en.Wedoany.com Reported - Beijing Liangkun Technology Co., Ltd. recently completed hundreds of millions of yuan in angel and angel+ funding rounds. This round was led by InnoAngel Fund, with participation from institutions including Guoqi Investment, Beigong Investment, BV Baidu Ventures, and Tsinghua SEM Alumni Fund. The funds will be used for core technology research and development, product platform construction, expansion of industrial scenarios, and introduction of high-level talent.
Liangkun Technology's financing is primarily directed towards building a research infrastructure that integrates "quantum computing, artificial intelligence, and high-performance computing." The company adopts a Quantum × AI × HPC technical roadmap, planning to construct a new-generation scientific research platform for fields such as materials, chemistry, biomedicine, and advanced manufacturing. This platform aims to enhance the efficiency of modeling, solving, validating, and engineering transformation for complex scientific problems. Unlike general AI application companies, Liangkun Technology addresses the long-standing "precision, efficiency, and cost" constraints in scientific research scenarios: material discovery requires screening candidate structures in vast chemical spaces; drug development involves handling molecular conformations, reaction pathways, and multi-parameter optimization; and advanced manufacturing necessitates simulation, prediction, and process optimization under complex conditions. Relying solely on traditional computing or a single AI model makes it difficult to simultaneously meet the requirements of high-precision simulation, rapid iteration, and industrial-grade usability. Quantum computing can offer new solution approaches for specific complex problems, AI can enhance the efficiency of modeling, prediction, and search, while high-performance computing provides stable large-scale task scheduling and engineering operation capabilities. By combining these three capabilities, the research platform has the potential to transform processes that previously relied on long-term experimental trial and error into a closed loop of "computational prediction, experimental validation, and model iteration."
Founded in January 2026, the company is an early-stage but high-financing AI for Science enterprise. The founding team possesses interdisciplinary backgrounds in quantum computing, artificial intelligence, high-performance computing, and materials chemistry. LightSource Capital served as the exclusive financial advisor.
In the context of the information industry, this financing essentially represents the extension of intelligent data processing from general enterprise software to underlying scientific research platforms. Over the past few years, AI has been first implemented in scenarios like office work, customer service, marketing, and code generation. However, scientific research and industrial R&D scenarios demand higher data quality, model credibility, computational accuracy, and engineering validation, leading to slower commercialization progress. Liangkun Technology's chosen direction is closer to "scientific computing infrastructure": connecting quantum algorithms, machine learning models, and high-performance computing resources on one end, and the real R&D problems of companies in materials, chemistry, biomedicine, and manufacturing on the other. If the platform can develop stable capabilities in candidate material screening, molecular simulation, reaction pathway prediction, experimental parameter optimization, and process window searching, it could help enterprises reduce trial-and-error costs, shorten R&D cycles, and enhance the reusability of complex R&D tasks. For industrial enterprises, the value of such a platform lies not just in generating a computational result, but in reorganizing knowledge scattered across laboratories, databases, simulation software, and engineering experience into a computable, traceable, and verifiable R&D process. As competition intensifies in advanced manufacturing, pharmaceutical R&D, and new materials industries, the role of intelligent data processing will shift from "auxiliary analysis" to "R&D decision-making infrastructure." This is a key context for why Liangkun Technology has secured participation from multiple industrial capital and technology funds.
Subsequent variables will focus on the speed of platform productization, validation in real industrial scenarios, the synergistic effect between quantum computing capabilities and AI models, as well as mechanisms for research data security and intellectual property protection. AI for Science is still in a critical phase of transitioning from frontier research to engineering applications. Whether Liangkun Technology can convert its financing into deliverable products, industry case studies, and long-term customers will determine its actual position in the new generation of scientific computing platforms.
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