Postdoctoral Research Fellowships in Near-Term Quantum Advantage in Tensor Network-Inspired Machine Learning at the Laboratory for Physical Sciences

Job type: 

Application deadline: 

Monday, March 2, 2020

The Quantum Information and Device Theory group of Charles Tahan at the Laboratory for Physical Sciences (LPS) and the Quantum Information and Many-Body Physics group of Brian Swingle at the University of Maryland (UMD) are collaborating to explore the possible quantum advantage offered by near-term quantum systems from quantum computers with dozens or hundreds of qubits to analog quantum systems of similar size to tensor-network inspired machine learning. The combination of theoretical expertise on tensor networks at UMD and the broad knowledge of the state-of-the-art in quantum computers and device physics at LPS offers unique synergy to pursue this line of inquiry. Understanding the implications of modern quantum many body techniques to quantum information systems both for quantum speedup (or lack thereof) and vice versa is of growing interest. We will focus on three questions: 1) quantifying the scaling of quantum advantage in machine learning with the number of qubits used, 2) comparing the use of digital and analog quantum approaches in machine learning, and 3) identifying near term quantum systems that can realize interesting new physics. We expect to work with experimental collaborators at MIT and elsewhere to realize these and related quantum many-body physics proposals.

Applicants

This research project will support two postdoctoral fellows, one working at LPS and another at the Physics Department, both located at the University of Maryland in College Park, MD. Applicants should have experience with and interest in tensor networks, machine learning, quantum many-body physics, and/or the physics of qubit devices for quantum information processing, particularly solid-state implementations. Postdoctoral fellows are expected to collaborate closely between LPS and UMD and work as a team to achieve project goals.

Applicants should be open to working with experimental groups on problems of practical interest as well as developing novel proposals.

Anticipated start date is Fall 2019 or until filled. Applicants should submit an electronic application to Academic Jobs Online including a CV, publications list, and the electronic (email) contact details of two references. Please direct any questions to Charles Tahan (ctahan@lps.umd.edu ) or Brian Swingle (bswingle@umd.edu).

The University of Maryland is an Affirmative Action/Equal Opportunity employer and particularly welcomes applications from women and members of minority groups.

PHYSICS DEPARTMENT AND QUICS AT THE UNIVERSITY OF MARYLAND The Joint Center for Quantum Information and Computer Science (QuICS) is a partnership between the University of Maryland (UMD) and the National Institute of Standards and Technology (NIST). Located at the University of Maryland just outside of Washington, D.C., the center advances research and education in quantum computer science and quantum information theory.

JOINT QUANTUM INSTITUTE The Joint Quantum Institute (JQI) is pursuing that goal through the work of leading quantum scientists from the Department of Physics of the University of Maryland (UMD), the National Institute of Standards and Technology (NIST) and the Laboratory for Physical Sciences (LPS).

QUANTUM COMPUTING AT LPS The Solid State and Quantum Physics group at LPS consists of both experimentalists and theorists focused on various aspects of solid-state quantum devices, quantum computers, condensed matter theory, and quantum information science.

ABOUT THE LABORATORY FOR PHYSICAL SCIENCES Located adjacent to the University of Maryland's College Park Campus, the Laboratory for Physical Sciences is a unique facility where university and federal government personnel collaborate on research in advanced communication and computer technologies. The Lab for Physical Sciences is also a member of the Joint Quantum Institute together with NIST and UMD.