aiqbench analysis
This research introduces a noise-aware framework to optimize quantum resource allocation by mathematically determining the precise number of shots needed for reliable algorithm execution. By distributing shot budgets across circuit partitions based on error minimization, the approach reduces required executions and energy consumption significantly compared to standard practices. This advancement directly addresses critical efficiency challenges in near-term noisy intermediate-scale quantum (NISQ) device utilization.
How Many Shots Does It Take? A Noise-Aware Quantum Resource Allocation Framework
arXiv:2607.24704v1 Announce Type: new Abstract: Any algorithm execution on quantum computers requires several repeated and costly executions (known as shots) to obtain reliable results. In this work, we propose a closed-form accurate analytical expression to determine optimal number of shots required for reliable execution of any algorithm on a quantum computer. We also present a theoretically grounded technique to distribute fixed shot budget across different partitions in a quantum circuit minimizing the total error. Our proposed analytical model helps to reduce the shots associated with reliable execution of quantum algorithms by about 58\% compared to current practice, in turn reducing the energy consumption by upto 62\%. Furthermore, our proposed optimal shot allocation technique across different partitions reduces total error by up to 73\% compared to conventional approaches.
Title:How Many Shots Does It Take? A Noise-Aware Quantum Resource Allocation Framework
Abstract:Any algorithm execution on quantum computers requires several repeated and costly executions (known as shots) to obtain reliable results. In this work, we propose a closed-form accurate analytical expression to determine optimal number of shots required for reliable execution of any algorithm on a quantum computer. We also present a theoretically grounded technique to distribute fixed shot budget across different partitions in a quantum circuit minimizing the total error. Our proposed analytical model helps to reduce the shots associated with reliable execution of quantum algorithms by about 58\% compared to current practice, in turn reducing the energy consumption by upto 62\%. Furthermore, our proposed optimal shot allocation technique across different partitions reduces total error by up to 73\% compared to conventional approaches.
Quantum Physics (quant-ph); Emerging Technologies (cs.ET)
arXiv:2607.24704 [quant-ph]
(or arXiv:2607.24704v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.24704 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3816440.3818544 Focus to learn more DOI(s) linking to related resources
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