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[2506.08749] Superposed parameterised quantum circuits
Summary
The paper introduces superposed parameterised quantum circuits, enhancing quantum machine learning by embedding multiple parameter sets in a single circuit, improving expressivity and scalability.
Why It Matters
This research addresses the limitations of existing quantum machine learning approaches by proposing a novel architecture that allows for greater expressivity and efficiency. The findings could significantly impact the development of quantum algorithms capable of handling complex data patterns, thus advancing the field of quantum computing and its applications in machine learning.
Key Takeaways
- Superposed parameterised quantum circuits can embed an exponential number of parameterised sub-models.
- The architecture allows for parallel training of multiple parameter sets, enhancing efficiency.
- Numerical experiments show significant reductions in mean-squared error and improved classification accuracy.
- The approach broadens representational power beyond conventional quantum kernels.
- This method offers a hardware-efficient route for developing deeper quantum circuits.
Quantum Physics arXiv:2506.08749 (quant-ph) [Submitted on 10 Jun 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Superposed parameterised quantum circuits Authors:Viktoria Patapovich, Maniraman Periyasamy, Mo Kordzanganeh, Alexey Melnikov View a PDF of the paper titled Superposed parameterised quantum circuits, by Viktoria Patapovich and 3 other authors View PDF HTML (experimental) Abstract: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 ...
