EAGER: Circuit Cutting for Variational Quantum Algorithms and Quantum Machine Learning

Quantum computing, while incredibly powerful, still faces significant challenges with current technology, such as limited memory, short data lifespans, and errors. To tackle these issues, researchers use a method called circuit cutting, which breaks down large, complex tasks into smaller, more manageable pieces that can be solved independently. However, once these smaller tasks are solved, recombining their solutions into a final answer is not straightforward. It requires sophisticated methods to ensure the combined solution is accurate and efficient, especially for tasks in quantum-based machine learning. This project aims to explore and improve these recombination techniques, ultimately paving the way for more reliable and effective quantum computing. Such advancements could have a transformative impact on fields like artificial intelligence, drug discovery, and secure communication, while also preparing the next generation of researchers to tackle these cutting-edge challenges.

Considering the Noisy Intermediate-Scale Quantum (NISQ) devices, the project will investigate the impact of circuit cutting techniques on variational quantum algorithms (VQAs) and quantum machine learning (QML). By investigating the role of information entropy and quantum entanglement, optimizing sub-circuit recombination, and developing advanced cutting techniques, the research aims to minimize sampling overhead while maintaining fidelity. These efforts seek to benchmark circuit cutting's impact on accuracy and efficiency, advancing quantum computational frameworks and enabling larger, more robust computations tailored to QML applications.

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.

Award Number: 2515880
Principal Investigator: Ashfaq Khokhar
Funds Obligated: $119,999
State: IA