Hardware-Agnostic Modeling of Quantum Side-Channel Leakage via Conditional Dynamics and Learning from Full Correlation Data

2026-02-17

AI Summary

This work addresses the challenge of side-channel information leakage in quantum computing arising from probe–target qubit interactions, particularly when circuit depth increases and conventional methods struggle with exponentially growing correlated data. The authors propose a hardware-agnostic sequential coherent side-channel model that captures leakage behavior through full-correlation data logging and integrates conditional dynamics with a parameterized machine learning mapping to enable generalized decoding of hidden gate sequences. Key innovations include a depth-dependent leakage envelope theory for predicting an optimal “Goldilocks” coupling band and a decoder architecture that generalizes across coupling strengths and noise configurations without retraining. Experiments demonstrate that sequence recovery performance is sharply concentrated within this coupling band and exhibits predictable degradation under decoherence and finite sampling.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

We study a sequential coherent side-channel model in which an adversarial probe qubit interacts with a target qubit during a hidden gate sequence. Repeating the same hidden sequence for $N$ shots yields an empirical full-correlation record: the joint histogram $\widehat{P}_g(b)$ over probe bit-strings $b\in\{0,1\}^k$, which is a sufficient statistic for classical post-processing under identically and independently distributed (i.i.d.) shots but grows exponentially with circuit depth. We first describe this sequential probe framework in a coupling- and measurement-agnostic form, emphasizing the scaling of the observation space and why exact analytic distinguishability becomes intractable with circuit depth. We then specialize to a representative instantiation (a controlled-rotation probe coupling with fixed projective readout and a commuting $R_x$ gate alphabet) where we (i) derive a depth-dependent leakage envelope whose maximizer predicts a "Goldilocks" coupling band as a function of depth, and (ii) provide an operational decoder, via machine learning, a single parameter-conditioned map from $\widehat{P}_g$ to Alice's per-step gate labels, generalizing across coupling and noise settings without retraining. Experiments over broad coupling and noise grids show that strict sequence recovery concentrates near the predicted coupling band and degrades predictably under decoherence and finite-shot estimation.

Problem

• quantum side-channel leakage
• hardware-agnostic modeling
• full correlation data
• coherent interaction
• circuit depth scaling

Innovation

• quantum side-channel
• hardware-agnostic modeling
• conditional dynamics
• full-correlation learning
• leakage envelope