1 citations · 1 across the 4 of their papers we have counts for
6 papers
Experimental differentiation and extremization with analog quantum circuits
Evan Philip, Julius de Hond, Vytautas Abramavicius +8
Solving and optimizing differential equations (DEs) is ubiquitous in both engineering and fundamental science. The promise of quantum architectures to accelerate scientific computi…
From quantum feature maps to quantum reservoir computing: perspectives and applications
Casper Gyurik, Filip Wudarski, Evan Philip +5
We explore the interplay between two emerging paradigms: reservoir computing and quantum computing. We observe how quantum systems featuring beyond-classical correlations and vast…
Vortex Detection from Quantum Data
Chelsea A. Williams, Annie E. Paine, Antonio A. Gentile +2
Quantum solutions to differential equations represent quantum data -- states that contain relevant information about the system's behavior, yet are difficult to analyze. We propose…
Quantum algorithm for solving nonlinear differential equations based on physics-informed effective Hamiltonians
Hsin-Yu Wu, Annie E. Paine, Evan Philip +2
We propose a distinct approach to solving linear and nonlinear differential equations (DEs) on quantum computers by encoding the problem into ground states of effective Hamiltonian…
Differential equation quantum solvers: engineering measurements to reduce cost
Annie Paine, Casper Gyurik, Antonio Andrea Gentile
Quantum computers have been proposed as a solution for efficiently solving non-linear differential equations (DEs), a fundamental task across diverse technological and scientific d…
Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
Chelsea A. Williams, Stefano Scali, Antonio A. Gentile +2
Quantum differential equation solvers aim to prepare solutions as -qubit quantum states over a fine grid of points, surpassing the linear scaling of classical solvers.…