PQC Side-Channel Analysis
I designed a falsifiable evaluation framework for testing whether quantum-kernel methods can reduce ML-KEM/Kyber side-channel key-recovery trace requirements against strong classical baselines.
Can quantum-kernel methods recover secret information from ML-KEM/Kyber power traces using fewer observations than strong classical baselines, or do classical methods remain superior under a fair comparison?
I mapped a staged path: validate the profiling side-channel pipeline on public ASCAD AES traces, gate the transition to Kyber on access to target-specific power traces, and compare reproducible CNN and quantum-kernel key-rank curves under documented feature constraints. ASCAD validates the methodology; it does not stand in for a Kyber result.
The design requires signal-bearing features before quantum-model training, reproducible classical baselines, Kyber-specific data before making Kyber claims, and a Bowles-style classical-kernel control before any quantum-advantage claim. These gates make a negative result as technically useful as a positive one.
I synthesized the relevant literature, selected and presented the research direction, and defined the candidate datasets, model families, success metrics, risks, and go/no-go conditions. The completed output is a technical reference and evaluation plan; it is not a completed attack or key-recovery result.
I retain the framework as an independent research direction, ready for experimental execution once target-specific hardware and high-resolution power-trace data support a defensible comparison.