Everything about Machine learning in physics
Applications of machine learning to quantum physics
Applying machine learning (ML) methods to the study of quantum systems is an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other examples include learning Hamiltonians,, detecting phase transition in spin-systems even when not trained on physical configurations near criticality, learning quantum phase transitions, and automatically generating new quantum experiments. ML is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including quantum information theory, quantum technology development, and computational materials design. In this context, for example, it can be used as a tool to interpolate pre-calculated interatomic potentials, or directly solving the Schrödinger equation with a variational method. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Machine learning in physicsA 5-minute orientation
- Skim a recent paper: Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s NextInfluential work since 2022
- Pick up a classic book: The Elements of Statistical LearningThe most-reprinted book on the subject
- Read the landmark paper: Physics-informed machine learningCited 9,189 times
- Go sideways: Quantum machine learningA closely related topic
- Go sideways: Tensor networkA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Physics-informed machine learning
When physics meets machine learning: a survey of physics-informed machine learning
Physics‐Constrained Machine Learning of Evapotranspiration
Physics informed machine learning: Seismic wave equation
Quantum machine learning for chemistry and physics
Machine learning meets quantum physics
Learning new physics from a machine
Physics guided machine learning using simplified theories
🆕 Influential since 2022
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Colloquium : Machine learning in nuclear physics
Machine Learning Interatomic Potentials and Long-Range Physics
High-energy nuclear physics meets machine learning
Physics-Informed Machine Learning for metal additive manufacturing
📚 Books worth reading
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Bayesian networks and decision graphsFinn V. Jensen · 2001
The Expected KnowledgeSivashanmugam Palaniappan · 2012
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Quantum machine learning Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks…
- Tensor network A tensor network is a representation of a high-dimensional array or multilinear map as a collection of smaller tensors whose indices are contracted according…
- Bose–Hubbard model The Bose–Hubbard model gives a description of the physics of interacting spinless bosons on a lattice. It is closely related to the Hubbard model that…
- HHL algorithm The Harrow–Hassidim–Lloyd (HHL) algorithm is a quantum algorithm for obtaining certain limited information about the solution to a system of linear equations…
- Frank Verstraete Frank Verstraete (born November 1972) is a Belgian quantum physicist who is the Leigh Trapnell Professor of Quantum Physics at the Faculty of Mathematics…
- Jacob Biamonte Jacob Daniel Biamonte FInstP is an American physicist and computer scientist working in quantum information theory and quantum computing. He is a professor…
- Quantum computational chemistry Quantum computational chemistry is an emerging field that exploits quantum computing to simulate chemical systems. Despite quantum mechanics' foundational…
- Giuseppe Carleo Giuseppe Carleo (born 1984) is an Italian physicist. He is a professor of computational physics at EPFL (École Polytechnique Fédérale de Lausanne) and…