Quantinuum Says it cuts quantum errors by 10x, a new world record

Quantinuum reports achieving 99.9995% logical fidelity on its Helios computer, a new world record representing just five errors in a million quantum operations. This result surpasses the company’s own record from September 2026 and is 100 times lower than the best published result from Google Quantum AI using a superconducting system, Quantinuum says.

Dr. Rajeeb Hazra, CEO of Quantinuum, said that this result demonstrates the company’s error-correction architecture and trapped-ion technology are capable of achieving universal fault-tolerance. These successive breakthroughs demonstrate rapid gains in accuracy with Quantinuum’s trapped-ion technology, placing the company on track for its 2029 goal of a universal fault-tolerant quantum computer.

Helios Achieves 99.9995% Logical Fidelity with Error Correction

Achieving 99.9995% logical fidelity, Quantinuum’s Helios recently demonstrated high-performance quantum computation. The achievement relies on a ‘color code’ implemented within Quantinuum’s end-to-end framework, which integrates tailored AI decoders, all-to-all qubit connectivity and hardware-level error correction, according to the company. This integrated approach allows for the testing and refinement of different error-correction codes, optimizing for both accuracy and efficiency. The significance of this advance extends beyond a simple numerical improvement. The company reports an exponential fall in logical errors as the ‘code distance’ increases.

Each tested increase in code distance reduced remaining logical errors by a factor of 12, indicating increasingly effective error suppression as the code scales, a critical capability for building practical, fault-tolerant quantum computers. Quantinuum believes this scaling behavior suggests that fault-tolerance can be achieved with fewer qubits than currently projected for other quantum computing modalities, which is particularly important as qubit count remains a major obstacle to widespread quantum computation.

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The speed of these advancements is highlighted by the fact that this latest record follows a similar breakthrough just one month prior, using a different error-correction code, Helix, on the same Helios hardware. Dr. Rajeeb Hazra, CEO of Quantinuum, said, “We have reduced error rates tenfold in rapid succession using a different code on the same commercial hardware, demonstrating the flexibility of our error-correction architecture.” The ability to rapidly prototype and deploy different codes within a single architecture provides a significant advantage, allowing Quantinuum to tailor its approach to specific applications and optimize performance.

Helix employed 10 physical qubits per logical qubit, while the color code used up to 37, highlighting the trade-offs between resource use and error rates. Quantinuum’s architecture incorporates adaptive syndrome extraction, which dynamically adjusts error-checking measurements during computation, and chain-mapping, which connects codes suited to different computational tasks.

These techniques, combined with efficient code structures and real-time decoding, provide multiple avenues for reducing errors and minimizing resource requirements. The company is now focused on integrating its hardware, error-correction architecture and full-stack software platform into a cohesive system for fault-tolerant quantum computing, aiming to provide the tools necessary for developing and scaling quantum applications across diverse sectors, the company says. These findings go a long way toward resolving one of the field’s central technical risks.

With this result, we’ve proven that we have the error-correction architecture and trapped-ion technology required to achieve universal fault-tolerance.

Dr. Rajeeb Hazra, CEO of Quantinuum
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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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