Aqarios Quantum Technologies will actively harness quantum noise, typically viewed as a hindrance, as a resource for machine learning within the three-year IgnisQNC project. Funded by the European QuantERA programme, the initiative running from autumn 2026 to autumn 2029 focuses on developing AI methods for current, noisy quantum hardware.
The project, titled “Harnessing Quantum Uncertainty: Trustworthy Quantum Neural Computing for Quantum Utility,” also addresses the growing need for explainable AI models, particularly in light of the EU AI Act. Aqarios will test these methods on real quantum hardware via its Luna platform, aiming to deliver practical applications for industry.
Quantum Neural Computing Harnesses Uncertainty for Near-Term Hardware
Aqarios GmbH will benchmark quantum neural computing methods against classical approaches as part of the IgnisQNC project, a three-year initiative beginning in autumn 2026 and concluding in autumn 2029. The project, a collaboration between Lancaster University, LMU Munich, TU Bergakademie Freiberg, Universidad Pablo de Olavide and Aqarios, focuses on running data-driven learning tasks on existing quantum hardware rather than awaiting the development of fully error-corrected systems.
This strategy directly addresses the limitations of current quantum computers, which are inherently noisy and prone to errors. The core of the research lies in Quantum Neural Computing, or QNC, a method that intentionally uses quantum uncertainty as a computational resource. Unlike most quantum algorithms designed to minimize noise, QNC treats uncertainty as a positive attribute for probabilistic modelling within layered neural networks.
The total volume of funding for IgnisQNC is approximately EUR 1.8 million, provided through the European QuantERA programme with German partners funded by BMFTR and project management by DLR Projektträger. A key driver for the project’s emphasis on transparency is the evolving regulatory landscape surrounding artificial intelligence, specifically the EU AI Act. IgnisQNC will therefore develop explainability methods tailored for quantum models, enabling the auditing and understanding of their outputs.
This focus on “trust” is not merely an academic exercise; it is a direct response to the increasing need for accountable and interpretable AI systems. The consortium’s work extends beyond theoretical development, with Aqarios responsible for translating the QNC approach into practical applications and evaluating its performance on actual quantum hardware, the company says. The company, a 2021 spin-off of LMU Munich, is a leading provider of application-oriented quantum software, combining classical optimization with quantum computing. This focus on near-term utility distinguishes IgnisQNC from many quantum machine learning projects that target distant, fault-tolerant quantum computers.
Aqarios and Luna Platform Enable Real-World Quantum Benchmarking
Aqarios is directly evaluating new quantum machine learning methods on commercially available hardware, a step beyond simulations common in the field. The IgnisQNC project’s approach to benchmarking is particularly rigorous; every quantum method developed will be directly compared against established classical algorithms. This head-to-head assessment, conducted via Aqarios’ Luna platform, aims to definitively establish where quantum computing delivers a tangible advantage, according to the company.
According to Aqarios, this is the only reliable way to demonstrate the value of quantum computation beyond theoretical potential. Luna’s role extends beyond testing, providing industry partners with access to these methods without requiring them to develop in-house quantum expertise. Aqarios’ responsibilities within the consortium include running these new methods on quantum computers from multiple providers, ensuring results are not specific to a single hardware architecture.
The company will also integrate successful applications into the Luna product suite, expanding the platform’s capabilities and offering commercial users a broader range of quantum-enhanced tools.
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