发表机构
RIKEN AIP（理化学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出量子数据集蒸馏（QDD），将数据集直接蒸馏为浅层电路，使样本与加载电路合一，在MNIST和Fashion-MNIST上以极小电路数达到全数据训练精度，并显著减少测量开销，且已在真实量子硬件上验证。
AI 中文摘要
在量子机器学习中，训练一个量子模型需要将每个样本通过加载电路制备为量子态，而该电路必须在每个训练步骤的每次测量（shot）中重新执行。因此，总负担随样本数量和制备成本两者同时增长。现有方法要么降低单个输入的加载成本，要么在应用单独的量子编码之前对数据进行蒸馏并压缩其表示。这种分离可能导致生成的样本制备成本高昂，或在后续编译为浅层电路时造成额外的信息损失。我们提出量子数据集蒸馏（QDD），该方法将整个数据集直接蒸馏为一小组浅层电路。每个合成样本被参数化为一个阶梯电路，对应一个低秩张量网络，从而使样本与其加载电路成为同一对象。电路参数在显式加载预算下，通过分布匹配使用精确梯度进行经典优化，无需事后态制备合成。在MNIST和Fashion-MNIST上，每类仅需10个电路（为完整数据集大小的0.0017倍）即可达到与全数据训练相当的准确率，并在匹配的样本和加载预算下优于选择基线。在有限测量训练中，QDD以超过100倍的累计测量次数减少达到全数据准确率的95%。此外，我们在真实量子硬件上验证了QDD，展示了其实用部署潜力。
英文摘要
In quantum machine learning, training a quantum model requires each sample to be prepared as a quantum state by a loading circuit that must be re-executed for every shot at every training step. The total burden therefore scales with both the number of samples and the cost of preparation. Existing approaches reduce the loading cost of individual inputs or distill data and compress their representations before applying a separate quantum encoding. This separation can leave resulting samples costly to prepare or cause additional loss of the information during subsequent compilation into shallow circuits. We propose quantum dataset distillation (QDD), which distills the full dataset directly into a small set of shallow circuits. Each synthetic sample is parameterized as a staircase circuit corresponding to a low-rank tensor network, making the sample and its loading circuit the same object. The circuit parameters are optimized classically with exact gradients using distribution matching under an explicit loading budget, without post-hoc state-preparation synthesis. On MNIST and Fashion-MNIST, only 10 circuits per class ($0.0017\times$ the full dataset size) achieve accuracy comparable to full-data training and outperform selection baselines under matched sample and loading budgets. In finite-shot training, QDD reaches 95\% of the full-data accuracy with more than $100\times$ fewer cumulative shots. Additionally, we validate QDD on real quantum hardware, demonstrating its practical deployment potential.
