aiqbench analysis
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
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 250 words
