Quantum Error Correction, Quantum Computing News

Can Riverlane’s Deltaflow unlock useful quantum computing?

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October 9, 2026 by Ivy Delaney

By 2026, Riverlane aims to achieve a critical milestone for practical quantum computing: a million real-time quantum operations, known as MegaQuOps, the company says. This threshold, the company reports, is where quantum computers can begin running their first error-corrected applications. Deltaflow 2, Riverlane’s quantum error correction stack, will perform real-time error correction on up to 250 physical qubits using its proprietary Local Clustering Decoder. The system processes petabytes of data through readout processing, a powerful decoder, and an orchestration layer to transform unstable qubits into reliable logical qubits.

Deltaflow Enables MegaQuOp Computing by 2026

This decoder, broken into smaller sections, processes data in parallel to maintain speed while tackling the escalating number of qubit errors inherent in quantum systems. Deltaflow’s orchestration layer coordinates this complex activity in real time, integrating readout processing from any quantum computer control system with the powerful decoder.

The company states that crossing into the MegaQuOp regime presents a central moment in quantum computing, where the power of quantum computers goes beyond the reach of any classical supercomputer. The development of Deltaflow is not occurring in isolation; Riverlane actively partners with leading quantum computer manufacturers to tailor the error correction stack to specific hardware.

This collaborative process begins with a deep understanding of each partner’s qubits, hardware and error characteristics, followed by the design of optimal error correction codes and decoders. Riverlane partner to test and implement QEC solutions offline in software, and then test and implement real-time QEC solutions in hardware, with support from a contract with the UK’s National Quantum Computing Centre.

Riverlane also collaborates with Oak Ridge National Laboratory to improve the integration of High Performance Computing (HPC) with quantum systems, further demonstrating a commitment to improving the integration of these technologies, according to the company. The company’s approach centers on building a comprehensive quantum error correction stack, capable of handling the immense data processing demands of scaling quantum computers to useful levels of performance.

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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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