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.
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.
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.
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.
In quantum computing, the efficient utilization of computational resources is of paramount importance. HOLO's nonlinear quantum optimization algorithm technology not only achieves a breakthrough in computational performance but also significantly improves resource utilization efficiency.
First, compared to existing methods, HOLO's algorithm reduces measurement complexity to a polynomial level. Measurement complexity is a critical metric in quantum computing, directly impacting the execution time and accuracy of computational tasks. Traditional quantum optimization methods typically require a large number of repeated measurements, whereas HOLO's algorithm optimizes measurement strategies, significantly reducing the number of measurements while maintaining computational accuracy. This leads to a notable improvement in overall computational efficiency.
Second, HOLO's algorithm doubles computational speed while halving the demand for quantum resources. This breakthrough stems from HOLO's optimized quantum circuit architecture. Compared to traditional approaches, HOLO's shallow circuit design can complete computational tasks in less time while reducing the need for qubits and quantum gate operations. In other words, this algorithm technology not only runs faster but also imposes lower hardware requirements, making it more feasible for implementation on current quantum computers.
In experiments, HOLO employed an efficient simulation strategy based on tensor methods. While traditional quantum computing simulations face exponential scaling issues as the number of qubits increases, our algorithm, with its optimized tensor network structure, enables computations to be completed on a single GPU even with 512 qubits. This experimental result not only validates the efficiency of our algorithm but also further demonstrates its potential for application in large-scale optimization problems.
HOLO's nonlinear quantum optimization algorithm has achieved groundbreaking progress in theoretical research while also showcasing broad prospects across multiple real-world application scenarios.
In the financial sector, optimization algorithms are widely used in tasks such as portfolio optimization and risk management. HOLO's algorithm can compute optimal investment portfolios in a shorter time and effectively address non-convex optimization challenges arising from market fluctuations. This opens up new possibilities for the application of quantum computing in the financial industry.
In logistics and supply chain management, the ability to solve optimization problems directly impacts overall efficiency. HOLO's technology can be applied to tasks such as intelligent scheduling and route planning, helping businesses utilize resources more efficiently, thereby reducing costs and improving service quality.
Furthermore, in the fields of artificial intelligence and machine learning, HOLO's algorithm can serve as an efficient optimization tool for training deep learning models. Leveraging the parallel computing capabilities of quantum computing, our algorithm can provide faster convergence speeds during the optimization process, laying the groundwork for future quantum artificial intelligence.
HOLO remains committed to advancing the development of quantum computing technology and continuously exploring new optimization methods. In the future, plans are in place to further refine this technology to accommodate larger-scale computational tasks. As quantum computing technology continues to progress, there is every reason to believe that efficient quantum optimization algorithms will play an increasingly vital role. HOLO's research not only offers a new perspective on quantum optimization but also establishes a solid foundation for the industrial application of quantum computing. In the forthcoming era of quantum computing, we will continue to lead technological innovation, contributing even more to global scientific and technological advancement.
About MicroCloud Hologram Inc.
MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud’s holographic technology services include high-precision holographic light detection and ranging (“LiDAR”) solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems (“ADAS”). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud’s holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud’s holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/
Safe Harbor Statement
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