Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning

2026-06-22

AI Summary

This work addresses the challenges of unstable convergence and limited predictive performance in quantum models for temporal data processing by introducing, for the first time, a self-modulation mechanism into the quantum fast weight programmer. By dynamically balancing the injection of new information with the retention of historical memory, the proposed approach optimizes the learning and propagation of temporal dependencies. Integrating parameterized quantum circuits with sequence modeling techniques, the method significantly enhances both convergence stability and prediction accuracy across varying numbers of qubits and sequence lengths, thereby demonstrating its effectiveness and robustness in diverse settings.

Abstract

Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating Quantum Fast Weight Programmers, or Self-Modulating QFWP, which extends Quantum Fast Weight Programmers by introducing adaptive modulation over both newly generated fast-weight updates and historical fast-weight memory. Numerical results show that the proposed mechanism improves convergence stability and prediction performance across varying model settings, including different numbers of qubits and input sequence lengths. We further provide theoretical arguments explaining how self-modulation balances new information injection with memory retention, thereby enhancing temporal information propagation. These results suggest that Self-Modulating QFWP is a compact and effective framework for quantum machine learning on time-series data.

Problem

• quantum machine learning
• sequential learning
• fast-weight memory
• temporal information propagation
• adaptive modulation

Innovation

• Self-Modulating
• Quantum Fast Weight Programmers
• Adaptive Modulation
• Temporal Information Propagation
• Quantum Machine Learning