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QCentroid shows defence groups how to find quantum’s value

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October 17, 2026 by Ivy Delaney

On September 29, 2026, QCentroid co-founders Carlos Kuchkovsky and Alberto Calvo presented a method for evaluating quantum computing’s value to the Quantum Security Defence (QSECDEF) community, addressing a critical gap in how defence organizations approach the technology. While much focus has been placed on preparing for quantum threats, QCentroid argues for a parallel effort to harness quantum computing for operational advantage.

The company frames the path to quantum advantage as being proven “one problem at a time, against the best classical method, on cost, quality, speed and energy.” Anna Beata Kalisz Hedegaard, Co-Founder and CEO of QSECDEF, opened the session and introduced the speakers, signaling a growing interest in quantifying quantum’s potential.

QuantumOps Method for Continuous Problem Evaluation

The method moves beyond threat mitigation to identify problems where quantum systems can deliver a demonstrable advantage, a shift in focus the company argues is now essential. QuantumOps aims to establish a repeatable process for determining when to integrate, hold, or wait on quantum solutions, factoring in hardware advancements and evolving needs. The session placed defence and dual-use problems in three families, each at a different level of maturity.

In every family, quantum computing arrives inside a hybrid stack. Quantum processors run next to HPC, GPUs and AI and near-term claims of advantage depend on that combination.

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Defence and Dual-Use Problem Maturity in 2026

The company’s analysis reveals that optimisation problems, such as mission planning and logistics, currently offer the most immediate value, using quantum-inspired and hybrid solvers benchmarked against established classical methods. Simulation, encompassing areas like energetic materials and battery electrolytes, holds the strongest theoretical promise but requires the development of fault-tolerant hardware to realise substantial impact. While significant investment has focused on mitigating quantum threats, QCentroid asserts a parallel need for rigorous evaluation of potential benefits, aligning with demands from Allied programmes like NATO, DIANA and the EU for independent benchmarking and sovereign deployment options.

The QCentroid platform shows a process for establishing a single, governed definition for each use case, facilitating side-by-side comparisons of classical and quantum solvers. QCentroid CEO Carlos Kuchkovsky and CTO Alberto Calvo emphasized that proving quantum advantage requires demonstrating value, a methodology embedded within their QuantumOps platform.

The company’s approach differs from broader claims of universal quantum superiority, instead focusing on a data-driven assessment of specific applications. The platform also offers an on-premise deployment option, allowing customers to maintain control over classified data within their own data centres, addressing a critical security requirement for defence organisations. “Where classical methods win, the QCentroid platform records that and schedules a re-test when new hardware arrives,” said Kuchkovsky, highlighting the platform’s commitment to objective evaluation.

Allied Programme Requirements for Quantum Investment

These programmes consistently require independent benchmarking, validation with end users, data sovereignty and a phased approach to hardware investment, demanding more than theoretical potential. These allied programmes share a common need for evidence before committing to large-scale quantum deployments. NATO’s Quantum Strategy, the DIANA accelerator, DARPA’s Quantum Benchmarking Initiative and the EU’s Quantum Europe strategy all prioritize verification on an organisation’s own problems, tested in real-world conditions.

According to Carlos Kuchkovsky and Alberto Calvo, many organisations have successfully built defensive strategies against quantum attacks, but lack a consistent process for proving where quantum computing delivers a tangible benefit. “Each one starts by demanding evidence,” the company states, highlighting the shift towards demonstrable results over speculative promises. This emphasis on operational requirements aims to bridge the gap between theoretical quantum advantage and practical, deployable solutions for allied defence programmes.

QCentroid Platform Deployment Options for Sovereign Data

QCentroid provides three distinct deployment options tailored to varying levels of data sensitivity and programme maturity, ranging from a managed SaaS offering to a fully private, on-premise installation. This tiered approach addresses a critical need within defence organisations for granular control over sensitive data, allowing them to participate in quantum computing pilots without compromising security protocols. The on-premise option, in particular, ensures data remains within the customer’s perimeter by installing the QCentroid platform directly in their data centre.

This gap is filled by the QuantumOps method, which connects use cases, data, algorithms, and hardware, and benchmarks each option against the best classical baseline. The company’s platform is currently available for discovery pilots through its managed SaaS offering, with enterprise programmes requiring configuration or integration to use the private or sovereign cloud options, QCentroid says. QCentroid’s emphasis on demonstrable results reflects a broader shift within the field, as organisations increasingly demand evidence before committing to large-scale quantum investment.

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Avatar of Ivy Delaney

Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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