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This research introduces a quantum-native data loader that leverages shared structures within real-world signals to prepare quantum states using a single, fixed circuit rather than tailoring one for each input. By learning a low-dimensional description of the dataset once, the method significantly reduces the number of parameters required per signal while maintaining competitive gate costs against existing structured loaders. Although it intentionally excludes certain cases like electrocardiograms where representation is impossible, this approach offers a scalable solution to the high overhead of quantum state preparation in practical applications.
Quantum data loading from the learned shared structure of real signals
arXiv:2610.06076v1 Announce Type: cross Abstract: Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show that the signals of a real dataset share structure that can be learned once and reused. Our quantum-native loader learns a low-dimensional description of a dataset and prepares every signal with one fixed circuit set by a few numbers. Across seven views of five public datasets it meets the targets of the strongest structured loader at equal gate cost with several times fewer numbers per signal. These numbers can be inferred from a random subset: in a preregistered blind replication the subset needed to come within ten per cent of full-signal accuracy stayed constant within a prespecified margin as signals grew sixteenfold, whereas the structured loader needed ever more. It declines what it cannot represent, covering fewer cases than that baseline and no electrocardiogram.
Abstract:Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show that the signals of a real dataset share structure that can be learned once and reused. Our quantum-native loader learns a low-dimensional description of a dataset and prepares every signal with one fixed circuit set by a few numbers. Across seven views of five public datasets it meets the targets of the strongest structured loader at equal gate cost with several times fewer numbers per signal. These numbers can be inferred from a random subset: in a preregistered blind replication the subset needed to come within ten per cent of full-signal accuracy stayed constant within a prespecified margin as signals grew sixteenfold, whereas the structured loader needed ever more. It declines what it cannot represent, covering fewer cases than that baseline and no electrocardiogram.
Quantum Physics (quant-ph); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
arXiv:2610.06076 [quant-ph]
(or arXiv:2610.06076v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.06076 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
supplementary_information.pdf
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