MicroAlgo Inc. Develops Quantum Architecture Search (QAS) Technology to Enhance VQA Robustness and Trainability, Optimizing the Potential of Quantum Computing Devices

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[{"type":"text","content":"SHENZHEN, China, May 8, 2026 /PRNewswire/ -- MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of an innovative technology—Quantum Architecture Search (QAS), aimed at automatically optimizing the architecture of quantum circuits to enhance the robustness and trainability of VQA, maximizing the potential of quantum computing devices.","length":398,"tagName":"p"},{"type":"text","content":"In the traditional VQA framework, the design of quantum circuit architectures is typically performed manually or based on certain predefined standard architectures. However, the noise and errors in quantum computers are extremely severe in medium-scale devices, making circuit design a critical factor affecting VQA performance. More complex circuit architectures may enhance expressive power but simultaneously introduce more noise and errors, leading to difficulties in the training process or even complete failure.","length":518,"tagName":"p"},{"type":"text","content":"To balance the expressive power of circuit architectures and the impact of noise, MicroAlgo has proposed a Quantum Architecture Search (QAS) method. QAS optimizes VQA performance by automatically searching for quantum circuit architectures, mitigating the impact of noise on training, and finding a near-optimal circuit structure. This method not only helps improve the robustness of quantum algorithms in noisy environments but also significantly enhances their performance in practical tasks.","length":494,"tagName":"p"},{"type":"text","content":"The core idea of MicroAlgo QAS is to systematically search the architecture space of quantum circuits to find the circuit structure most suitable for a specific task. Unlike traditional design, QAS adopts an intelligent optimization approach, automatically exploring the space of circuit architectures to maximize the trainability and robustness of VQA.","length":353,"tagName":"p"},{"type":"text","content":"The design of quantum circuit architectures is not merely a matter of arranging quantum gates; it involves multiple levels of optimization, such as the selection of quantum gates, the connectivity of qubits, and the interaction patterns between qubits. QAS first defines a circuit architecture space that encompasses all possible quantum circuit configurations, including the types, ...

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