Quantum algorithm prepares exact Hadamard product state of two function states with N-independent query complexity when either function has finitely many non-zero Fourier coefficients.
Ran, Encoding of matrix product states into quan- tum circuits of one- and two-qubit gates, Physical Review A101, 10.1103/physreva.101.032310 (2020)
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A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.
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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An efficient quantum Hadamard product algorithm for functions
Quantum algorithm prepares exact Hadamard product state of two function states with N-independent query complexity when either function has finitely many non-zero Fourier coefficients.
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Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
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Time Evolution on Hybrid Tensor Networks -- A Novel and Parallelizable Algorithm
Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.