arXiv:2505.04880v2 Announce Type: replace-cross
Abstract: Understanding the high-level conceptual structure of quantum algorithms from their low-level circuit representations is a critical task for verification, debugging, and education. While traditi…
arXiv:2603.26695v1 Announce Type: cross
Abstract: Multimodal deep learning has substantially improved electrocardiogram (ECG) classification by jointly leveraging time, frequency, and time-frequency representations. However, existing generative models…
arXiv:2603.11382v4 Announce Type: replace-cross
Abstract: How can we determine whether an AI system preserves itself as a deeply held objective or merely as an instrumental strategy? Autonomous agents with memory, persistent context, and multi-step pl…
arXiv:2502.14950v2 Announce Type: replace-cross
Abstract: Inferring causal models from observed correlations is a challenging task, crucial to many areas of science. In order to alleviate the computational effort when sifting through possible causal e…
arXiv:2603.26494v1 Announce Type: cross
Abstract: Quantum language models have shown competitive performance on sequential tasks, yet whether trained quantum circuits exploit genuinely quantum resources — or merely embed classical computation in quan…
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…