How Many Shots Does It Take? A Noise-Aware Quantum Resource Allocation Framework
2026-07-27 • Emerging Technologies
Emerging Technologies
AI summaryⓘ
The authors address the problem of needing many repeated runs (shots) to get trustworthy results from quantum computers, which can be costly and energy-consuming. They developed a simple, exact formula to figure out the best number of shots needed for any quantum algorithm. Additionally, they propose a method to smartly divide a fixed number of shots across parts of a quantum circuit to minimize errors. Their approach can reduce the total shots required by about 58% and cut energy use by up to 62%, while lowering errors by up to 73% compared to usual methods.
quantum computingquantum algorithmshotserror minimizationquantum circuitenergy consumptionanalytical modelshot allocationexecution reliability
Authors
Prateek P. Kulkarni, Sumit K. Mandal
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.