Toward Secure Multi-Tenancy in Quantum Computing: A Software-First Approach to Detecting Crosstalk-Induced Fidelity Degradation with Machine Learning - Sarker, Shaswato (2025)
Quantum computers accessed through the cloud may soon allow multiple users to share hardware simultaneously, easing resource constraints. That convenience creates the risk of quantum crosstalk, where operations on one qubit can perturb a neighboring one, serving as an attack vector. This thesis investigated whether classical machine-learning models, trained on simulated quantum data, can detect and quantify the degradation caused by crosstalk with no prior quantum knowledge. A total of 4566 ten-qubit MNISQ circuits encoding MNIST images were run on a simulator modeled after IBM’s 127-qubit Brisbane device, with injected gate, relaxation, readout, and both coherent and stochastic crosstalk errors as noise models. From the resulting density matrices and original circuits, we built a dataset combining complex quantum features with simple classical descriptors like circuit depth and gate counts. We then designed TripleBranchNet, a model that merges three branches: a convolutional neural network (CNN) over the off-diagonal real and imaginary parts of the density matrix, a multilayer perceptron (MLP) for the diagonal terms, and another MLP for classical features. Trained on 80% of the data, the best model reached a mean absolute error of 0.0055 and an \(R^{2}\) score of 0.5131 on the unseen test set. The results show that crosstalk leaves a detectable imprint in the density matrix, one visible and learnable to classical AI models. This points to the possibility of software-level monitoring without hardware changes. Still, about 49% of variance remains unexplained, and real-device validation is required before deployment. The work offers a preliminary, practical step toward AI-assisted safeguards as quantum multi-tenancy grows with the proof-of-concept TripleBranchNet model.