Qubit-efficient quantum combinatorial optimization solver

TL;DR

This paper introduces a qubit-efficient quantum optimization algorithm that encodes solutions into entangled states of fewer qubits, enabling more effective use of limited quantum hardware for combinatorial problems.

Contribution

It proposes a novel variational quantum circuit that reduces qubit requirements by mapping solutions to entangled states, improving quantum optimization scalability.

Findings

The ansatz shows parameter concentration properties.

Performance guarantees are derived for the proposed method.

Potential benefits for near-term quantum devices.

Abstract

Quantum optimization solvers typically rely on one-variable-to-one-qubit mapping. However, the low qubit count on current quantum computers is a major obstacle in competing against classical methods. Here, we develop a qubit-efficient algorithm that overcomes this limitation by mapping a candidate bit string solution to an entangled wave function of fewer qubits. We propose a variational quantum circuit generalizing the quantum approximate optimization ansatz (QAOA). Extremizing the ansatz for Sherrington-Kirkpatrick spin glass problems, we show valuable properties such as the concentration of ansatz parameters and derive performance guarantees. This approach could benefit near-term intermediate-scale and future fault-tolerant small-scale quantum devices.