QuantumScouter: Reinforcement Learning-Based Optimization of Variational Quantum Circuits for Differential Cryptanalysis

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Classical deep learning for differential cryptanalysis requires millions of ciphertext pairs, rendering attacks infeasible or easily detectable. This work overcomes this data limitation by introducing quantum differential distinguishers, enabling a practical attacker model where executing few queries is feasible. We design these distinguishers via quantum machine learning based on variational quantum circuits. To address circuit design challenges, we propose QuantumScouter, a reinforcement learn