QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
quant-ph 4representative citing papers
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 compilation scheme called Parity Twine achieved a 50-qubit unitary QFT with process fidelity ≈1e-2 on IBM Heron r3, and up to 52 qubits with plurality voting, surpassing prior unitary QFT demonstrations.
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
-
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.
-
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.
-
Demonstrating Record Fidelity for the Quantum Fourier Transform
A compilation scheme called Parity Twine achieved a 50-qubit unitary QFT with process fidelity ≈1e-2 on IBM Heron r3, and up to 52 qubits with plurality voting, surpassing prior unitary QFT demonstrations.
-
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.