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

2024-07-29

AI Summary

The Quantum Approximate Optimization Algorithm (QAOA) suffers from poor scalability in large-scale combinatorial optimization due to stringent quantum hardware resource constraints. Method: This paper proposes the Distributed QAOA (DQAOA), a classical-quantum hybrid framework deployed on a quantum-centric supercomputing architecture. DQAOA decomposes large problems into subtasks processed in parallel by heterogeneous resources, iteratively aggregating local solutions to update the global optimum. Further, it integrates active learning with machine learning to form the AL-DQAOA closed-loop framework, extending applicability to materials design—specifically photonic structure optimization. Contribution/Results: DQAOA achieves near-optimal performance on 1000-qubit instances, attaining an approximation ratio of ~99% within ~276 seconds. It significantly outperforms state-of-the-art methods in photonic inverse design. This work marks the first deep integration of DQAOA with quantum-centric supercomputing, establishing a scalable paradigm for quantum-classical hybrid computing.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Distributed QAOA addresses scalability in quantum optimization.
• DQAOA decomposes large problems into smaller, manageable tasks.
• AL-DQAOA integrates machine learning for materials science optimization.

Innovation

• Distributed QAOA decomposes large problems into smaller tasks
• Combines high-performance and quantum computing resources
• Integrates active learning with DQAOA for material optimization