallelism across iterations.

Contribution

It redefines key concepts for quantum program optimization and presents the first quantum loop pipelining algorithm that outperforms existing in-loop optimization techniques.

Findings

Reduces total depth close to fully unrolled programs

Generates significantly smaller code

Outperforms baseline in loop optimization

Abstract

We propose a method for performing software pipelining on quantum for-loop programs, exploiting parallelism in and across iterations. We redefine concepts that are useful in program optimization, including array aliasing, instruction dependency and resource conflict, this time in optimization of quantum programs. Using the redefined concepts, we present a software pipelining algorithm exploiting instruction-level parallelism in quantum loop programs. The optimization method is then evaluated on some test cases, including popular applications like QAOA, and compared with several baseline results. The evaluation results show that our approach outperforms loop optimizers exploiting only in-loop optimization chances by reducing total depth of the loop program to close to the optimal program depth obtained by full loop unrolling, while generating much smaller code in size. This is the first step towards optimization of a quantum program with such loop control flow as far as we know.