MicroAlgo develops quantum circuit optimization technology

MicroAlgo Inc. (NASDAQ: MLGO) announced the development of Quantum Architecture Search (QAS) technology designed to automatically optimize quantum circuit architectures for Variational Quantum Algorithms (VQA).

The QAS method addresses challenges in quantum computing where manual circuit design often fails to balance computational power with noise interference. Traditional VQA frameworks rely on manually designed or predefined circuit architectures, which can introduce errors that disrupt training processes on quantum devices.

The technology uses reinforcement learning and genetic algorithms to search through possible quantum circuit configurations. QAS evaluates different architectures by simulating training processes and incorporates noise modeling to predict performance under various conditions.

According to the company's testing, QAS improved training speed by more than 40% compared to traditional methods and enhanced robustness in noisy environments by 30%. The system aims to avoid "barren plateau" regions that can trap optimization processes in local optima.

The technology searches through circuit architecture spaces that include quantum gate types, qubit connectivity patterns, and interaction designs. QAS combines this search capability with classical optimization algorithms like gradient descent to ensure selected architectures can be efficiently trained.

MicroAlgo stated the QAS system can adapt to different quantum computing applications including machine learning, optimization problems, and quantum simulation tasks. The company indicated the technology is designed to operate on current quantum devices while maintaining scalability for future hardware.

The Shenzhen-based company develops central processing algorithms and provides software and hardware optimization solutions. Information is based on a company press release.

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