QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018)
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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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.
QIML uses a quantum-trained Q-Prior to enhance classical autoregressive predictions of spatiotemporal chaos, improving accuracy by up to 17.25% and full-spectrum fidelity by up to 29.36% while enabling stable forecasts for 3D turbulent channel flow.
Equivariant sp-QCNN encodes general symmetries with group theory, splits circuits at pooling layers to preserve symmetry while enabling parallel measurements, and shows improved efficiency and trainability over standard equivariant QCNNs in noisy quantum data classification.
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.
Proposes a transmon-based co-designed quantum processor using a central resonator and QCR to enable NISQ simulation of nanoscale NMR with major SWAP reduction and non-unitary hyperpolarization operations.
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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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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Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
QIML uses a quantum-trained Q-Prior to enhance classical autoregressive predictions of spatiotemporal chaos, improving accuracy by up to 17.25% and full-spectrum fidelity by up to 29.36% while enabling stable forecasts for 3D turbulent channel flow.
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Resource-efficient equivariant quantum convolutional neural networks
Equivariant sp-QCNN encodes general symmetries with group theory, splits circuits at pooling layers to preserve symmetry while enabling parallel measurements, and shows improved efficiency and trainability over standard equivariant QCNNs in noisy quantum data classification.
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The power and limitations of learning quantum dynamics incoherently
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.
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Co-Design quantum simulation of nanoscale NMR
Proposes a transmon-based co-designed quantum processor using a central resonator and QCR to enable NISQ simulation of nanoscale NMR with major SWAP reduction and non-unitary hyperpolarization operations.