Quantum X Labs reports a 15- to 21-fold reduction in maximum cumulative error achieved by its LayerQake technology when tested on three IBM quantum processors: ibm_fez, ibm_kingston and ibm_marrakesh. The company’s pre-registered, reproducible experiment also lowered measurement bias from approximately 11% to 15% to below 6% across the three systems, demonstrating improvements in the reliability of quantum computational results.
These gains were achieved using a problem-independent calibration approach designed to mitigate hardware inaccuracies without tailoring to each specific computation. Quantum X Labs believes this milestone is an important step in the development of its broader quantum cybersecurity and quantum computing software portfolio.
LayerQake Reduces Measurement Bias on IBM Processors
This performance boost was validated through a pre-registered, reproducible experiment conducted on three operational IBM systems: ibm_fez, ibm_kingston and ibm_marrakesh. The experiment assessed LayerQake’s ability to systematically reduce the effects of hardware noise across different quantum devices, establishing predefined success criteria focused on both measurement bias and maximum cumulative error. Before the application of Quantum X Labs’ calibration methodology, hardware-induced noise resulted in measurement bias ranging from approximately 11% to 15% across the three IBM processors.
After implementing LayerQake, the observed bias was reduced to below 6% on each system, a result the company believes provides initial evidence of the technology’s effectiveness. This reduction in bias is noteworthy because it was achieved using a problem-independent calibration approach, meaning the methodology doesn’t require tailoring to specific computational problems. The design aims to mitigate inaccuracies stemming from the hardware itself, rather than addressing errors on a case-by-case basis.
The experimental program was designed to determine if LayerQake could consistently improve accuracy across varied quantum computing hardware. Quantum X Labs established two key success criteria before the experiments began: a reduction in measurement bias and a reduction in maximum cumulative error.
The company reports that “these findings provide initial experimental evidence that LayerQake’s calibration methodology can improve the accuracy and consistency of results obtained from existing quantum computing hardware.” Hardware noise and computational inaccuracies are significant challenges currently limiting the practical performance of quantum computers, as processors are sensitive to environmental interference, imperfect control operations and measurement errors. Technologies capable of identifying and mitigating these inaccuracies are therefore critical to advancing the field. Quantum X Labs believes that LayerQake’s demonstrated ability to reduce measurement bias and cumulative errors across multiple processors highlights the potential of its technology to contribute to improved reliability in quantum computing environments.
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