Training Hybrid Deep Quantum Neural Network for Reinforcement Learning Efficiently

• URL: http://arxiv.org/abs/2503.09119v3
• Date: Tue, 08 Apr 2025 04:28:22 GMT
• Title: Training Hybrid Deep Quantum Neural Network for Reinforcement Learning Efficiently
• Authors: Jie Luo, Xueyin Chen,
• Abstract summary: Quantum machine learning (QML) emerged recently as a novel interdisciplinary research direction.<n>Recent works on hybrid QML models, compatible with noisy intermediate-scale quantum computers, have hinted at improved performance.<n>We present a scalable QML architecture that overcomes challenges and demonstrates efficient batch optimization through PQC blocks.
• Score: 2.7812018782449073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computing offers a new paradigm for computation, exploiting an exponentially growing Hilbert space for data representation and operation. Results are obtained from sampling over qubit state distributions that can have complex correlations from entanglement produced by the quantum computing process. Quantum machine learning (QML) emerged recently as a novel interdisciplinary research direction, developing novel machine learning architectures with quantum blocks. While large-scale fault-tolerant quantum machines are not yet available, recent works on hybrid QML models, compatible with noisy intermediate-scale quantum computers, have hinted at improved performance. Such hybrid deep quantum neural networks (hDQNNs) integrate GPU/CPU-based deep neural networks (DNNs) with parameterized quantum circuits (PQC) that can be straightforwardly executed on quantum processors. However, efficiently training hDQNNs using quantum hardware compatible batch backpropagation through PQCs was unavailable, limiting hDQNNs' scalability and usefulness for complex modern machine-learning tasks. Here, we present a scalable QML architecture that overcomes these challenges and demonstrates efficient batch optimization through PQC blocks to update associated model DNNs, enabling scalable hDQNN training compatible with physical quantum computers. Applied to the high-dimensional complex reinforcement learning benchmark, Humanoid-v4, successfully for the first time, our method highlights that hDQNN can deliver improved performance over models based on widely used state-of-the-art classical architectures. These findings offer a pathway toward leveraging near-term hybrid quantum-classical (hQC) computing systems for large-scale machine learning and underscore the potential of hQC architectures in advancing reinforcement learning and artificial intelligence.

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