A quantum prototype learning scheme encodes class representatives as generative matrix product states and performs classification and clustering via geometric measures in Hilbert space, outperforming classical prototypes on Fashion-MNIST and ECG data.
Anomaly detection with tensor networks
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
SMT-AD detects anomalies via superposed multiresolution bond-dimension-1 MPOs with Fourier embedding, claiming competitive baseline performance and linear parameter scaling.
Tensor Train compression algorithms detect anomalies by maintaining normal data structure and deleting anomalous structure, tested on digits, faces, and cyber-attack datasets.
Introduces two algorithms for efficient finite initialization of tensor network layers via iterative partial norm computations, applied to MPS/TT and MPO/TT-M layers with scaling analysis and public code.
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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Geometric Prototype Learning in Quantum Hilbert Space with Matrix Product States
A quantum prototype learning scheme encodes class representatives as generative matrix product states and performs classification and clustering via geometric measures in Hilbert space, outperforming classical prototypes on Fashion-MNIST and ECG data.
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SMT-AD: a scalable quantum-inspired anomaly detection approach
SMT-AD detects anomalies via superposed multiresolution bond-dimension-1 MPOs with Fourier embedding, claiming competitive baseline performance and linear parameter scaling.
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Anomaly Detection from a Tensor Train Perspective
Tensor Train compression algorithms detect anomalies by maintaining normal data structure and deleting anomalous structure, tested on digits, faces, and cyber-attack datasets.
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Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms
Introduces two algorithms for efficient finite initialization of tensor network layers via iterative partial norm computations, applied to MPS/TT and MPO/TT-M layers with scaling analysis and public code.
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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.