Neural Networks Uncover Semiclassical Structures in Quantum Chaotic Systems
Physics-informed neural networks and neural quantum states have consolidated a new paradigm to analyze and discover physical phenomena through constrained neural parametrizations. In this context, we…
Read the full articleYou might also wanna read
Quantum Meets Classical: A New Path for Generative Models
Quantum diffusion models are revolutionizing generative learning. By merging classical and quantum methods, a new hybrid pipeline tackles th
Witness expansion: A unified framework for detecting quantum resources in mixed states
Quantum information science aims to harness different kinds of quantum resources to accomplish specific information-processing tasks. These
scirate.com·15d agoSemi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements
We consider data-driven Bayesian state estimation from compressed measurements (BSCM) of a model-free process. The dimension of the temporal
The Mathematics of Random Walks in High-Dimensional Spaces and Their Role in Deep Learning
The geometry of random walks in high dimensions provides the power behind deep learning and may be the secret to intelligence.
galileo-unbound.blog·10mo ago
Paper Argues Semiclassical Gravity Could Enable NP-Complete Problem Solving, Suggesting Gravity Must Be Quantized
Assuming the gravitational field is classical and that it couples to quantum fields via the semiclassical Einstein field equations, we show
arxiv.org·24d ago
Quantum CNNs: The Quest for Error-Free Computing
Quantum CNNs promise speed but face hurdles with noise and qubit costs. A new error-correction technique using bivariate bicycle codes might
Comments
Sign in to join the conversation.
No comments yet. Be the first.