Quantum Physics-Informed Neural Networks.

In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed, and hybrid configurations are explored. The results show that (1) for some applications, quantum PINNs can obtain comparable accuracy with less neural network parameters than classical PINNs, and (2) adding quantum nodes in classical PINNs can increase model accuracy with less total network parameters for noiseless models.

A Variational Quantum Linear Solver Application to Discrete Finite-Element Methods.

Finite-element methods are industry standards for finding numerical solutions to partial differential equations. However, the application scale remains pivotal to the practical use of these methods, even for modern-day supercomputers. Large, multi-scale applications, for example, can be limited by their requirement of prohibitively large linear system solutions.

Reconciling semiclassical and Bohmian mechanics. III. Scattering states for continuous potentials.

In a previous paper [B. Poirier, J. Chem.

Reconciling semiclassical and Bohmian mechanics. II. Scattering states for discontinuous potentials.

In a previous paper [B. Poirier, J. Chem.

Multidimensional quantum trajectories: applications of the derivative propagation method.

In a previous publication [J. Chem. Phys.