Quantum Physics-Informed Neural Networks
Corey J. Trahan, Mark Loveland, Samuel Dent
Abstract
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.
Topics & Concepts
Artificial neural networkQuantumComputer scienceTransient (computer programming)Statistical physicsQuantum statePhysicsQuantum mechanicsArtificial intelligenceOperating systemModel Reduction and Neural NetworksAdvancements in Semiconductor Devices and Circuit DesignQuantum Computing Algorithms and Architecture