Rice University

Events at Rice

Seminar

Physics & Astronomy

Speaker: Lin Dong
Rice University

General Topic- MACHINE LEARNING FOR QUANTUM SIMULATION ON CLASSICAL COMPUTERS

Thursday, March 30, 2017
4:00 PM  to 5:00 PM

300  Brockman Hall for Physics
Rice University
6100 Main St
Houston, Texas, USA

Ultra-cold atoms and condensed-matter physics is the study of various collective be- havior of infinitely complex assemblies of electrons, nuclei, magnetic moments, spin, atoms, molecules or qubits. The complexity is reflected in the size of the state space, which grows exponentially with the number of particles, reminiscent of the ’curse of dimensionality’ commonly encountered in the machine learning and deep learning field of studies. Despite the curse, the machine learning community has developed various techniques to turn it into a blessing. Remarkable progresses have been made, over the last few decades but most prominently within the last ten years, to recog- nize, classify, interpolate, and characterize complex sets of data. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science, applied statistics, applied mathematics and electrical and computer engineering, and at the core of artificial intelligence and data science. The rapid progresses have been driven by the combination of development of new learning algorithms and theories, new programming frameworks and hardware infrastructures, and the ongoing explosion in the availability of online data and low-cost computations. Not to mention and going into details of how the adoption of machine-learning based solutions have been playing a more and more important role throughout various domain science, technology and commerce, I would like to note that machine learning has been on the frontier of fundamental research in physics, and is being used to solve long out- standing problems in quantum science. Machine learning and in particular, deep learning approaches have been used to search for exotic particles and improved the search power and precision of high-energy particle colliders, an exact mapping between the variational renormalization group and deep learning has been made, quantum algorithms for supervised and unsupervised machine learning has been proposed, machine learning has been used to classify phases of matter with one specific example of classifying phases and phase transitions in the Ising model, and solving many-body problems with machine learning based approaches. In this talk, I will introduce some basic concepts about machine learning and deep learning, combine them with my own experiences and applications, and finally touch upon the methodology and results from the research article published at Science magazine by G. Carleo and M. Troyer, Science 355, 602-606 (2017).

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