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

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 test accuracy on Semeion (reducing error by relative to amplitude encoding and matching a width‐matched classical network), on Fashion MNIST (exceeding amplitude encoding by and a linear multilayer perceptron by ), and on MNIST (exceeding amplitude encoding by percentage points and the width‐matched classical network by ), all without any data‐encoding gates.