The many-body problem in the age of quantum machine learning

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NONTECHNICAL SUMMARYThis award supports theoretical and computational research with an aim to develop and apply new machine learning algorithms and computational tools for investigating novel properties of quantum materials in which the electron-electron interactions are very strong. These materials can serve as building blocks for devices with new functionalities beyond the semiconductor paradigm. Numerical modeling of such systems, on the other hand, is challenging, as it requires dealing with a large configuration space: Datasets are comprised of all possible electronic configurations and cannot be stored in the memory of the largest supercomputer. Hence, the strongly interacting many-electron problem can be interpreted as an “extreme data science” problem from an information processing perspective. A game-changing idea that tackles this complexity consists of identifying patterns and compressing datasets in a spirit very similar to algorithms to compress images and videos. In the past five years, a novel line of research, now referred to as “quantum machine learning”, has emerged that uses neural networks and machine-learning algorithms to extract insightful information and represent the complex structure encoded in quantum mechanical states. In this project, the PI will develop new algorithms based on quantum information and machine-learning ideas to study the electronic and magnetic properties of a variety of strongly-interacting model systems and real materials. This award also supports several educational and outreach activities. The PI will (i) work in collaboration with the Center for STEM Education at Northeastern to enrich current program offerings for high school and community college students and teachers through seminars and presentations, and (ii) establish a broad reaching program of class visits to high schools in underserved communities with a large population of minority students to expose them to scientific research. In conjunction, a year-round mentoring program will be created for students who want to choose a scientific career path. In addition, the PI will continue to organize the International Quantum Matter Seminar series, which is live streamed on Zoom and YouTube. Finally, the PI will continue to maintain and develop an open-source collection of programs and libraries for the simulation of strongly correlated quantum lattice models, that is currently being used by dozens of researchers and students around the globe.TECHNICAL SUMMARYThis award supports theoretical and computational research with an aim to develop and apply new machine learning algorithms and computational tools for investigating novel electronic and magnetic properties of strongly correlated quantum materials. The research consists of two main directions. In the first thrust, the PI and his team will develop several new algorithms to study ground-state, excitation spectra, and thermodynamics of quantum many-body problems using neural networks and Gaussian processes as approximators. In the second thrust, they will study interacting chains and ladders with long-range interactions that realize actual spontaneous symmetry breaking and true long-range order, thus enabling one to study competition between phases that are currently out of reach. This award also supports several educational and outreach activities. The PI will (i) work in collaboration with the Center for STEM Education at Northeastern to enrich current program offerings for high school and community college students and teachers through seminars and presentations, and (ii) establish a broad reaching program of class visits to high schools in underserved communities with a large population of minority students to expose them to scientific research. In conjunction, a year-round mentoring program will be created for students who want to choose a scientific career path. In addition, the PI will continue to organize the International Quantum Matter Seminar series, which is live streamed on Zoom and YouTube. Finally, the PI will continue to maintain and develop the ALPS open-source collection of programs and libraries for the simulation of strongly correlated quantum lattice models, that is currently being used by dozens of researchers and students around the globe.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.