MicroCloud Hologram Inc. Develops Nonlinear Quantum Optimization Technology Based on Efficient Model Encoding

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[{"type":"text","content":"SHENZHEN, China, May 12, 2025 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announced the development of a groundbreaking nonlinear quantum optimization algorithm based on efficient model encoding technology. This algorithm significantly enhances computational efficiency while reducing the consumption of quantum resources. This innovation not only addresses the key bottlenecks of current quantum optimization methods but also demonstrates remarkable performance advantages in practical applications, paving the way for the industrial adoption of quantum computing.","length":650,"tagName":"p"},{"type":"text","content":"Traditional quantum optimization algorithms primarily rely on the Variational Quantum Algorithm (VQA) framework, where the depth of quantum circuits is often high, making the demand for computational resources difficult to meet. However, HOLO's efficient model encoding technology overcomes this limitation through two key innovations: multi-basis graph encoding and the application of nonlinear activation functions.","length":421,"tagName":"p"},{"type":"text","content":"The multi-basis graph encoding method is a novel quantum encoding strategy that effectively represents high-dimensional optimization problems with a limited number of qubits. In HOLO's approach, an optimized tensor network structure is employed to map high-dimensional optimization spaces using fewer qubits. This not only reduces the depth of quantum circuits but also improves computational efficiency.","length":408,"tagName":"p"},{"type":"text","content":"On the other hand, the introduction of nonlinear activation functions enables HOLO's optimization method to better address non-convex optimization problems. Traditional variational quantum algorithms are often constrained by the optimization landscape, easily getting trapped in local minima when dealing with complex non-convex problems. In contrast, HOLO's nonlinear activation functions can adaptively adjust the optimization path during training, allowing the algorithm to converge more efficiently to the global optimum. This innovation significantly enhances the algorithm's optimization capabilities, demonstrating greater adaptability in tackling large-scale optimization challenges.","length":703,"tagN...

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