Berlin, January 26, 2026 -- Freie Universität Berlin, Technische Universität Berlin, ParityQC and NXP Semiconductors Germany GmbH kicked off the new collaborative project PraktiQOM (“Practical Quantum Advantages for Optimization Problems with Cryptographic Methods”). The goal of the project is to substantially advance the performance of quantum algorithms to solve real-world optimization problems, focusing specifically on the field of semiconductor chip manufacturing – one of the most critical and complex domains in the IT industry. The project was launched as part of Germany’s “Application-Oriented Quantum Computing” initiative and is funded by the Federal Ministry of Research, Technology and Space. It will run until October 2028.

A new strategy for powerful quantum optimization algorithms

In quantum computing, it has already been shown that certain algorithms can fundamentally outperform classical approaches. Well-known examples include Shor’s algorithm for factorization and Grover’s algorithm for unstructured search, both of which offer proven computational advantages. PraktiQOM builds on this foundation by developing novel methods that push these advantages even further.

The PraktiQOM project partners take a strategic and multifaceted approach to the development of quantum algorithms, with the goal of making them more efficient, easy to scale and ready to use. This approach includes a novel strategy for transferring the exponential advantages known from quantum cryptography to optimization problems, as well as further enhancing the advantage of Grover’s algorithm over classical methods. In addition, by leveraging ParityQC’s Parity Twine technology and the Parity Code for error correction, the goal is to achieve improvements of at least one order of magnitude in both runtime and the number of required qubits when executing the quantum algorithms.

Direct application to industry: semiconductor chip design

These new methods will be developed for specific, highly relevant problems in the semiconductor chip manufacturing industry, with NXP Semiconductors bringing extensive industrial expertise and real-world design challenges. A particularly complex optimization problem in this domain is floor planning – an early-stage physical design step that that involves arranging components on a chip to optimize area, connectivity, and performance. The problem poses significant challenges for classical algorithms, and it will therefore serve as a test case within PraktiQOM for developing powerful and efficient quantum optimization algorithms.

The final output of the project will be a series of quantum solvers: quantum algorithms tailored to specific classes of real-world problems and designed with future commercialization in mind.

Expertise from academia and industry

To advance this ambitious goal, PraktiQOM brings together leading expertise from academia and industry:

• Freie Universität Berlin (Project Coordinator), led by Prof. Dr. Jens Eisert
• Technische Universität Berlin, contributing theoretical algorithm development
• NXP Semiconductors, providing industrial use cases and semiconductor design expertise
• ParityQC, delivering the ParityQC Architecture, error correction strategies and record-efficient implementation solutions through Parity Twine.

This collaboration ensures that theoretical advances are directly connected both to practical industrial needs and actual quantum hardware capabilities.

ParityQC’s role: from algorithms to hardware-ready solutions

Within PraktiQOM, ParityQC is contributing its expertise in quantum architecture and algorithms. Our specific contributions include:

• Optimal algorithm implementation: We will implement newly developed quantum algorithms using the Parity Flow methodology. This approach ensures that algorithms are optimized specifically for practical quantum hardware constraints.
• Resource assessment with error correction: Using the Parity Code’s natural error correction capabilities, we will conduct precise assessments of the computational resources needed. We aim to achieve at least a ten-fold improvement in both runtime and required qubits compared to conventional quantum error correction approaches.
• Realistic performance evaluation: We will establish clear relationships between solution quality, computational resources, and actual runtime on quantum hardware. This comprehensive approach enables reliable statements about when quantum methods can genuinely outperform classical approaches.