  Cryptanalysis on standard quantum cryptographic systems generally involves
finding optimal adversarial attack strategies on the underlying protocols. The
core principle of modelling quantum attacks in many cases reduces to the
adversary's ability to clone unknown quantum states which facilitates the
extraction of some meaningful secret information. Explicit optimal attack
strategies typically require high computational resources due to large circuit
depths or, in many cases, are unknown. In this work, we propose variational
quantum cloning (VQC), a quantum machine learning based cryptanalysis algorithm
which allows an adversary to obtain optimal (approximate) cloning strategies
with short depth quantum circuits, trained using hybrid classical-quantum
techniques. The algorithm contains operationally meaningful cost functions with
theoretical guarantees, quantum circuit structure learning and gradient descent
based optimisation. Our approach enables the end-to-end discovery of hardware
efficient quantum circuits to clone specific families of quantum states, which
in turn leads to an improvement in cloning fidelites when implemented on
quantum hardware: the Rigetti Aspen chip. Finally, we connect these results to
quantum cryptographic primitives, in particular quantum coin flipping. We
derive attacks on two protocols as examples, based on quantum cloning and
facilitated by VQC. As a result, our algorithm can improve near term attacks on
these protocols, using approximate quantum cloning as a resource.
