Non-Variational Quantum Random Access Optimization with Alternating Operator Ansatz

2025-02-06

AI Summary

Existing quantum random access optimization (QRAO) methods for industrial-scale combinatorial optimization rely on variational training, hindering scalability to near-term quantum hardware; moreover, quantum error correction overhead and qubit-count limitations impede practical deployment. Method: We propose the first non-variational QRAO framework, leveraging a fixed-parameter, alternating quantum operator ansatz inspired by QAOA—eliminating instance-dependent parameter optimization—and integrating QRAM for efficient problem encoding. We systematically evaluate mixer Hamiltonians, initial states, and operator implementation strategies to identify high-performance configurations. Contribution/Results: On MaxCut benchmarks, our method matches the solution quality of variational QRAO while drastically reducing runtime and resource overhead. It offers a scalable, low-overhead paradigm for QRAO deployment on early fault-tolerant quantum computers, advancing practical quantum optimization beyond variational paradigms.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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 and initial states, as well as QAOA operator implementations when customizing for QRAO, 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.

Problem

• Non-variational quantum optimization
• Quantum Random Access Optimization
• MaxCut problem solution

Innovation

• Non-variational QRAO approach
• Quantum Alternating Operator Ansatz
• Fixed parameters reduce optimization