TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing

TL;DR

TensorHyper-VQC introduces a tensor-train-guided hypernetwork framework that enhances the robustness and scalability of variational quantum computing by decoupling parameter generation from quantum hardware and improving noise resilience.

Contribution

It proposes a novel tensor-train-guided hypernetwork approach that significantly improves VQC robustness and scalability, with theoretical guarantees and hardware validation.

Findings

Achieves superior performance across multiple quantum tasks.

Demonstrates robustness to quantum noise and hardware imperfections.

Validated on a 156-qubit IBM quantum processor.

Abstract

Variational Quantum Computing (VQC) faces fundamental scalability barriers, primarily due to barren plateaus and sensitivity to quantum noise. To address these challenges, we introduce TensorHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework that significantly improves the robustness and scalability of VQC. Our framework fully delegates the generation of quantum-circuit parameters to a classical TT network, thereby decoupling optimization from quantum hardware. This innovative parameterization mitigates gradient vanishing, enhances noise resilience through structured low-rank representations, and facilitates efficient gradient propagation. Grounded in 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 TensorHyper-VQC consistently achieves superior performance and robust noise tolerance, including hardware-level validation on a 156-qubit IBM Heron processor. These results position TensorHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.