[Submitted on 20 Mar 2025 (v1), last revised 22 Oct 2026 (this version, v3)]
Abstract:Variational quantum algorithms (VQAs) are a promising approach for leveraging near-term quantum computers, but trainability and quantum-resource costs remain major challenges. Here we address these challenges with the textbf{H}ardware textbf{E}fficient and dynamical textbf{LI}e algebra supported textbf{A}nsatz (HELIA) and two training methods combining the classical-enhanced g method with the quantum-based Parameter-Shift Rule (PSR). This distributes gradient estimation and training across classical and quantum hardware. We evaluate ground-state estimation of 6- to 18-qubit Hamiltonians using the Variational Quantum Eigensolver (VQE) and quantum phase classification for Hamiltonians up to 12 qubits. For VQE, our methods improve accuracy and success rates, with a mean reduction in estimated logical quantum evaluations of up to $60%$ relative to Full-PSR training of the same ansatz. For classification, the largest improvement in mean peak test accuracy relative to Full-PSR is $2.8$ percentage points. We also use these methods as a warm-start pretraining strategy for Hamiltonians with exponential-dimensional dynamical Lie algebras. Gradient diagnostics for the studied Hamiltonian configurations show slower gradient-variance decay than the selected deep hardware-efficient reference over the simulated system sizes, potentially paving the way for training large-qubit quantum models.
Submission history
From: Harsh Wadhwa [view email]
[v1] Thu, 20 Mar 2025 17:17:58 UTC (2,487 KB)
[v2] Mon, 7 Jul 2025 08:24:45 UTC (2,286 KB)
[v3] Thu, 22 Oct 2026 06:00:55 UTC (2,577 KB)
