Non-Variational Quantum Random Access Optimization with Alternating Operator Ansatz

TL;DR

This paper introduces a non-variational Quantum Random Access Optimization approach using the Quantum Alternating Operator Ansatz, enabling scalable quantum optimization without the need for instance-specific parameter training.

Contribution

It proposes and benchmarks a non-variational QRAO method based on QAOA with fixed parameters, simplifying implementation and improving scalability.

Findings

Fixed parameters perform well across instances

Different design choices impact performance

Strategy identified for practical QRAO execution

Abstract

Solving hard optimization problems is one of the most promising application domains for quantum computers due to the ubiquity of such problems in industry and the availability of broadly applicable quantum speedups. However, the ability of near-term quantum computers to tackle industrial-scale optimization problems is limited by their size and the overheads of quantum error correction. Quantum Random Access Optimization (QRAO) has been proposed to reduce the space requirements of quantum optimization. However, to date QRAO has only been implemented using variational algorithms, which suffer from the need to train instance-specific variational parameters, making them difficult to scale. We propose and benchmark a non-variational approach to QRAO based on the Quantum Alternating Operator Ansatz (QAOA) for the MaxCut problem. We show that instance-independent ``fixed" parameters achieve good performance, removing the need for variational parameter optimization. Additionally, we evaluate different design choices, such as various mixers, initial states, and QRAO-specific implementations of the QAOA cost operator, and identify a strategy that performs well in practice. Our results pave the way for the practical execution of QRAO on early fault-tolerant quantum computers.