25 citations · 41 across the 6 of their papers we have counts for
6 papers
Benchmarking of quantum fidelity kernels for Gaussian process regression
Xuyang Guo, Jun Dai, Roman V. Krems
Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels…
Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules
Jun Dai, Roman V. Krems
Kernel models of potential energy surfaces (PES) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the co…
Bayesian autotuning of Hubbard model quantum simulators
Ludmila Szulakowska, Jun Dai
Spins in gated semiconductor quantum dots (QDs) are a promising platform for Hubbard model simulation inaccessible to computation. Precise control of the tunnel couplings by tuning…
Quantum Gaussian process model of potential energy surface for a polyatomic molecule
Jun Dai, Roman V. Krems
With gates of a quantum computer designed to encode multi-dimensional vectors, projections of quantum computer states onto specific qubit states can produce kernels of reproducing…
Machine-learning-corrected quantum dynamics calculations
A. Jasinski, J. Montaner, R. C. Forrey +6
Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is…
Interpolation and extrapolation of global potential energy surfaces for polyatomic systems by Gaussian processes with composite kernels
Jun Dai, Roman V. Krems
Gaussian process regression has recently emerged as a powerful, system-agnostic tool for building global potential energy surfaces (PES) of polyatomic molecules. While the accuracy…