The Cost of Certainty: Shot Budgets in Quantum Program Testing
Andriy Miranskyy

TL;DR
This paper presents a framework for quantifying and optimizing measurement shot budgets in quantum program testing, balancing verification accuracy against scarce quantum hardware resources.
Contribution
It introduces a unified approach connecting error bounds with test strategies and extends analysis to program-level verification, considering noise and resource allocation.
Findings
Inverse test is most measurement-efficient
Swap test requires about twice as many shots as inverse test
Chi-square test often needs orders of magnitude more measurements
Abstract
As quantum computing advances toward early fault-tolerant machines, testing and verification of quantum programs become urgent but costly, since each execution consumes scarce hardware resources. Unlike in classical software testing, every measurement must be carefully budgeted. This paper develops a unified framework for reasoning about how many measurements are required to verify quantum programs. The goal is to connect theoretical error bounds with concrete test strategies and to extend the analysis from individual tests to full program-level verification. We analyze the relationship between error probability, fidelity, trace distance, and the quantum Chernoff bound to establish fundamental shot count limits. These foundations are applied to three representative testing methods: the inverse test, the swap test, and the chi-square test. Both idealized and noisy devices are…
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