How Many Shots Does It Take? A Noise-Aware Quantum Resource Allocation Framework

2026-07-27

AI Summary

This work addresses the excessive sampling (shots) required by quantum algorithms to ensure result reliability, which leads to significant resource consumption and energy waste. The authors propose, for the first time, a closed-form analytical model that precisely computes the optimal number of shots needed to execute a quantum algorithm. Under a fixed shot budget, they further introduce a noise-aware circuit partitioning strategy that theoretically guides the allocation of shots across circuit segments to minimize total error. By integrating noise modeling, circuit partitioning, error propagation analysis, and closed-form optimization, the method reduces shot usage by approximately 58% and energy consumption by 62% compared to current practices, while achieving a 73% reduction in total error relative to conventional allocation strategies—substantially enhancing resource efficiency in near-term quantum computations.

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.

Problem

• quantum computing
• shot allocation
• resource optimization
• error minimization
• noise-aware

Innovation

• quantum resource allocation
• optimal shot count
• noise-aware quantum computing
• error minimization
• closed-form analytical model