Quantum Trojan Detection Dataset

Quantum Trojan Detection Dataset This project focuses on the detection of trojan-injected quantum circuits using classical and quantum machine learning models. It forms part of a master’s thesis exploring security threats in quantum computing environments. Research Focus Objective: Detect anomalies and logical tampering in quantum circuits. Models Used: Classical: Random Forest Quantum: Quantum Support Vector Machine (QSVM) Circuits Analyzed: Deutsch–Jozsa (DJ) Grover’s Search Quantum Fourier Transform (QFT) Shor’s Algorithm Bernstein–Vazirani (BV) Quantum Approximate Optimization Algorithm (QAOA) Core Features Clean and malicious quantum circuit generation (Qiskit) Circuit simulation using Qiskit Aer Feature extraction from quantum circuits: Depth, gate counts, entropy, structure CSV dataset generation for training/testing Binary classification: Clean vs. Malicious Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC