A quantum algorithm discovers and samples rare events with optimal quantum scaling without prior knowledge of the events, yielding quadratic speedup for heavy-tailed systems and polynomial speedup for stationary processes.
Rattew and Patrick Rebentrost
10 Pith papers cite this work. Polarity classification is still indexing.
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Quantum algorithms are constructed for exact analog encoding of correlated Gaussian vectors and their exponentiation, achieving subcubic gate-depth complexity under polylogarithmic data-loading assumptions, with end-to-end resource analysis for rough Bergomi variance simulation.
Quantum algorithms achieve polynomial advantage for synchronization estimation and super-polynomial advantage for no-phase-locking certification in higher-order simplicial Kuramoto models under stated assumptions.
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Quantum algorithm solves regularized Wasserstein proximal operator via Schrödingerization of Cole-Hopf transformed heat equations with O(d N_x T log²(1/ε)) query complexity.
A hybrid quantum-classical variational method using polynomial approximations to the energy functional enables finite element analysis of a 1D Neo-Hookean hyperelastic model on near-term quantum hardware.
Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
Unitaria is a new open-source Python library that provides a high-level, composable interface for block encodings in quantum computing, enabling automatic circuit generation and classical simulation-based verification.
citing papers explorer
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Quantum enhanced rare event discovery and sampling
A quantum algorithm discovers and samples rare events with optimal quantum scaling without prior knowledge of the events, yielding quadratic speedup for heavy-tailed systems and polynomial speedup for stationary processes.
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Quantum analog-encoding for correlated Gaussian vectors and their exponentiation with application to rough volatility
Quantum algorithms are constructed for exact analog encoding of correlated Gaussian vectors and their exponentiation, achieving subcubic gate-depth complexity under polylogarithmic data-loading assumptions, with end-to-end resource analysis for rough Bergomi variance simulation.
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Efficient Quantum Algorithms for Higher-Order Coupled Oscillators
Quantum algorithms achieve polynomial advantage for synchronization estimation and super-polynomial advantage for no-phase-locking certification in higher-order simplicial Kuramoto models under stated assumptions.
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Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
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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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Schr\"odingerization based quantum algorithms for regularized Wasserstein proximal operators
Quantum algorithm solves regularized Wasserstein proximal operator via Schrödingerization of Cole-Hopf transformed heat equations with O(d N_x T log²(1/ε)) query complexity.
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A Variational Quantum Algorithm for Nonlinear Finite Element Analysis of Hyperelastic Materials
A hybrid quantum-classical variational method using polynomial approximations to the energy functional enables finite element analysis of a 1D Neo-Hookean hyperelastic model on near-term quantum hardware.
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Analog photonic simulator for large-scale transport
Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.
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Spectral methods: crucial for machine learning, natural for quantum computers?
Quantum computers may enable more natural manipulation of Fourier spectra in ML models via the Quantum Fourier Transform, potentially leading to resource-efficient spectral methods.
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Unitaria: Quantum Linear Algebra via Block Encodings
Unitaria is a new open-source Python library that provides a high-level, composable interface for block encodings in quantum computing, enabling automatic circuit generation and classical simulation-based verification.