Insider letter
- MagiQware has raised €575,000 in pre-seed funding to develop AI-driven software for fault-tolerant quantum computing.
- The company uses reinforcement learning to optimize magic-state factories, a key component in reducing quantum error correction effort.
- Initial results cited in the LinkedIn post show a reduction in circuit length of up to 40% for targeted magic state factories.
According to a LinkedIn post announcing the investment, MagiQware has raised €575,000 in a pre-seed funding round to further develop artificial intelligence software designed to reduce the cost of fault-tolerant quantum computing.
Graduate Ventures, Delft Enterprises BV and LUMO Labs participated in the round. The funding will support the company’s efforts to develop software that improves one of the most resource-intensive aspects of large-scale quantum computing: quantum error correction.
Fault-tolerant quantum computing is widely seen as a prerequisite for running large, practical quantum applications. Because today’s quantum bits, or qubits, are prone to errors caused by noise and environmental interference, researchers expect future systems to rely on sophisticated error correction methods that use many physical qubits to produce a smaller number of reliable logical qubits. However, these techniques significantly increase the hardware and computational resources required to perform useful calculations.
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Target an important bottleneck
According to the LinkedIn post, Netherlands-based MagiQware focuses on optimizing so-called magic state factories, which produce specialized quantum resources needed to perform many operations in fault-tolerant quantum computers.
Investors said reducing the overhead associated with these factories could make commercially useful quantum applications more practical.
“Fault-tolerant quantum computing holds enormous commercial promise, but resource costs remain a major barrier to real-world impact,” the LinkedIn post said. “MagiQware is tackling this problem head-on by optimizing magic-state factories, a critical building block for scalable quantum systems, using advanced AI techniques. Their work brings commercially useful quantum applications significantly closer to reality.”
The company develops software that runs within the quantum compiler and software stack rather than in the underlying hardware. Compilers translate quantum algorithms into instructions that quantum processors can execute, making them an important layer for improving overall system efficiency.
AI-driven optimization
According to the LinkedIn post, MagiQware uses reinforcement learning, a branch of artificial intelligence in which algorithms learn through repeated trial and error and feedback, to dynamically optimize the operations of magic-state factories.
The company said its approach reduces the resources required for quantum error correction and allows quantum computers to run useful algorithms more efficiently.
Initial results cited in the post indicate that the software has shown a reduction in circuit length of up to 40% for targeted magic-state factories. Shorter quantum circuits generally require fewer operations, which can improve performance by reducing the chance of errors accumulating during computation.
Investors said this type of optimization is expected to become increasingly important as quantum hardware advances toward commercial use.
Leadership team
MagiQware is led by CEO Arash Ahmadi and CTO Shakeeb Majid. The management team also includes equipment manager Sahar Hejazi and theory manager Ali Moghaddam.
According to the LinkedIn post, the team brings together expertise in quantum computing, artificial intelligence, software engineering and physics to address the technical challenges associated with building fault-tolerant quantum computers.
https://thequantuminsider.com/2026/07/06/magiqware-raises-e575k-pre-seed-round-to-advance-ai-software-for-quantum-computing/
