Beijing University of Posts and Telecommunications Fuses Cross-View Information in Quantum Multiview Kernel Learning
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Researchers at Beijing University of Posts and Telecommunications have developed a new approach to quantum machine learning that effectively combines information from multiple data sources.
The team, led by Fei Gao, proposed quantum multiview kernel learning with local information, termed L-QMVKL, to address performance limitations when processing complex data; existing quantum kernel methods often struggle with localized structural patterns due to reliance on single viewpoints and global data structure. By fusing cross-view information through a quantum multiple kernel and leveraging local data characteristics, L-QMVKL demonstrates significant accuracy improvements on the Mfeat dataset, achieving competitive results when compared to classical machine learning models. Fei Gao, a researcher at the State Key Laboratory of Networking and Switching Technology, said, “Our work holds promise for advancing the theoretical and practical understanding of quantum kernel methods.” Quantum Multiple Kernel Learning for Multiview Data Quantum computing offers the potential to solve previously unsolvable problems by exploiting exponentially large computational spaces, and new research from Beijing University of Posts and Telecommunications details a method for applying this power to complex, real-world datasets. This difficulty arises from an over-reliance on global data structure and insufficient representation of individual data views, hindering performance on intricate datasets. L-QMVKL builds upon multiple kernel learning, a technique already established for processing data from multiple sources, by constructing a “quantum multiple kernel” designed to effectively integrate information across these views. Researchers further enhanced the system by incorporating local information, aiming to capture the inherent structural details within the data itself; this is achieved through a sequential training strategy for quantum circuit parameters and combination weights, utilizing a hybrid global-local kernel alignment.
The team states that they evaluate the effectiveness of L-QMVKL through comprehensive numerical simulations on the Mfeat dataset, demonstrating significant accuracy improvements. Quantum kernel methods, while promising solutions to classically intractable problems by mapping data into expansive Hilbert spaces, have historically struggled with complex datasets exhibiting localized patterns. This limitation stems from an over-reliance on single-view feature representation and global data structure. A key innovation lies in the construction of a quantum multiple kernel, combining view-specific quantum kernels to enhance cross-view data fusion and capture nuanced relationships. Quantum kernels, by mapping data into exponentially large Hilbert spaces, offer the potential to solve problems intractable for classical models. Source: http://link.aps.org/doi/10.1103/rp2m-n8xd Tags: Quantum News There is so much happening right now in the field of technology, whether AI or the march of robots. Adrian is an expert on how technology can be transformative, especially frontier technologies. But Quantum occupies a special space. Quite literally a special space. A Hilbert space infact, haha! Here I try to provide some of the news that is considered breaking news in the Quantum Computing and Quantum tech space. Latest Posts by Quantum News: planqc Highlights Quantum Information Science Expertise in New Hire March 27, 2026 University of Manchester Builds Partnerships to Accelerate Quantum Technology Development March 27, 2026 Rigetti Computing Will Deploy Quantum Computer With Over 1,000 Qubits in UK March 27, 2026