Gate fusion applied to both forward and backward passes in quantum circuit simulation achieves 20-30x throughput gains and supports training large 20-qubit 1000-layer QML models with 60000 parameters using gradient checkpointing.
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Fast and memory-efficient classical simulation of quantum machine learning via forward and backward gate fusion
Gate fusion applied to both forward and backward passes in quantum circuit simulation achieves 20-30x throughput gains and supports training large 20-qubit 1000-layer QML models with 60000 parameters using gradient checkpointing.