TensoMeta-VQC: A Tensor-Train-Guided Meta-Learning Framework for Robust and Scalable Variational Quantum Computing

2025-08-01

AI Summary

Variational quantum computing (VQC) faces scalability challenges due to barren plateaus and hardware noise. To address this, we propose a tensor-network-guided meta-learning framework that decouples classical optimization from quantum hardware: (i) parameterizing quantum circuits via tensor-train (TT) networks to mitigate gradient vanishing; (ii) incorporating low-rank structural constraints to enhance noise resilience; and (iii) establishing rigorous theoretical guarantees on optimization convergence and function approximation capacity using the neural tangent kernel (NTK) and statistical learning theory. Evaluated on quantum dot classification, Max-Cut, and molecular simulation tasks, our end-to-end quantum-classical co-design framework significantly outperforms state-of-the-art baselines—achieving improved training stability, generalization, and robustness. This work provides a scalable, theoretically grounded paradigm for practical VQC on near-term noisy intermediate-scale quantum (NISQ) devices.

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
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Variational Quantum Computing (VQC) faces fundamental barriers in scalability, primarily due to barren plateaus and quantum noise sensitivity. To address these challenges, we introduce TensoMeta-VQC, a novel tensor-train (TT)-guided meta-learning framework designed to improve the robustness and scalability of VQC significantly. Our framework fully delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware. This innovative parameterization mitigates gradient vanishing, enhances noise resilience through structured low-rank representations, and facilitates efficient gradient propagation. Based on Neural Tangent Kernel and statistical learning theory, our rigorous theoretical analyses establish strong guarantees on approximation capability, optimization stability, and generalization performance. Extensive empirical results across quantum dot classification, Max-Cut optimization, and molecular quantum simulation tasks demonstrate that TensoMeta-VQC consistently achieves superior performance and robust noise tolerance, establishing it as a principled pathway toward practical and scalable VQC on near-term quantum devices.

Problem

• Addresses scalability and noise issues in Variational Quantum Computing
• Mitigates gradient vanishing and enhances noise resilience
• Improves performance in quantum tasks like classification and simulation

Innovation

• Classical tensor-train network generates circuit parameters
• Structured low-rank representations enhance noise resilience
• Neural Tangent Kernel ensures stable optimization performance