Fault Injection Attacks on Machine Learning-based Quantum Computer Readout Error Correction

TL;DR

This paper investigates how physical fault injections can compromise machine learning-based quantum readout error correction, revealing layer-dependent vulnerabilities and structured readout failures, thus emphasizing the need for fault-tolerant designs.

Contribution

First analysis of fault injection susceptibility in ML-based quantum readout correction, demonstrating layer-dependent vulnerabilities and structured error patterns in quantum systems.

Findings

Fault susceptibility varies by layer, with early layers more vulnerable.

Single-shot glitches can cause structured, non-random readout errors.

Fault injection can significantly degrade readout accuracy.

Abstract

Machine-learning (ML) classifiers are increasingly used in quantum computing systems to improve multi-qubit readout discrimination and to mitigate correlated readout errors. These ML classifiers are an integral component of today's quantum computer's control and readout stacks. This paper is the first to analyze the susceptibility of such ML classifiers to physical fault-injection which can result in generation of incorrect readout results from quantum computers. The study targets 5-qubit (thus 32-class) readout error-correction model. Using the ChipWhisperer Husky for physical voltage glitching, this work leverages an automated algorithm for scanning the fault injection parameter search space to find various successful faults in all the layers of the target ML model. Across repeated trials, this work finds that fault susceptibility is strongly layer-dependent: early-layers demonstrate higher rates of misprediction when faults are triggered in them, whereas later layers have smaller misprediction rates. This work further characterizes the resulting readout failures at the bitstring level using Hamming-distance and per-bit flip statistics, showing that single-shot glitches can induce structured readout corruption rather than purely random noise. These results motivate treating ML-based quantum computer readout and readout correction as a security-critical component of quantum systems and highlight the need for lightweight, deployment-friendly fault detection and redundancy mechanisms in the quantum computer readout pipelines.