A deep photonic QNN achieves nonlinear operations via virtual Hilbert space expansion on a linear chip with four entanglement sources, demonstrated on classification, generation, and state preparation tasks.
P´ erez-Salinas, A
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QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Formation of a bound state in the agent-noise energy spectrum restores QRL performance to the noiseless case for eigenstate solving under non-Markovian decoherence.
citing papers explorer
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Photonic-Implemented Efficient Deep Quantum Neural Network via Virtual-Driven Hilbert Space Expansion
A deep photonic QNN achieves nonlinear operations via virtual Hilbert space expansion on a linear chip with four entanglement sources, demonstrated on classification, generation, and state preparation tasks.
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QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
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Noise-Resilient Quantum Reinforcement Learning
Formation of a bound state in the agent-noise energy spectrum restores QRL performance to the noiseless case for eigenstate solving under non-Markovian decoherence.