Quantum information science is poised to deliver transformative applications in the areas of quantum computing, networking, privacy, and sensing. In addition to the importance of quantum information science for understanding of basic quantum science, quantum information advances have strategic relevance for both national security and the economy of future information-based societies. However, as ever larger and more complex quantum devices are constructed, a key challenge is to control them in a way that preserves their fragile quantum nature. To achieve the required level of control, it is essential to precisely identify crucial properties and features of the quantum system, material, or process of interest. This project addresses the identification of key properties by using a bootstrapping approach, combining today's small quantum computers with large-scale classical computing resources to design the next generation of quantum computers. This approach will allow to systematic refining of large quantum systems, and engineering of devices with better functional properties such as intrinsic resistance to errors. Specifically, the project will use a combination of machine learning to screen candidate materials with desired properties, quantum simulation of promising systems using available intermediate-scale quantum processors to refine adaptive learning strategies, and experimental validation of the fundamental microscopic material properties. The transformative goal of this research is to develop improved robust and accurate control of large-scale quantum systems. By integrating big data, quantum simulation, and experimental validation to solve fundamental challenges in quantum information science, the project aims to synergistically leverage the benefits offered by these diverse and powerful tools. The collaborations and techniques developed will build a unique center of excellence for quantum information science, in response to a recognized national priority. The local infrastructure combined with a highly trained quantum-literate workforce will be instrumental in ensuring American competitiveness in quantum technology development.
This EPSCoR proposal brings together a team of researchers from Brown University (RI) and Dartmouth (NH) to investigate the use of novel data science methods to address two key challenges in quantum science: (i) System identification and quantum control of complex systems; and (ii) Many-body simulation of quantum materials. As ever larger quantum systems are constructed, a key challenge is to precisely identify the system Hamiltonian (even more generally, the underlying dynamical model) and to precisely manipulate it as desired. A key feature of the proposed work is to use quantum bootstrapping to both systematically refine our understanding of a quantum many-body system, and to engineer novel systems with desired functional properties, such as topologically protected states that may permit encoding of quantum information. This effort involves a combination of machine learning to screen candidate materials with desired properties, quantum simulation of promising systems using intermediate scale quantum processors to refine adaptive learning strategies, and experimental validation of fundamental microscopic materials properties. The transformative goal of the collaborative research is to develop both Hamiltonian and open-system (e.g. Liouvillian) identification approaches to characterize unknown quantum systems, by using algorithmic learning with experimental data obtained by highly controllable magnetic resonance techniques. The project will develop tools to account for environmental noise to enable robust, high-fidelity control of quantum dynamics. The collaborations and techniques developed will allow building of a unique center for quantum information science research in the US, with long-term research capabilities. The participating graduate and undergraduate students will form a valuable quantum-literate workforce.
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.
