How Many Shots Are Enough for a Quantum Circuit?

2026-06-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Expert, extended

Summary

The IncrementalExecution framework is introduced as a novel online method to dynamically determine the minimal number of "shots" (repeated circuit executions) required for quantum algorithms to achieve target output distribution accuracy. This black-box approach, which makes no assumptions about circuit structure or noise models, operates on the principle of diminishing returns, stopping when additional shots no longer significantly alter the empirical distribution. Evaluated across 33,750 configurations and 180 static quantum circuit–backend combinations, totaling 7.3 million experiments, the framework effectively approximates optimal shot counts. It supports customizable policies for shot management, enabling flexible trade-offs between execution cost and result fidelity, and is immediately deployable on current quantum cloud platforms.

Key takeaway

For AI Engineers optimizing quantum circuit execution costs, IncrementalExecution offers a robust method to dynamically manage shot counts. Implement this framework to avoid over-sampling by detecting the point of diminishing returns, especially for static, non-variational circuits. Consider using TVD for stringent accuracy needs and Hellinger distance for more permissive tolerances to balance cost and fidelity effectively. This approach can significantly reduce hardware resource consumption and queueing latency on cloud platforms.

Key insights

Dynamically stop quantum circuit shots when empirical distribution changes diminish, saving cost.

Principles

• Stop shots at the point of diminishing returns.
• Black-box shot optimization requires no circuit or noise model assumptions.

Method

IncrementalExecution iteratively performs small shot batches, updates empirical distribution, and checks for convergence using configurable stopping and stability criteria, dynamically allocating subsequent shots.

In practice

• Integrate with quantum sessions for low-latency adaptive execution.
• Use TVD for strict accuracy, Hellinger for looser tolerances.
• Bootstrap with prior iteration's output for VQA/QML.

Topics

• Quantum Shot Optimization
• Incremental Execution Framework
• Black-box Quantum Circuits
• Noisy Intermediate-Scale Quantum
• Divergence Metrics
• Quantum Cloud Platforms

Code references

• GBisi/quantum-incremental-execution
• GBisi/qsimbench

Best for: Research Scientist, AI Scientist, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.