arXiv:2603.27836v1 Announce Type: cross
Abstract: Large language models have recently shown potential in bridging the gap between classical machine learning and quantum machine learning. However, the lack of standardized, high-quality datasets and rob…
arXiv:2603.28421v1 Announce Type: cross
Abstract: Generating and preserving metrologically useful quantum states is a central challenge in quantum-enhanced atomic magnetometry. In multilevel atoms operated in the low-field regime, the nonlinear Zeeman…
arXiv:2603.26692v1 Announce Type: cross
Abstract: We introduce a new notion, that of a contextuality profile of a system. Rather than characterizing a system’s contextuality by a single number, its overall degree of contextuality, we show how it can b…
arXiv:2603.26718v1 Announce Type: cross
Abstract: We analyze the challenges of benchmarking scientific (multi)-agentic systems, including the difficulty of distinguishing reasoning from retrieval, the risks of data/model contamination, the lack of rel…
arXiv:2603.26739v1 Announce Type: cross
Abstract: In 2006 we proposed Quantum Fuzzy Sets, observing that states of a quantum register could serve as characteristic functions of fuzzy subsets, embedding Zadeh’s unit interval into the Bloch sphere. That…
arXiv:2603.28122v1 Announce Type: cross
Abstract: Integrating quantum circuits into deep learning pipelines remains challenging due to heuristic design limitations. We propose Q-DIVER, a hybrid framework combining a large-scale pretrained EEG encoder …
arXiv:2603.26904v1 Announce Type: cross
Abstract: Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc. However, despite decades of research, they rem…
arXiv:2603.27269v1 Announce Type: cross
Abstract: Foundation models have recently improved electrocardiogram (ECG) representation learning, but their deployment can be limited by computational cost and latency constraints. In this work, we fine-tune E…
arXiv:2603.09645v2 Announce Type: replace-cross
Abstract: Photonic Quantum Machine Learning (PQML) is an emerging method to implement scalable, energy-efficient quantum information processing by combining photonic quantum computing technologies with m…
arXiv:2603.28294v1 Announce Type: cross
Abstract: Learning from quantum data using classical machine learning models has emerged as a promising paradigm toward realizing quantum advantages. Despite extensive analyses on their performance, clean and fu…