Qualification
Assessment of experimental data, syndromes, and observable conditions before correction decisions.
LambdaInfo develops Lambda-Q Gatekeeper, a classical supervision and qualification architecture for quantum error correction workflows.
Our research investigates when decoding decisions effectively reduce logical errors and when they may introduce or amplify degradation, creating an independent analysis layer between QPU data, the decoder, and the logical result.
Lambda-Q Gatekeeper is designed to operate alongside existing decoders. Rather than replacing them, it introduces an independent qualification layer that analyzes experimental data, syndromes, decoder behavior, and risk conditions before a decision is validated.

Assessment of experimental data, syndromes, and observable conditions before correction decisions.
Identification of scenarios in which decoder decisions may introduce or amplify logical errors.
Blocking degrading decisions while explicitly monitoring false veto and net gain.
Traceability across RAW, decoder, and Gatekeeper decisions for independent validation.
Lambda-Q development is supported by experimental analyses conducted across multiple quantum platforms, technologies, and independent campaigns. This diversity is used to compare hardware-specific behavior and identify potentially generalizable patterns.
Platform names are referenced solely to identify the quantum systems and datasets analyzed in our research. No partnership, endorsement, or commercial affiliation is implied unless explicitly stated.

When a correction decision should be trusted.
Detection of decisions that introduce or amplify errors.
Comparison across technologies and independent campaigns.
Traceability and qualification across quantum workflows.
LambdaInfo is seeking collaboration with quantum hardware manufacturers, universities, research centers, laboratories, and quantum infrastructure providers for independent validation, new experimental campaigns, and proof-of-concept development.