Quantum Neural Networks Need Checkpointing

Résumé fourni par la source

Quantum Neural Networks (QNNs) harness quantum superposition and entanglement, offering promising advantages for machine learning tasks. However, noise in quantum computers frequently disrupts QNN training, wasting computational resources and extending queue times. This paper introduces the first QNN checkpointing framework to address this challenge. Through experiments on various quantum devices, we demonstrate that QNN behavior is fundamentally hardware-dependent, with the same model performing differently across platforms. This key finding shows that quantum checkpoints require additional metadata about hardware specifics and shot counts unique to quantum systems. Our framework requires minimal storage (only 186.6KB for a 100-qubit QNN) and negligible overhead, enabling frequent checkpointing to enhance training resilience and reproducibility in the NISQ era.

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