REU Site: Quantum Machine Learning Algorithm Design and Implementation

Quantum Computing (QC) promises to accelerate information processing and solve highly complex massive data problems. This three-year REU site will recruit and train nine undergraduate students each summer and engage them in research endeavors on the design of quantum signal processing and quantum machine learning circuits and simulations. The investigators, along with a team of faculty advisors, will supervise a series of multidisciplinary research projects in quantum AI and quantum Digital Signal Processing (DSP).

In addition to the planned REU projects, the investigators of this project will organize a series of industry-university collaborative training activities for the students. This REU features multidisciplinary synergies across different research labs that provide access to unique quantum simulation software, quantum physics and networking facilities, and quantum machine learning circuit design for several applications including health, sustainability, and security.

Specific applications include audio recognition, image understanding, encryption and solar energy systems. The program will also include crosscutting professional development, modules and workshops in public speaking, policy, ethics, patent development and outreach. Annual REU workshops will train students to communicate with stakeholders.

The investigator team will use the NSF Education and Training Application (ETAP) system for recruitment of REU student participants. Local and national evaluation units including the Center for Evaluating the Research Pipeline (CERP) will be deployed for assessments that will provide feedback for program improvement. Local site evaluators will also assess REU goals annually using feedback from student participants, academic and industry mentors, and other stakeholders.

The program engages minority-serving institutions and professional student chapters to broaden participation and enhance recruitment.

The REU will address STEM problems associated with quantum information processing (QIP) and specifically quantum signal processing and quantum machine learning (QML). Key research and education problems include a) understanding the theory and statistics of Quantum bits (Qubits), b) introduction to quantum noise models, c) understanding of tradeoffs between Qubit precision and quantum noise, d) skill-building with programming quantum simulations, and e) laboratory access to unique QC facilities.

The faculty investigators will organize project and mentorship activities including REU student mentorship by industry partners. The objectives of the proposed site are to a) introduce students to research practices by immersing them in government and industry projects, b) engage students in quantum machine learning research, c) motivate students to pursue QIP research careers and recruit them to graduate programs, and d) provide cross-cutting skills in presentation, ethics, and standards.

The REU projects are designed to introduce students to an array of quantum information processing technologies that emphasize the design of quantum simulation circuits for: AI-based signal and data classification, signal analysis synthesis using the quantum Fourier transform, quantum cloud and edge computing, quantum networking, quantum image understanding, and quantum based encryption. During the same period, projects will train REU students to understand issues dealing with quantum noise and quantum precision, quantum bit (qubit) measurement methods and theoretical aspects of superposition and entanglement.

The REU will achieve social impact through several mechanisms including cross-cutting training, workshops on public speaking and ethics, dissemination of quantum project results and outreach.

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.