Shot-based quantum encoding: a data-loading paradigm for quantum neural networks

Abstract:Efficient data loading remains a bottleneck for near-term quantum machine learning. Existing schemes (angle, amplitude, and basis encoding) either underuse the exponential Hilbert-space capacity or require circuit depths that exceed the coherence budgets of noisy intermediate-scale quantum hardware. We introduce shot-based quantum encoding (SBQE), a data embedding strategy that distributes the hardware's native resource, shots, according to a data-dependent classical distribution over multiple initial quantum states. By treating the shot counts as a learnable degree of freedom, SBQE produces a mixed-state representation whose expectation values are linear in the classical probabilities and can therefore be composed with nonlinear activation functions. We show that SBQE is structurally equivalent to a multilayer perceptron whose weights are realized by quantum circuits, and we describe a hardware-compatible implementation protocol. Benchmarks on three image datasets, with 10 independent initializations per model, show that SBQE achieves 89.1% +- 0.9% test accuracy on Semeion (reducing error by 5.3% relative to amplitude encoding and matching a width-matched classical network), 80.95% +- 0.10% on Fashion MNIST (exceeding amplitude encoding by +2.0% and a linear multilayer perceptron by +1.3%), and 90.25% +- 0.18% on MNIST (exceeding amplitude encoding by 2.1 percentage points and the width-matched classical network by 0.3), all without any data-encoding gates.

Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2604.06135 [quant-ph]

(or arXiv:2604.06135v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2604.06135 Focus to learn more arXiv-issued DOI via DataCite

Adv. Comput. 1(1), e70004 (2026)

https://doi.org/10.1002/adco.70004 Focus to learn more DOI(s) linking to related resources

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