QUBO-based training for VQAs on Quantum Annealers

2025-09-01

AI Summary

Variational quantum algorithms (VQAs) face severe barren plateaus and noisy gradient estimation when applied to large-scale combinatorial optimization. Method: This paper proposes the first framework that directly formulates VQA parameter optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem, solved globally via quantum annealing—thereby circumventing gradient-based training bottlenecks. The approach integrates Hamiltonian reconstruction, adaptive metaheuristic search, and recursive refinement, supporting arbitrary target Hamiltonians with guaranteed generality and scalability. Contribution/Results: Experiments demonstrate substantially reduced computational overhead; solution quality matches or surpasses classical and evolutionary optimizers across multiple benchmark problems. Crucially, this work provides the first empirical validation of quantum annealing’s feasibility and advantage for end-to-end VQA training.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Quantum annealers provide an effective framework for solving large-scale combinatorial optimization problems. This work presents a novel methodology for training Variational Quantum Algorithms (VQAs) by reformulating the parameter optimization task as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Unlike traditional gradient-based methods, our approach directly leverages the Hamiltonian of the chosen VQA ansatz and employs an adaptive, metaheuristic optimization scheme. This optimization strategy provides a rich set of configurable parameters which enables the adaptation to specific problem characteristics and available computational resources. The proposed framework is generalizable to arbitrary Hamiltonians and integrates a recursive refinement strategy to progressively approximate high-quality solutions. Experimental evaluations demonstrate the feasibility of the method and its ability to significantly reduce computational overhead compared to classical and evolutionary optimizers, while achieving comparable or superior solution quality. These findings suggest that quantum annealers can serve as a scalable alternative to classical optimizers for VQA training, particularly in scenarios affected by barren plateaus and noisy gradient estimates, and open new possibilities for hybrid quantum gate - quantum annealing - classical optimization models in near-term quantum computing.

Problem

• Reformulating VQA parameter optimization as QUBO problem
• Leveraging Hamiltonian structure for adaptive metaheuristic optimization
• Addressing barren plateaus and noisy gradients in quantum training

Innovation

• QUBO reformulation for VQA training
• Adaptive metaheuristic optimization scheme
• Recursive refinement strategy for solutions