Quantum Integration of Data and Emergence at Atomic Scales (Qu-IDEAS)

Cornell University, Ithaca NY

Investigators

Abstract

An intensely computational and data-intensive theme of modern quantum materials research is to search for new materials platforms with desired properties and understand them. Quantum materials have key properties and exhibit phenomena deeply rooted in the laws of quantum physics and the collective behavior of their constituent electrons. These materials are foundational for innovative future quantum-based technologies. A successful search requires connecting data to theoretical insight. Rapid advances in research on quantum materials presents an opportunity for significant progress; however, the volume and novelty of the ever-growing datasets present a problem for analysis and search. Rapid advances in quantum technologies have led to development of Noisy Intermediate-Scale Quantum (NISQ) devices, the current quantum computer technology. NISQ devices present new opportunities to compare the complex reality of the material world and idealized theoretical models that contain the essential physics but are notoriously difficult to compute with conventional computers. The need for optimal control of NISQ devices and understanding image-like data that results from their use places quantum materials research and quantum simulation on NISQ devices at a crossroads of opportunity that can benefit from new solutions to data-driven challenges and optimization. This project addresses the challenges using machine learning tools based on artificial intelligence. New insights will be fed into synthesizing new quantum materials and optimizing use of the NISQ devices. In the process, new software infrastructure for conventional computers and those using NISQ technology will be developed and made available to the broader community. Internship opportunities will help train students in machine learning, quantum computation, and materials discovery for the next-generation quantum workforce. The team will develop ML tools guided by the structure of the data, scientific meaning, and objectives. The tools will then be used to gain new theoretical insights, and feed the insight back into material synthesis and optimal use of NISQ devices. Specifically, data from the Inorganic Crystal Structure Database and Materials Project, guided by quantum chemical reasoning, will be used to discover new descriptors, and predict new topological materials. Quantum-classical hybrid approaches for NISQ computing will be developed, exploiting their advantage in encoding sampling problems. This project plans to deliver new topological materials, suites of ML tools for solving the data problems, and new AI algorithms for quantum-classical hybrid optimization. The effort will help build the next-generation quantum workforce through internship opportunities. The suit of classical ML tools and quantum-classical hybrid AI tools will be openly available in a user-friendly formats. The Office of Advanced Cyberinfrastructure in the Computer and Information Science and Engineering Directorate and the Division of Materials Research in the Mathematical and Physical Sciences Directorate jointly supported this award. 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.