A factorization of the parent Hamiltonian into noncommutative first-order operators enables a walk-free QSVT algorithm with quadratic gap improvement for preparing purified Gibbs states under exact KMS detailed balance.
Quantum state preparation without coherent arith- metic
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
A polynomial-time classical decision algorithm exactly characterizes which multivariable Laurent polynomial pairs are realizable by M-QSP and supplies a constructive implementation when the answer is yes.
n-regular block encodings let QSP apply degree-n polynomials directly to the eigenvalues of any square matrix, with an efficient conversion from standard block encodings.
Hybrid algorithm classically diagonalizes Hamiltonian tensor factors to construct block-encodings for quantum simulation via QSVD, with extensions for commuting time-dependent cases.
MPS generative model trained to sample Heston model paths for quantum path-dependent option pricing.
citing papers explorer
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Accelerating quantum Gibbs sampling without quantum walks
A factorization of the parent Hamiltonian into noncommutative first-order operators enables a walk-free QSVT algorithm with quadratic gap improvement for preparing purified Gibbs states under exact KMS detailed balance.
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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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Polynomial time constructive decision algorithm for multivariable quantum signal processing
A polynomial-time classical decision algorithm exactly characterizes which multivariable Laurent polynomial pairs are realizable by M-QSP and supplies a constructive implementation when the answer is yes.
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Quantum Eigenvalue Transformations for Arbitrary Matrices
n-regular block encodings let QSP apply degree-n polynomials directly to the eigenvalues of any square matrix, with an efficient conversion from standard block encodings.
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Hybrid Quantum-Classical Algorithm for Hamiltonian Simulation
Hybrid algorithm classically diagonalizes Hamiltonian tensor factors to construct block-encodings for quantum simulation via QSVD, with extensions for commuting time-dependent cases.
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Time series generation for option pricing on quantum computers using tensor network
MPS generative model trained to sample Heston model paths for quantum path-dependent option pricing.