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.

翻译：高效的数据加载仍是近期量子机器学习的发展瓶颈。现有方案（角度编码、振幅编码和基编码）要么未能充分利用指数级增长的希尔伯特空间容量，要么需要超过含噪中等规模量子硬件相干预算的电路深度。我们提出基于观测的量子编码（SBQE），这是一种数据嵌入策略，它能根据数据依赖的经典分布，在多个初始量子态上分配硬件原生资源——观测次数。通过将观测次数视为可学习的自由度，SBQE生成混合态表示，其期望值与经典概率呈线性关系，因此可与非线性激活函数组合。我们论证了SBQE在结构上等价于权重由量子电路实现的多层感知机，并描述了兼容硬件的实现协议。在Fashion MNIST和Semeion手写数字数据集上的基准测试中（每个模型进行十次独立初始化），SBQE在Semeion数据集上达到89.1%±0.9%的测试准确率（相比振幅编码误差降低5.3%，与等宽度经典网络持平），在Fashion MNIST数据集上达到80.95%±0.10%（比振幅编码高出2.0%，比线性多层感知机高出1.3%），且全程无需使用数据编码门。