5 papers
Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Pauline Mathiot, Elio Garnaoui, Axel-Ugo Leriche +37
We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel…
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…
Evaluation of derivatives using approximate generalized parameter shift rule
Vytautas Abramavicius, Evan Philip, Kaonan Micadei +5
Parameter shift rules are instrumental for derivatives estimation in a wide range of quantum algorithms, especially in the context of Quantum Machine Learning. Application of singl…
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…