TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE

Yifeng Peng, Xinyi Li, Samuel Yen-Chi Chen, Kaining Zhang, Zhiding Liang, Ying Wang, Yuxuan Du

Stevens Institute of Technology· Wells Fargo· Nanyang Technological University· Rensselaer Polytechnic Institute

Barren Plateau Variational Quantum Algorithms Circuit Optimization Measurement Overhead VQE Parameter Freezing

Abstract

Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency deteriorates rapidly for large Hamiltonians. Two issues underlie this bottleneck:

the no-cloning theorem imposes a linear growth in circuit evaluations with the number of parameters per gradient step; and
deeper circuits encounter barren plateaus (BPs), leading to exponentially increasing measurement overheads.

To address these challenges, here we propose a deep learning framework, dubbed Titan, which identifies and freezes inactive parameters of a given ansätze at initialization for a specific class of Hamiltonians, reducing the optimization overhead without sacrificing accuracy. The motivation of Titan starts with our empirical findings that a subset of parameters consistently has negligible influence on training dynamics.

Its design combines a theoretically grounded data construction strategy, ensuring each training example is informative and BP-resilient, with an adaptive neural architecture that generalizes across ansätze of varying sizes.

Across benchmark transverse-field Ising models, Heisenberg models, and multiple molecule systems up to

30

qubits, Titan achieves up to

3 \times

faster convergence and

40

–

60%

fewer circuit evaluations than state-of-the-art baselines, while matching or surpassing their estimation accuracy. By proactively trimming parameter space, Titan lowers hardware demands and offers a scalable path toward utilizing VQE to advance practical quantum chemistry and materials science.