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arXiv 2510.03389quant-phcs.LG
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量子特征映射学习:降低资源开销

Quantum feature-map learning with reduced resource overhead

Department of Computer Science and Institute of Applied Mathematics, University of British Columbia (UBC), Vancouver, B.C. V6T 1Z4, Canada(计算机科学系和应用数学研究所,不列颠哥伦比亚大学(UBC),温哥华,B.C. V6T 1Z4,加拿大);Stewart Blusson Quantum Matter Institute (QMI), Vancouver, B.C. V6T 1Z4, Canada(斯图尔特·布卢森量子物质研究所(QMI),温哥华,B.C. V6T 1Z4,加拿大);Institute of Physics, University of Freiburg, Freiburg (Breisgau), 79104, Germany(物理研究所,弗赖堡大学,弗赖堡(巴登-符腾堡),79104,德国);Department of Chemistry, University of British Columbia (UBC), Vancouver, B.C. V6T 1Z1, Canada(化学系,不列颠哥伦比亚大学(UBC),温哥华,B.C. V6T 1Z1,加拿大)

Jonas Jäger, Philipp Elsässer, Elham Torabian

AI总结:

提出Q-FLAIR算法,通过部分解析重构将工作负载转移到经典计算机,显著降低量子资源开销,在真实IBM设备上仅用4小时即在完整MNIST数据集上达到90%以上准确率。

摘要:

当前的量子计算机需要算法经济地使用有限资源。在量子机器学习中,成功取决于量子特征映射,它将经典数据嵌入到量子比特的状态空间中。我们引入了通过解析迭代重构的量子特征映射学习(Q-FLAIR),这是一种在迭代特征映射电路构建中减少量子资源开销的算法。它通过部分解析重构量子模型,仅使用少量评估就将工作负载转移到经典计算机上。对于每次探测到的门添加到拟设中,数据特征和权重参数的同时选择和优化则完全在经典计算机上进行。集成到量子神经网络和量子核支持向量分类器中,Q-FLAIR展示了最先进的基准性能。由于资源开销与特征维度解耦,我们在真实的IBM设备上仅用四小时就训练了一个量子模型,在完整分辨率MNIST数据集(784个特征,数字3 vs 5)上达到了超过90%的准确率。这样的结果以前是无法实现的,因为特征维度会极大地增加固定拟设的硬件需求以及自适应拟设的搜索成本。此外,Q-FLAIR展示了针对直接经典建模的去量子化鲁棒性,满足了文献中罕见的基准,这是潜在量子优势的必要条件。通过超越黑盒优化重新思考特征映射学习,这项工作为在现实问题和近期量子计算机上实现量子机器学习迈出了具体的一步。

英文摘要:

Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature-maps, which embed classical data into the state space of qubits. We introduce Quantum Feature-Map Learning via Analytic Iterative Reconstructions (Q-FLAIR), an algorithm that reduces quantum resource overhead in iterative feature-map circuit construction. It shifts workloads to a classical computer via partial analytic reconstructions of the quantum model, using only a few evaluations. For each probed gate addition to the ansatz, the simultaneous selection and optimization of the data feature and weight parameter is then entirely classical. Integrated into quantum neural network and quantum kernel support vector classifiers, Q-FLAIR shows state-of-the-art benchmark performance. Since resource overhead decouples from feature dimension, we train a quantum model on a real IBM device in only four hours, surpassing 90% accuracy on the full-resolution MNIST dataset (784 features, digits 3 vs 5). Such results were previously unattainable, as the feature dimension prohibitively drives hardware demands for fixed and search costs for adaptive ansätze. Furthermore, Q-FLAIR demonstrates de-quantization robustness against direct classical modeling, satisfying a benchmark rare in the literature and a necessary condition for potential quantum advantage. By rethinking feature-map learning beyond black-box optimization, this work takes a concrete step toward enabling quantum machine learning for real-world problems and near-term quantum computers.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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