Year: 2024
Type: conference-paper
Abstract: Quantum computing (QC) is poised to revolutionize problem-solving across various fields, with research suggesting that systems with over 50 qubits may achieve quantum advantage-surpassing supercomputers in certain optimization tasks. As the hardware size of Noisy Intermediate-Scale Quantum (NISQ) computers continues to grow, Multi-tenant computing (MTC) has emerged as a viable approach to enhance hardware utilization by allowing shared resource access across multiple quantum programs. However, MTC can also bring challenges and security concerns. This paper focuses on optimizing quantum hardware utilization in shared environments by implementing multi-programming strategies that not only enhance hardware utilization but also effectively manage associated risks like crosstalk and fault injection. We propose a novel partitioning and allocation method called Community-Based Dynamic Allocation Partitioning (COMDAP) and Secure COMDAP to refine and secure multi-programming capabilities in quantum systems. COMDAP ensures equitable and efficient resource distribution, addresses the issues of suboptimal partitioning, and significantly improves hardware utilization. We report a 23% average improvement in hardware utilization rate compared to existing greedy heuristics, with rates averaging 92%. COMDAP introduces an average increase of approximately 0.05X in$\Delta \mathbf{CX}$, alongside a 3.5% average reduction in PST across benchmarks.
Authors: Suryansh Upadhyay, Swaroop Ghosh
Institution: Pennsylvania State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: SaTC: EDU: Advancing Cybersecurity Education through Self-Learning Cybersecurity Training Kit, SaTC: CORE: Small: SLIQ: Securing Large-Scale Noisy-Intermediate Scale Quantum Computing, SaTC: Collaborative: Exploiting Spintronics for Security, Trust and Authentication, SaTC: EDU: A Curriculum for Quantum Security and Trust, SHF:Small: Collaborative Research: Exploring 3-Dimensional Integration Strategies of STTRAM, NSF Convergence Accelerator - Track C: SQAI: Scalable Quantum Artificial Intelligence for Discovery, SaTC: EDU: CyCAD: A Virtual Platform for Cybersecurity Curriculum on Analog Design, FET:Medium: Drug discovery using quantum machine learning
OpenAlex ID: W4406261122