Distributed Quantum Approximate Optimization Algorithm on a Quantum-Centric Supercomputing Architecture

TL;DR

This paper introduces a distributed quantum approximate optimization algorithm (DQAOA) that enables large-scale combinatorial optimization on quantum-centric supercomputing architectures, demonstrating high accuracy and efficiency for real-world problems.

Contribution

The paper presents a novel distributed approach to QAOA that decomposes large problems for scalable quantum optimization, and integrates active learning for material science applications.

Findings

Successfully optimized 1,000-bit problems with ~99% approximation ratio.

Achieved short solution times (~276 seconds) outperforming existing methods.

Extended DQAOA with active learning to optimize photonic structures.

Abstract

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than necessitated to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1,000-bit problem) achieving a high approximation ratio ($\sim$99%) and short time-to-solution ($\sim$276 s), outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA's applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.