I-Corps: Translation Potential of Distributed Quantum Resource Management and Scheduling with Efficient Circuit Cutting

This I-Corps project focuses on the development of a scalable quantum-classical framework, to overcome critical limitations in near-term quantum computing hardware. While quantum computers hold transformative potential for solving complex problems in drug discovery, cryptography, and materials science, today's error-prone, smaller-scale quantum devices lack the capacity and reliability to execute large algorithms. This quantum-classical framework overcomes these barriers by intelligently leveraging both quantum and classical computing resources to execute quantum circuits that are too large to fit on existing quantum hardware. The technology enables quantum advantage on current resource-constrained hardware and accelerates practical application while reducing costs for quantum cloud providers, research institutions, and enterprises.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on a scalable computing framework that partitions large quantum circuits into smaller, executable subcircuits and manages their execution and reconstruction within a heterogeneous distributed system comprising both quantum and classical computing resources. This computing framework employs advanced graph partitioning algorithms to model quantum circuits and identify optimal cutting points, taking into account practical hardware constraints to minimize cutting overhead and maximize algorithm fidelity. This framework also incorporates a weighted closest-first scheduler for distributing subcircuits efficiently across heterogeneous quantum and classical resources, enabling both sequential and parallel execution. Scientifically, this framework advances beyond existing tools by offering improved performance, enhanced resource utilization, and superior scalability for distributed quantum systems, while preserving the full computational power of the original circuit. Users benefit from its seamless integration with standard quantum programming environments, user-friendly application programming interfaces, and automated pipelines, allowing them to leverage complex quantum algorithms on resource-constrained hardware without requiring deep expertise in circuit optimization techniques.

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: 2514704
Principal Investigator: Ying Mao
Funds Obligated: $50,000
State: NY