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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4 Pith papers cite this work. Polarity classification is still indexing.
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Parity Architecture delivers record ~0.01 fidelity for 50-qubit QFT on IBM hardware with super-exponential scaling improvement.
A geometric encoding method using hyperplane self-similarity and Hopf fibration maps vortex structures to quantum observables, allowing 30-qubit simulation of Re=35,000 turbulence with logarithmic scaling.
A two-step method minimizes entanglement entropy of target states before using matrix product state representations to achieve high-accuracy quantum state preparation on NISQ devices.
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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Demonstrating Record Fidelity for the Quantum Fourier Transform
Parity Architecture delivers record ~0.01 fidelity for 50-qubit QFT on IBM hardware with super-exponential scaling improvement.
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Geometric encoding of turbulence for end-to-end quantum simulation
A geometric encoding method using hyperplane self-similarity and Hopf fibration maps vortex structures to quantum observables, allowing 30-qubit simulation of Re=35,000 turbulence with logarithmic scaling.
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Minimizing entanglement entropy for enhanced quantum state preparation
A two-step method minimizes entanglement entropy of target states before using matrix product state representations to achieve high-accuracy quantum state preparation on NISQ devices.