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Summary

This summary is machine-generated.

Quantum tomography data can verify experimental assumptions and detect errors without extra measurements. This statistical analysis helps identify issues like signal drift in quantum state preparation and measurement.

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Area of Science:

Quantum information science
Experimental quantum physics
Statistical data analysis

Background:

Quantum tomography is essential for characterizing quantum states and processes.
Experimental quantum systems are prone to systematic errors like drifts and instabilities.
Validating the accuracy of state preparation and measurement is crucial for reliable quantum experiments.

Purpose of the Study:

To demonstrate that standard quantum tomography data can be leveraged for self-validation of experimental assumptions.
To show that systematic errors can be detected without dedicated validation measurements.
To compare the ease of validation between minimal and overcomplete quantum tomography schemes.

Main Methods:

Statistical analysis of data acquired during generic quantum tomography experiments.
Identifying systematic errors by examining deviations from expected results within the existing dataset.
Illustrating the method with experimental quantum homodyne tomography data.

Main Results:

Quantum tomography data inherently contains information for verifying state preparation and measurement assumptions.
Systematic errors, such as drifts and instabilities, can be identified using statistical analysis of existing tomography data.
Overcomplete tomography schemes are more amenable to validation than minimal schemes, like symmetric informationally complete measurements.

Conclusions:

The information gathered during quantum tomography is sufficient for validating experimental setups and assumptions.
This approach offers a practical method for detecting and characterizing systematic errors in quantum experiments.
The findings have implications for improving the reliability and accuracy of quantum information processing and metrology.