Harnessing Quantum Control Algorithms that Utilize and Enable New Machine Learning Applications with Entangled Qubits
Abstract
This project will harness quantum optimal control algorithms that both use and enable new machine learning applications for data science and the broader quantum information sciences. In contrast to existing machine-learning algorithms that use/enable classical calculations (i.e., conventional neural networks based on classical computation), this project represents a transformative departure by harnessing quantum computing to increase the capabilities of classical machine learning using quantum states and systems. The research objectives, inter-connected technical approaches, and potential impact of this project are as follows: (1) predictive quantum control calculations are first utilized to create a quantum training dataset (i.e., magnetic signal strength, pulse shape, and excitation frequency) that (2) enables machine learning algorithms to construct tailored optimal pulse shapes that initialize qubit arrays into desired quantum states. This initialization process will subsequently enable (3) quantum algorithms that can be used to analyze quantum states instead of classical data. Together, these initiatives (4) support NSWC Corona s program goals to educate and train the next generation of students for advancing Naval mission priorities in quantum information science. Moreover, this project provides an additional campus-wide pipeline for students to participate in research as well as potential employment experience for NSWC Corona.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 21, 2021
- Source ID
- N001742110014
Entities
People
- Bryan M. Wong
Organizations
- United States Navy
- University of California Regents