MicroCloud Hologram unveils quantum spectral filter tech for hybrid graph neural networks

MicroCloud Hologram stock rises after unveiling quantum spectral filter tech By Investing.com

Investing.com -- MicroCloud Hologram Inc. (NASDAQ:HOLO) stock climbed 5.2% on Monday after the technology service provider announced the release of learnable quantum spectral filter technology for hybrid graph neural networks. The new technology proposes a quantum-classical hybrid graph neural network architecture that maps the graph Laplacian operator to a trainable quantum circuit. This enables graph signal processing to gain exponential compression capability, representing a significant advancement in quantum graph machine learning. According to the company, the quantum spectral filter fuses graph convolution and pooling operations into a complete quantum computing process. A key advantage is the technology's ability to directly map high-dimensional graph signals to low-dimensional space, achieving unified convolution and pooling functions. The company highlighted that for networks with one million nodes, where classical spectral convolution would be computationally prohibitive, their quantum circuit requires only about 20 qubits. This dramatic reduction in computational requirements could make large-scale graph learning more accessible for industrial applications. MicroCloud Hologram's approach uses a logarithmic encoding method to reduce the number of qubits needed, representing an N-dimensional feature space using just log(N) qubits. The system forms an end-to-end trainable hybrid graph neural network through classical-quantum hybrid optimization. The technology could potentially address challenges in processing massive graphs in fields such as social media, traffic flow networks, and internet connectivity, where nodes can number in the tens or hundreds of millions. This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.

Microcloud Hologram Inc. Releases Learnable Quantum Spectral Filter Technology for Hybrid Graph Neural Networks

MicroCloud Hologram Inc. released learnable quantum spectral filter technology for hybrid graph neural networks. This achievement proposes a brand-new quantum-classical hybrid graph neural network foundational architecture. By mapping the graph Laplacian operator to a trainable quantum circuit, it enables graph signal processing to gain exponential compression capability and a new computational perspective, representing a key step for quantum graph machine learning toward practicalization. HOLO's this technology proposes a quantum spectral filter that fuses graph convolution and pooling operations into a complete quantum computing process. The input signal is loaded into the quantum state using amplitude encoding or probability encoding. The quantum circuit performs spectral transformation based on the graph structure. After passing through learnable rotation gates and controlled gates, the measurement results of the output state naturally form an n-dimensional probability distribution vector, where n = log(N). This property enables the quantum circuit to directly map high-dimensional graph signals to low-dimensional space, achieving a unified function of convolution + pooling. HOLO points out that the quantum measurement process is essentially a structured nonlinear mapping, capable of upcoming the complex structural search problems in classical GNN pooling operations. Large-scale graph learning has always been a difficult problem in the industrial field. Domains such as social media, traffic flow networks, and internet connectivity graphs each have tens of millions or even hundreds of millions of nodes. classical GNNs typically require large amounts of video memory, long-duration matrix multiplications, complex sparse matrix management, and massive convolution filter parameters. In contrast, quantum spectral filters provide a disruptive solution. As the number of nodes grows exponentially, the required qubits grow only logarithmically, making it a natural choice for future quantum-classical GNNs. Particularly in the current stage where quantum hardware is about to enter the medium-scale phase, this method with low qubit demand and high structural utilization offers excellent implementation possibilities. HOLO believes that rather than waiting for the full maturation of quantum hardware, it is more important to build quantum frontier algorithm infrastructure in advance. This quantum spectral filter has established a complete research route, deeply integrating graph structures with quantum learnable models, laying an algorithms for future hardware development. With the official release of HOLO's learnable quantum spectral filter for hybrid graph neural networks, the fusion of quantum computing and graph neural networks has taken a key step forward. HOLO not only demonstrates the enormous potential of quantum circuits in complex structure learning but also opens up a practical and scalable technical path for future quantum machine learning. The successful implementation of this technology is driving graph neural networks toward a true quantum era. In the future, as quantum hardware gradually matures, such learnable quantum filters will become core components in numerous practical applications, constituting a brand-new cornerstone for the integrated development of graph computing, artificial intelligence, and physical computing.