Quantum-inspired estimators for F_alpha(P) and F_alpha(rho) achieve optimal sample complexity n ~ alpha and minimax MSE rate alpha/n, improving prior O(alpha^2) bounds.
Quantum speedup of Monte Carlo methods
5 Pith papers cite this work, alongside 332 external citations. Polarity classification is still indexing.
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A one-ancilla framework for QSAMPLE preparation via GQSP-based selective phase compilation embedded in fixed-point amplitude amplification, improving overlap dependence to inverse square-root minimum overlap.
Quantum rejection sampling yields a quadratically faster discrete Gaussian sampler on lattices, enabling two improved versions of quantum dual attacks with trade-offs in speed and memory.
A quantum Monte Carlo algorithm solves multidimensional Black-Scholes PDEs for option pricing with polynomial complexity in dimension d and accuracy 1/ε, with rigorous error bounds and a claimed speedup over classical Monte Carlo for bounded payoffs.
No single post-Moore technology replaces current HPC for plasma simulations, but FPGA-class accelerators offer near-term kernel offload, non-von Neumann architectures medium-term operator acceleration, and quantum computing long-term potential for warm dense matter microphysics.
citing papers explorer
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Towards Minimax Estimation of High-Order Functionals by Quantum Arguments
Quantum-inspired estimators for F_alpha(P) and F_alpha(rho) achieve optimal sample complexity n ~ alpha and minimax MSE rate alpha/n, improving prior O(alpha^2) bounds.
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Ancilla-Efficient QSAMPLE Preparation for Reversible Markov Chains
A one-ancilla framework for QSAMPLE preparation via GQSP-based selective phase compilation embedded in fixed-point amplitude amplification, improving overlap dependence to inverse square-root minimum overlap.
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Quantum algorithm for Discrete Gaussian Sampling
Quantum rejection sampling yields a quadratically faster discrete Gaussian sampler on lattices, enabling two improved versions of quantum dual attacks with trade-offs in speed and memory.
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Quantum Monte Carlo algorithm for option pricing and its complexity analysis
A quantum Monte Carlo algorithm solves multidimensional Black-Scholes PDEs for option pricing with polynomial complexity in dimension d and accuracy 1/ε, with rigorous error bounds and a claimed speedup over classical Monte Carlo for bounded payoffs.
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Post-Moore Technologies for Plasma Simulation: A Community Roadmap
No single post-Moore technology replaces current HPC for plasma simulations, but FPGA-class accelerators offer near-term kernel offload, non-von Neumann architectures medium-term operator acceleration, and quantum computing long-term potential for warm dense matter microphysics.