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Data re-uploading for a universal quantum classifier

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arxiv 1907.02085 v3 pith:IDQLZTKG submitted 2019-07-03 quant-ph

classification quant-ph
keywords dataquantumclassifierre-uploadinguniversalmultipleonlyqubit
verification ladder T0 review T1 audit T2 compute T3 formal
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A single qubit provides sufficient computational capabilities to construct a universal quantum classifier when assisted with a classical subroutine. This fact may be surprising since a single qubit only offers a simple superposition of two states and single-qubit gates only make a rotation in the Bloch sphere. The key ingredient to circumvent these limitations is to allow for multiple data re-uploading. A quantum circuit can then be organized as a series of data re-uploading and single-qubit processing units. Furthermore, both data re-uploading and measurements can accommodate multiple dimensions in the input and several categories in the output, to conform to a universal quantum classifier. The extension of this idea to several qubits enhances the efficiency of the strategy as entanglement expands the superpositions carried along with the classification. Extensive benchmarking on different examples of the single- and multi-qubit quantum classifier validates its ability to describe and classify complex data.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 540 citations worldwide. Full citation record

  1. When cheap gradients fail: the measurement cost of attacking quantum classifiers

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    Unbiased gradient extraction for attacking quantum classifiers costs at least Θ(d^{5/2}) shots under norm-concentration scaling, and ~d³ for tested deep circuits, so the attacker's relative cost diverges versus classi...

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    A data-agnostic circuit harmonic matrix C factorises Fourier-coefficient statistics and quantum neural tangent kernels for a broad class of re-uploading parametrised quantum circuits.

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  4. Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    Classical uncertainty quantification methods transfer to quantum machine learning; Bayesian quantum models and Gaussian dropout give the best-calibrated uncertainty estimates in small simulated experiments.

  5. Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models

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    Noise, especially decoherent gate errors, systematically reduces Fourier coefficient magnitudes, expressibility, and entangling capability of quantum Fourier models, with circuit architecture and encoding modulating t...

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