Quantum computation of chemistry and material on noisy intermediate-scale quantum hardware

Dr. Chih-Chun Chien¹, Associate Professor

Co-PIs: Xiaoyi Lu¹, Costin Iancu²

1: University of California, Merced, CA 95340

2: Lawrence Berkeley National Laboratory, Berkely, CA 94720

The program led by the University of California, Merced (UCM) and partnered with Lawrence Berkeley National Laboratory (LBNL) is to build the training capacity of graduate and undergraduate research and education on quantum information science and technology (QIST) at UCM, an R2 minority serving institution. Quantum science has transformed into a thriving industry with the urgent need for a new generation of skilled workforce. The goal of the program supported by the Department of Energy (DOE) Reaching a New Energy Sciences Workforce (RENEW) is to cultivate future leaders of under-represented and disadvantaged groups in QIST. The research will bridge the gap between currently available quantum computers prone to errors and prototypical error-correcting systems with applications from domain science to quantum hardware. Since programming in future error-correcting quantum computers requires quite different techniques compared to that in currently available hardware, the program will overcome these challenges by empowering the UCM trainees from physics, computer science (CS), chemistry, and material science with a combination of interdisciplinary skills of domain science, QIST, and CS to successfully program and benchmark current and near-future hardware. The research projects arise from the two pillars of quantum physics, namely quantum probabilities and correlations: (1) the spontaneous structural transitions in molecular systems and (2) quantum correlations between internal and external spaces relevant to quantum computation and communication. The former will showcase quantum computation for chemistry while the latter will explore novel materials with applications in QIST. The research will be complemented by artificial intelligence (AI)/machine learning (ML) and high-performance computing. The program will leverage the relationship with LBNL to teach and extend ML/AI techniques for quantum program development and verification, large-scale analyses, and extraction of relevant information. The research will prepare the UCM trainees for addressing QIST challenges and solving fundamental problems on quantum computers. The education efforts will begin with the recruitment of graduate students in physics, chemistry, CS, and material science by hosting tables at DOE and American Physical Society (APS) Graduate School Fairs, presenting at recruitment webinars, and distributing the opportunities via UCM program chairs. The program will provide in-depth training in QIST by developing and offering introductory QIST courses and seminars without prerequisites of quantum physics or programming, which serve as a channel to attract interested students to pursue QIST careers. The trainees will solve domain-science problems on cloud-based quantum platforms alongside access to the LBNL facilities and explore research opportunities at UCM and other universities, LBNL, and industrial partners. The program will feature weekly group meetings, peer-mentoring, close interactions, and workshops on professional development and career planning. The trainees will present their research in conferences and workshops and publish their results. Furthermore, the program aims to substantially promote inclusion in QIST and meet the goals of the DOE RENEW by giving the under-represented and first-generation students at UCM high priority to participate in the training and research as well as broadcasting via the partnership with the APS Bridge and CalBridge programs to students from non-traditional backgrounds. The program will prepare the UCM under-represented and disadvantaged trainees for successful careers in QIST and address urgent needs for energy conservation and efficient computation that align well with DOE missions.

This research was selected for funding by the Office of Basic Energy Sciences (BES)

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