QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Nonlinear transformation of com- plex amplitudes via quantum singular value transformation
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GQSP enables polynomial synthesis of Hermitian matrices without block-encoding, yielding stable degree-independent success probability and closed-form symmetric expansions.
Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.
citing papers explorer
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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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Hermitian Matrix Function Synthesis without Block-Encoding
GQSP enables polynomial synthesis of Hermitian matrices without block-encoding, yielding stable degree-independent success probability and closed-form symmetric expansions.
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Analog photonic simulator for large-scale transport
Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.