Riverlane’s Terhal to Guide Quantum Error Correction Research
Riverlane has appointed Professor Barbara Terhal as a Riverlane Fellow and established a new quantum error correction (QEC) research and development hub in Delft, the Netherlands. Located at the House of Quantum, this expansion signifies Riverlane’s commitment to Europe’s quantum ecosystem and will focus on applying artificial intelligence (AI) and machine learning to advance QEC. Professor Terhal joins Professors Earl Campbell and Dan Browne in guiding Riverlane’s roadmap, leveraging her expertise in quantum codes and fault-tolerant architectures to strengthen technologies like Deltaflow and the Deltakit platform, ultimately driving progress toward utility-scale quantum computing.
Riverlane’s European Expansion with Delft Hub
Riverlane is expanding its presence in Europe with a new hub in Delft, the Netherlands, led by Riverlane Fellow Professor Barbara Terhal. This move signifies the company’s deepening commitment to Europe’s quantum ecosystem and builds upon existing partnerships with Dutch companies like Qblox and Quantware. Located at the House of Quantum, the hub will concentrate on applying artificial intelligence (AI) and machine learning to advance quantum error correction (QEC), a critical step toward utility-scale quantum computing.
The Delft hub will focus on strengthening Riverlane’s real-time QEC technology, Deltaflow, and its open-source software platform, Deltakit. Research will center on developing AI-based decoders and tools to improve QEC performance. Professor Terhal’s expertise in foundational QEC theory will be instrumental in guiding this work and ensuring it’s grounded in world-class science. This expansion aims to position Europe at the forefront of the global quantum computing industry.
Riverlane’s commitment to mastering QEC is supported by over $120 million in private funding, including an $85 million Series C round in 2024. The company partners with over 60% of the world’s quantum computer companies and HPC centers to address the error problem hindering the development of utility-scale systems. Deltaflow is designed to work with all major qubit types and includes proprietary chips, decoders, and a compiler.
Advancing Quantum Error Correction with AI Tools
Riverlane is expanding its quantum error correction (QEC) research and development with a new hub in Delft, Netherlands, led by Professor Barbara Terhal. This move signifies a deepening commitment to the European quantum ecosystem and will focus on applying artificial intelligence (AI) and machine learning to advance QEC. The Delft hub, located at the House of Quantum, will specifically develop AI-based decoders and tools to enhance Riverlane’s real-time QEC technology, Deltaflow, and its open-source software platform, Deltakit.
The application of AI is central to Riverlane’s efforts to make quantum error correction faster, smarter, and more efficient. By leveraging AI across its QEC technologies, the company aims to push performance limits within Deltaflow and expand the capabilities of Deltakit. Professor Terhal’s expertise in foundational QEC theory will ensure this work is grounded in strong scientific principles, helping Riverlane move QEC from theory into practical technology.
Riverlane’s Deltaflow works with all major qubit types and includes proprietary QEC chips, decoders, and a compiler. The company partners with over 60% of the world’s quantum computer companies and leading high-performance computing centres, aiming to solve the error problem preventing the development of ‘utility-scale’ quantum systems. Established in 2016, Riverlane has raised over $120 million in funding, including $85 million in a Series C round in 2024.
Key Technologies: Deltaflow and Deltakit
Riverlane is advancing quantum error correction (QEC) through technologies like Deltaflow and Deltakit. Deltaflow is a real-time QEC stack designed to work with all major qubit types, incorporating proprietary QEC chips, decoders, and a compiler. Deltakit functions as an open-source software platform, intended to facilitate learning, development, and adoption of QEC for quantum developers. These technologies are central to Riverlane’s mission of mastering QEC and unlocking utility-scale quantum computing.
The establishment of a new hub in Delft, Netherlands, will focus on applying artificial intelligence (AI) and machine learning to strengthen Riverlane’s QEC technologies. Specifically, the team will develop AI-based decoders and tools to improve Deltaflow and expand the capabilities of Deltakit. This work aims to push the limits of performance in Deltaflow, ultimately driving progress toward building practical, utility-scale quantum computers.
Riverlane views Deltakit as key to broader industry adoption of QEC. The software platform is designed to help developers learn and implement QEC techniques. Combined with Deltaflow’s real-time capabilities, Riverlane intends to position itself as a leader in solving the error problem that currently blocks the path to useful quantum systems, partnering with over 60% of quantum computer companies worldwide.
Stong move bringing Terhal into the fold. The AI-powered decoder angle is probaly the most underrated part here, since classical decoding bottlenecks have been quietly limiting QEC scalability way more than most ppl realize. Curious if Deltakit's open-source approach actually accelerates industry adoption or just fragments implementation standards across diferent qubit platforms.