Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsTreat an AI agent’s quantum result as an unverified claim until you can reconstruct how it was produced, rerun the computational workflow, and assess the hardware measurements independently. Preserve the original record first. A failed reproduction is a reason to investigate—not, by itself, proof of fraud, error, or a new discovery.
First, distinguish what failed to reproduce
“Reproduce” can mean different things. The National Academies’ 2019 report Reproducibility and Replicability in Science distinguishes computational reproducibility—obtaining consistent results using the same inputs, methods, code, and analysis conditions—from replicability, which tests the research question using newly obtained data.
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| Check | What you hold constant | What the result tells you |
|---|---|---|
| Computational reproducibility | The original inputs, code, methods, and analysis conditions | Whether the recorded computational workflow produces consistent results |
| Scientific replicability | The research question, while obtaining new data | Whether new evidence supports the same scientific conclusion |
For a quantum experiment, an identical software output and a new run on a quantum processor are not the same test. Quantum measurements are stochastic, and hardware conditions can change. Decide what “agreement” means for the particular claim before comparing outputs: it might mean the same conclusion or agreement within a justified statistical tolerance, rather than identical measurement counts.
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Do not overwrite, clean up, or selectively retain only the run that supports the expected conclusion. Save what the agent actually received, did, and returned, along with the original raw quantum outcomes and job metadata. The National Academies recommends communicating clear, specific, and complete information about computational methods and data products so others can repeat an analysis, subject to restrictions such as nonpublic data policies.
#1 Best Overall
- Agent provenance: agent identity and version, prompt, accessible system instructions, tool configuration, tool-call logs, response, and any human edits.
- Code and environment: repository or archive and immutable commit, dependencies, compiler and SDK versions, operating system, hardware or simulator configuration, and an environment lockfile or container image where feasible.
- Experiment specification: mathematical definition, circuit source, measurement or observable, units, parameter values, input data, random seeds, and the expected result or tolerance.
- Executed quantum job: target backend, job ID and timestamp, number of shots, mitigation or postselection settings, and raw counts or measurement data.
- Compilation and analysis: transpiled circuit, compiler pass configuration, layout and routing, intermediate files, analysis code, plots, and the exact procedure used to obtain the claimed result.
- Hardware context: calibration information and its timestamp, if available, plus any limitations such as unavailable provider internals or calibration snapshots.
Record details that cannot be recovered too: for example, a missing seed, inaccessible service version, or unavailable calibration record. Those gaps limit what others can verify.
Reconstruct the circuit that actually ran
An abstract quantum circuit is not necessarily the circuit sent to a processor. Transpilation maps it to the device’s supported instructions and topology, and its routing, optimization, and pass choices can affect the physical circuit. Save the transpiled artifact and its configuration, not only the high-level circuit. IBM’s Qiskit documentation describes stochastic transpilation and recommends setting and recording seed_transpiler when repeatable compiler output is needed. The appropriate options can depend on the SDK and version, so preserve those version details rather than assuming a seed alone makes the entire experiment deterministic.
Reconstruct, where possible, the backend or device, submitted and transpiled circuits, layout, routing, optimization settings, transpiler seed, shots, mitigation settings, and calibration context. If the agent cannot provide this trace, do not present its output as a fully verified result.
Rerun in stages, not as one opaque repeat
- Recreate the computational setup. Use the recorded inputs, code, dependencies, environment, and settings. Set random seeds wherever the relevant components support them, and note any component whose randomness cannot be controlled.
- Check compilation and analysis separately. Compare the generated circuit and intermediate outputs, then inspect whether the analysis script produces the expected value from the same recorded data. This can expose a software, configuration, or analysis mismatch before another hardware run complicates the diagnosis.
- Repeat the hardware measurement. Retain raw outcomes and job metadata for each run. Record the execution context and relevant calibration information; IBM Research describes Qiskit Experiments as providing characterization, calibration, and verification experiments.
- Compare against a predeclared criterion. State whether the test requires an identical computational artifact, the same scientific conclusion, or statistical agreement within a justified tolerance. Do not treat bitwise identity of stochastic measurements as the default standard.
Calibration and execution conditions are part of the record, not incidental notes: a later run may take place under a different hardware context. The comparison should make that difference visible rather than attributing every changed count to the agent or every changed conclusion to the processor.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check the claim independently
Have a quantum-domain researcher inspect the derivation and the mapping from the mathematical claim to the circuit, observable, units, and analysis. Where feasible, test small instances against known results, compare with a simulator or independent implementation, or use a suitable verification experiment. These checks can reveal mistakes, but they do not establish that every interesting quantum result can be verified efficiently on a classical computer. State the limits of the check you used.
For the AI contribution, also preserve information about data collection and curation, model selection, and training details when they are available and relevant. A 2021 Nature Computational Science editorial, “Moving towards reproducible machine learning,” makes those reporting concerns for machine learning; they are useful context, not a quantum-specific rule.
Rank #4
Report the discrepancy and calibrate the claim
Present the original output, the conditions of each rerun, the observed differences, and the checks performed. Explain whether the mismatch appears in the computational pipeline, the compiled circuit, hardware outcomes, or analysis—and what remains unresolved. Do not silently discard failed runs or select only the result that matches an expectation.
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- If you find an error, correct the analysis or record and explain the correction.
- If provenance is incomplete or runs remain inconsistent, describe the claim as preliminary and seek independent review.
- If the result survives the checks, present it as a candidate finding with the validation performed and its limits made clear.
Describe the agent’s role and the human validation transparently, and check the requirements of the specific journal, funder, and institution. There is no single current AI-agent authorship or disclosure rule established across all venues.
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