How Many Shots Are Enough for a Quantum Circuit?

2026-06-15

AI Summary

This work proposes the IncrementalExecution framework to dynamically determine the optimal termination point for measurement shots in static quantum circuits under black-box conditions—where no structural assumptions are made and the noise model is unknown. The framework halts execution when additional shots no longer significantly alter the empirical output distribution, thereby balancing computational cost and fidelity according to the principle of diminishing returns. Requiring no prior knowledge of the algorithm, and eschewing variational or adaptive circuit structures, it is universally applicable to any static quantum circuit and readily deployable on existing quantum cloud platforms. Its efficacy is demonstrated across 180 circuit-backend combinations and 7.3 million experimental runs, where it consistently outperforms existing methods that rely on problem-specific assumptions.

Abstract

Quantum algorithms require repeated circuit executions, known as shots, to estimate output distributions accurately. Determining the minimal number of shots needed to meet a target accuracy is crucial to reduce costs and resource usage, especially on today's noisy and expensive quantum hardware. In this paper, we address the shot optimisation problem in a black-box setting, where no assumptions are made about the structure of the quantum circuit or the noise model of the backend. We introduce IncrementalExecution, a novel online framework that dynamically determines when to stop executing shots based on the principle of point of diminishing returns: the point at which additional shots no longer significantly alter the empirical distribution of a fixed circuit. The framework supports customisable policies for shot management, enabling flexible trade-offs between execution cost and result fidelity within static execution scenarios. We assess our proposal through an extensive experimental evaluation spanning 33,750 framework configurations across 180 unique static quantum circuit-backend combinations, for a total of 7.3M independent experiments. Unlike prior work that relies on problem-specific knowledge or algorithm-dependent assumptions (e.g., variational or adaptive workflows), our approach is applicable to a large set of static circuits and immediately deployable on current quantum cloud platforms.

Problem

• shot optimisation
• quantum circuit
• measurement shots
• resource efficiency
• output distribution estimation

Innovation

• shot optimization
• black-box quantum execution
• IncrementalExecution
• diminishing returns
• static quantum circuits