Quantum algorithms achieve polylog(N) complexity for high-dimensional linear SDEs by amplitude-encoding the solution and noise via Dyson series or Euler-Maruyama approximations plus quantum linear systems solvers.
Preparing arbitrary continuous functions in quantum registers with logarithmic complexity,
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MPS generative model trained to sample Heston model paths for quantum path-dependent option pricing.
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.
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Quantum algorithm for solving high-dimensional linear stochastic differential equations via amplitude encoding of the noise term
Quantum algorithms achieve polylog(N) complexity for high-dimensional linear SDEs by amplitude-encoding the solution and noise via Dyson series or Euler-Maruyama approximations plus quantum linear systems solvers.
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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.
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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.