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Quantum Cryptographic Primitives based on Parameterised Quantum Circuits

Mattia Fiorentini, David Garvin, Alexei Kondratyev, Marco Paini

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.2139/ssrn.6114006

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Quantum machine learning has shown promise for high-dimensional data analysis, yet many existing approaches rely on linear unitary operations and shared trainable parameters across outputs. These constraints limit expressivity and scalability relative to the multi-layered, non-linear architectures of classical deep networks. We introduce superposed parameterised quantum circuits to overcome these limitations. By combining flip-flop quantum random-access memory with repeat-until-success protocols, a superposed parameterised quantum circuit embeds an exponential number of parameterised sub-models in a single circuit and induces polynomial activation functions through amplitude transformations and post-selection. We provide an analytic description of the architecture, showing how multiple parameter sets are trained in parallel while non-linear amplitude transformations broaden representational power beyond conventional quantum kernels. Numerical experiments underscore these advantages: on a 1D step-function regression a two-qubit superposed parameterised quantum circuit cuts the mean-squared error by three orders of magnitude versus a parameter-matched variational baseline; on a 2D star-shaped two-dimensional classification task, introducing a quadratic activation lifts accuracy to 81.4\% and reduces run-to-run variance three-fold. These results position superposed parameterised quantum circuits as a hardware-efficient route toward deeper, more versatile parameterised quantum circuits capable of learning complex decision boundaries.

arXiv:2506.08749•Jun 10, 2025•0 citations
With the power of quantum information, we can achieve exciting and classically impossible cryptographic primitives. However, almost all quantum cryptography faces extreme difficulties with the near-term intermediate-scale quantum technology (NISQ technology); namely, the short lifespan of quantum states and limited sequential computation. At the same time, considering only limited quantum adversaries may still enable us to achieve never-before-possible tasks. In this work, we consider quantum cryptographic primitives against limited quantum adversaries - depth-bounded adversaries. We introduce a model for (depth-bounded) NISQ computers, which are classical circuits interleaved with shallow quantum circuits. Then, we show one-time memory can be achieved against any depth-bounded quantum adversaries introduced in the work, with their depth being any pre-fixed polynomial. Therefore we obtain applications like one-time programs and one-time proofs. Finally, we show our one-time memory has correctness even against constant-rate errors.
Quantum machine learning has shown promise for high-dimensional data analysis, yet many existing approaches rely on linear unitary operations and shared trainable parameters across outputs. These constraints limit expressivity and scalability relative to the multi-layered, non-linear architectures of classical deep networks. We introduce superposed parameterised quantum circuits to overcome these limitations. By combining flip-flop quantum random-access memory with repeat-until-success protocols, a superposed parameterised quantum circuit embeds an exponential number of parameterised sub-models in a single circuit and induces polynomial activation functions through amplitude transformations and post-selection. We provide an analytic description of the architecture, showing how multiple parameter sets are trained in parallel while non-linear amplitude transformations broaden representational power beyond conventional quantum kernels. Numerical experiments underscore these advantages: on a 1D step-function regression a two-qubit superposed parameterised quantum circuit cuts the mean-squared error by three orders of magnitude versus a parameter-matched variational baseline; on a 2D star-shaped two-dimensional classification task, introducing a quadratic activation lifts accuracy to 81.4\% and reduces run-to-run variance three-fold. These results position superposed parameterised quantum circuits as a hardware-efficient route toward deeper, more versatile parameterised quantum circuits capable of learning complex decision boundaries.