# Reducing QPU Calls in Variational Quantum Algorithms via Classical Offloading of Gradient Computation

DevFeed: [Reducing QPU Calls in Variational Quantum Algorithms via Classical Offloading of Gradient Computation](<https://devfeed.tech/articles/reducing-qpu-calls-in-variational-quantum-algorithms-via-classical-offloading-of-gradient-computation-96782.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2503.16361>)

Author: Rahul Bhowmick, Harsh Wadhwa, Avinash Singh, Tania Sidana, Quoc Hoan Tran, Krishna Kumar Sabapathy

Published: 2026-10-23T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Quantum Computing](<https://devfeed.tech/topics/quantum-computing.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [classification](<https://devfeed.tech/tags/classification.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [diagnostics](<https://devfeed.tech/tags/diagnostics.md>), [gradient](<https://devfeed.tech/tags/gradient.md>), [hamiltonian](<https://devfeed.tech/tags/hamiltonian.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [helia](<https://devfeed.tech/tags/helia.md>), [offloading](<https://devfeed.tech/tags/offloading.md>), [pretraining](<https://devfeed.tech/tags/pretraining.md>), [quant-ph](<https://devfeed.tech/tags/quant-ph.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-algorithms](<https://devfeed.tech/tags/quantum-algorithms.md>), [quantum-computers](<https://devfeed.tech/tags/quantum-computers.md>), [quantum-hardware](<https://devfeed.tech/tags/quantum-hardware.md>), [qubit](<https://devfeed.tech/tags/qubit.md>), [qubits](<https://devfeed.tech/tags/qubits.md>), [reduction](<https://devfeed.tech/tags/reduction.md>), [stat-ml](<https://devfeed.tech/tags/stat-ml.md>), [training](<https://devfeed.tech/tags/training.md>), [variance](<https://devfeed.tech/tags/variance.md>)

## AI overview

HELIA combines a hardware-efficient ansatz supported by dynamical Lie algebras with two training methods that pair classical-enhanced gradient computation with the quantum Parameter-Shift Rule (PSR), distributing gradient estimation across classical and quantum hardware. In simulations, the methods were evaluated on VQE ground-state estimation for 6-18-qubit Hamiltonians and quantum phase classification up to 12 qubits. Compared with Full-PSR training of the same ansatz, VQE showed improved accuracy and success rates, with up to a 60% mean reduction in estimated logical quantum evaluations; classification's largest reported gain in mean peak test accuracy was 2.8 percentage points. The methods were also used for warm-start pretraining on Hamiltonians with exponential-dimensional dynamical Lie algebras. Gradient diagnostics showed slower gradient-variance decay than the selected deep hardware-efficient reference for the studied configurations and simulated sizes.

## Source excerpt

arXiv:2503.16361v3 Announce Type: replace-cross 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.