发表机构
blueqat Inc.（Blueqat公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出无需磁梯度、仅用双自旋单元与海森堡交换相互作用的QML架构，在MNIST分类中准确率达90.9%±0.2%，优于经典基线，对缺陷鲁棒，可简化硬件实现。
AI 中文摘要
标准通用量子计算使用仅交换量子比特时，每个逻辑量子比特通常需要三个物理自旋，导致显著的硬件开销。相反，双自旋单元具有更高的密度，但依赖局部磁场梯度进行控制，增加了集成复杂度。本文提出一种资源高效的量子机器学习（QML）架构，仅使用最少的双自旋单元和海森堡交换相互作用即可实现高表达性，无需任何磁梯度。我们将范式从基于通用门的控制转变为利用自旋链的固有时域动力学作为学习资源。对MNIST数字分类的数值模拟表明，通过利用对间交换耦合，可绕过孤立自旋对的对称性保护约束。这种干涉介导的态混合显著增强了希尔伯特空间的表达性。该模型在完整的10000张图像MNIST测试集上，经5个独立随机种子得到的测试集准确率达到90.9%±0.2%。在相同线性读出下，训练后的量子特征图（88.1%）明显优于相同PCA输入的经典线性基线（83.2%），以及相同动力学的未训练（储备池式）版本（53.0%），表明学习到的、依赖输入的交换脉冲实现了真正的非线性可训练特征图。该协议对实验相关缺陷也具有鲁棒性：在10%准静态脉冲面积噪声下，准确率仍保持在89.9%；当每个可观测量从10^3次测量采样中估计时，准确率为89.8%。这些结果表明，可在最简单的半导体自旋链硬件上执行具有竞争力的QML，无需易泄漏的编码或复杂微磁体集成。
英文摘要
Standard universal quantum computing using exchange-only qubits typically requires three physical spins per logical qubit, leading to significant hardware overhead. Conversely, two-spin units offer higher density but rely on local magnetic field gradients for control, increasing integration complexity. In this paper, we propose a resource-efficient quantum machine learning (QML) architecture that achieves high expressibility using minimal two-spin units and Heisenberg exchange interactions alone, without any magnetic gradients. We shift the paradigm from universal gate-based control to utilizing the intrinsic, time-domain dynamics of a spin chain as a learning resource. Numerical simulations on MNIST digit classification demonstrate that the symmetry-protected constraints of isolated spin pairs are bypassed by leveraging inter-pair exchange coupling. This interference-mediated state mixing significantly enhances the expressibility of the Hilbert space. The model reaches a test-set accuracy of 90.9% +/- 0.2% over five independent seeds on the full 10,000-image MNIST test set. Under an identical linear readout, the trained quantum feature map (88.1%) clearly outperforms a classical linear baseline on the same PCA inputs (83.2%) as well as an untrained (reservoir-style) version of the same dynamics (53.0%), demonstrating that the learned, input-dependent exchange pulses implement a genuinely non-linear and trainable feature map. The protocol is also robust to experimentally relevant imperfections: accuracy remains at 89.9% under 10% quasi-static pulse-area noise and at 89.8% when every observable is estimated from 10^3 measurement shots. These findings suggest that competitive QML can be executed on the simplest possible semiconductor spin-chain hardware, bypassing the need for leakage-prone encodings or complex micro-magnet integration.
Comments9 pages, 10 figures. Code and data: https://github.com/minatoyuichiro/teex-mnist
