Programming Variational Quantum Circuits with Quantum-Train Agent

TL;DR

This paper introduces the QT-QFWP framework that efficiently programs variational quantum circuits using quantum-driven parameter updates, significantly reducing parameters while maintaining accuracy in time-series prediction tasks.

Contribution

The study presents a novel quantum-driven programming framework for VQCs that improves scalability and efficiency over traditional hybrid models, especially for near-term quantum systems.

Findings

Reduces parameters by 70-90% compared to QLSTM and QFWP.

Outperforms related models in efficiency and accuracy.

Demonstrates effectiveness on multiple time-series prediction tasks.

Abstract

In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90\% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight Programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains.