Distributed Variational Quantum Algorithm with Many-qubit for Optimization Challenges

2025-02-28

AI Summary

Existing quantum optimization algorithms suffer from poor scalability, noise sensitivity, and barren plateaus due to their strong reliance on entanglement. To address these limitations, this work proposes an entanglement-free variational quantum optimization algorithm (VQOA) for multi-qubit systems and its distributed variant (DVQOA). Methodologically, we introduce a novel multi-qubit (MQ) ansatz that leverages only quantum superposition—fully avoiding entanglement—and design a quantum-classical hybrid distributed architecture integrating variational circuits, native multi-qubit gates, adaptive hyperparameter optimization, and HPC cluster scheduling. Experimentally, the approach achieves over 50× speedup in metamaterial design tasks and successfully solves high-order N-ary optimization and quantum chemistry problems. It demonstrates verified scalability to the thousand-qubit regime and practical deployability on near-term hardware.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive reliance on entanglement. To address these limitations, we propose variational quantum optimization algorithm (VQOA), which leverages many-qubit (MQ) operations in an ansatz solely employing quantum superposition, completely avoiding entanglement. This ansatz significantly reduces circuit complexity, enhances noise robustness, mitigates Barren Plateau issues, and enables efficient partitioning for highly complex large-scale optimization. Furthermore, we introduce distributed VQOA (DVQOA), which integrates high-performance computing with quantum computing to achieve superior performance across MQ systems and classical nodes. These features enable a significant acceleration of material optimization tasks (e.g., metamaterial design), achieving more than 50$ imes$ speedup compared to state-of-the-art optimization algorithms. Additionally, DVQOA efficiently solves quantum chemistry problems and $ extit{N}$-ary $(N geq 2)$ optimization problems involving higher-order interactions. These advantages establish DVQOA as a highly promising and versatile solver for real-world problems, demonstrating the practical utility of the quantum-classical approach.

Problem

• Addresses scalability and accuracy in quantum optimization algorithms.
• Proposes a variational quantum optimization algorithm avoiding entanglement.
• Integrates quantum and classical computing for large-scale optimization tasks.

Innovation

• Variational quantum optimization algorithm avoids entanglement
• Distributed VQOA integrates quantum and classical computing
• Achieves 50x speedup in material optimization tasks