Learning thermodynamic master equations for open quantum systems
Quantum 10, 2151 (2026). The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific…
Read the full articleYou might also wanna read
Thermodynamic neurons enable interpretable rule-based machine learning through heat flow
Machine learning is typically described in terms of deterministic logical operations, whereas physical systems generally operate in the pres
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 throu
Quantum Correlations: The Unseen Revolution
Exploring how neural networks could unlock the mysteries of measurement-induced entanglement in quantum systems, revealing both opportunitie
Hybrid Quantum-Classical Neural Network Demonstrates High Accuracy in Recognizing Quantum Phases of Matter
Identifying quantum phases of matter is key to understanding strongly correlated materials, but remains a challenging task for both conventi
Generalized Two-Qubit Hamiltonian Improves Quantum Feature Maps for Machine Learning
Projected quantum feature maps provide a strategy for using quantum processors as feature generators for classical machine-learning models.
New bounds dramatically reduce measurement requirements for quantum simulations
Previously, simulating open quantum systems with the Wave Matrix Lindbladization algorithm required computational effort scaling with the sy

Comments
Sign in to join the conversation.
No comments yet. Be the first.