MagiQware has secured €575,000 in pre-seed funding led by Graduate Ventures with participation from Delft Enterprises BV to address the core challenges hindering progress in practical quantum computing. The startup focuses on optimizing key components of quantum error correction, and early results demonstrate that a novel combination of reinforcement learning and quantum compiler optimization can reduce circuit length by up to 40%. This improvement is an important step toward scalable and cost-effective quantum computing, as reducing resource overhead becomes increasingly important for commercial deployments. Grade Ventures said MagiQware’s potential lies in its ability to “remove fundamental bottlenecks in fault-tolerant quantum computing” by applying advanced AI techniques. The new funding will accelerate the development of MagiQware’s technology and foster collaboration within the growing quantum computing industry.
This initiative positions MagiQware as a specialist software provider that can deliver performance improvements to full-stack quantum computing companies without the need for extensive in-house development of these complex optimization tools. The founding team consists of Arash Ahmadi, Shakeeb Majid, Sahar Hejazi, and Ali Moghaddam, who bring an interdisciplinary skillset including quantum computing, artificial intelligence, software engineering, and physics. Beyond the pre-seed round, MagiQware recently won the Dutch High Tech SME Call and began a joint project with the University of Amsterdam to further develop AI-powered software for magic state factory optimization. The project aims to accelerate the commercialization of quantum computing and strengthen the Netherlands’ position in the field of emerging quantum technologies. This new funding will support ongoing technology development and expand collaboration within the rapidly evolving quantum computing industry.
These factories are responsible for generating the specialized quantum states needed to detect and correct errors, and MagiQware’s approach focuses on optimizing efficiency through artificial intelligence. The company’s core innovation is combining reinforcement learning and quantum compiler optimization to dynamically improve factory performance and significantly reduce the computational demands of fault-tolerant quantum computing. This reduction is particularly important as the field moves from theoretical research to practical application and resource overhead becomes a determining factor in commercial feasibility.
Initial results demonstrate circuit length reductions of up to 40%, representing a significant step toward scalable and cost-effective quantum computing.
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