A hybrid tensor network framework interpolates between classical and quantum models via controllable post-selection, with a trainable hyperparameter that complements bond dimension to enhance quantum machine learning.
In: Quantum Science and Technology3.3, p
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Benchmarking of variational tensor network ground-state searches reports 34x CPU speedup via parameter tuning and an additional 2.76x gain when moving to GPUs.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
citing papers explorer
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Entanglement is Half the Story: Post-Selection vs. Partial Traces
A hybrid tensor network framework interpolates between classical and quantum models via controllable post-selection, with a trainable hyperparameter that complements bond dimension to enhance quantum machine learning.
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Benchmarking Quantum Red TEA on CPUs, GPUs, and TPUs
Benchmarking of variational tensor network ground-state searches reports 34x CPU speedup via parameter tuning and an additional 2.76x gain when moving to GPUs.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.