arXiv:2603.26359v1 Announce Type: cross
Abstract: Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesi…
arXiv:2603.26432v1 Announce Type: cross
Abstract: Efficiently characterizing quantum dot (QD) devices is a critical bottleneck when scaling quantum processors based on confined spins. Measuring high-resolution charge stability diagrams (or CSDs, data …
arXiv:2603.25039v1 Announce Type: cross
Abstract: This review is designed to introduce mathematicians and computational scientists to quantum computing (QC) through the lens of uncertainty quantification (UQ) by presenting a mathematically rigorous an…
arXiv:2603.26318v1 Announce Type: cross
Abstract: We develop a tensor-network surrogate for option pricing, targeting large-scale portfolio revaluation problems arising in market risk management (e.g., VaR and Expected Shortfall computations). The met…
arXiv:2603.24654v1 Announce Type: cross
Abstract: This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manip…
arXiv:2603.24196v1 Announce Type: cross
Abstract: In scientific computing, the formulation of numerical discretisations of partial differential equations (PDEs) as untrained convolutional layers within Convolutional Neural Networks (CNNs), referred to…
arXiv:2603.03071v2 Announce Type: replace-cross
Abstract: Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reachability alone…
arXiv:2603.24298v1 Announce Type: cross
Abstract: The ground state search problem is central to quantum computing, with applications spanning quantum chemistry, condensed matter physics, and optimization. The Variational Quantum Eigensolver (VQE) has …