10 papers
Analog Quantum Asynchronous Event-Based Graph Neural Network
Kristian Sotirov, Shaheen Acheche, Antonio A. Gentile +1
Asynchronous, event-based graph neural networks (AEGNNs) have recently emerged as an efficient paradigm for processing the sparse and high-temporal-resolution data from event camer…
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…
Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos +2
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experim…
Weak forms offer strong regularisations: how to make physics-informed (quantum) machine learning more robust
Annie E. Paine, Smit Chaudhary, Antonio A. Gentile
Physics-informed (PI) methodologies have surged to become a pillar route to solve Differential Equations (DEs), sustained by the growth of machine learning methods in scientific co…
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…