Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks

• URL: http://arxiv.org/abs/2604.06135v1
• Date: Tue, 07 Apr 2026 17:44:37 GMT
• Title: Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks
• Abstract summary: Efficient data loading remains a bottleneck for near-term quantum machine-learning.<n>We introduce Shot-Based Quantum coherence (SBQE), a data embedding strategy that distributes the hardware's native resource, shots.<n>We show that SBQE is structurally equivalent to a multilayer perceptron whose weights are realised by quantum circuits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 non-linear activation functions. We show that SBQE is structurally equivalent to a multilayer perceptron whose weights are realised by quantum circuits, and we describe a hardware-compatible implementation protocol. Benchmarks on Fashion MNIST and Semeion handwritten digits, with ten independent initialisations 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) and 80.95% +/- 0.10% on Fashion MNIST (exceeding amplitude encoding by +2.0% and a linear multilayer perceptron by +1.3%), all without any data-encoding gates.

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