Barbara Terhal’s quantum return to Amsterdam as QuSoft’s 2026 Turing Chair

Barbara Terhal returns to Amsterdam as QuSoft’s 2026 Turing Chair, a position that marks a significant evolution for both her career and the field of quantum computing. In 1995, Terhal attended a formative quantum computing workshop in Turin, Italy, alongside a small mix of physicists, mathematicians and computer scientists, reflecting the field’s nascent state. Now a leading researcher in quantum error correction, the vital technique needed for functional quantum computers, Terhal’s month in Amsterdam culminates with the Workshop on Quantum Error Correction at Amsterdam Science Park (October 28-30), part of CWI’s research semester program.

Quantum Error Correction: Bridging Fragile Information and Physical Qubits

Quantum codes indirectly detect errors without reading the information itself, a necessity given the fragility of quantum information compared to its classical counterpart. Barbara Terhal emphasizes this fundamental need, stating, “Errors and faults occur in most processes, whether it is a CD getting scratched or DNA being altered while it gets copied.

To preserve information, mechanisms for error correction thus need to be in place. For quantum information, due to its fragility as compared to classical information, quantum error correction is a necessity at the most basic physical level.” This principle guides current research aimed at translating theoretical codes into functional hardware.

The intersection of quantum software and hardware is a key focus for Terhal in her role as QuSoft’s 2026 Turing Chair. She describes quantum error correction as sitting right in between quantum software and quantum hardware in the quantum computer stack. The last, say, 5 years have seen a lot of progress in experimental quantum error correction, with much more progress to come. These codes offer the possibility of protecting quantum information using fewer physical qubits than current methods, though computing on information stored with them remains an active area of investigation.

Despite the growing popularity of the field, Terhal offers candid advice to students, acknowledging that “Quantum error correction has become a very popular field of research, at times it feels a bit overcrowded.” She highlights the diverse nature of the work, noting, “However, it is fun to connect with quantum hardware, the research can be quite hands-on/numerical, but there are also beautiful & fascinating mathematical aspects of quantum error correction research.

So there is a bit of everything.” This blend of practical and theoretical work extends to the design of logical processing units, memory and storage, all reliant on quantum error correction, and how these elements optimize algorithm performance. Terhal believes maintaining connections between the practical and the mathematical is important as the field expands, and she stresses the importance of collaborative efforts, adding, “It can be challenging to bring people together from different communities, which is why it is important to do.

The last, say, 5 years have seen a lot of progress in experimental quantum error correction, with much more progress to come.

Barbara Terhal

Terhal’s 2026 Turing Chair Connects QuSoft with LDPC Code Advancement

Returning to Amsterdam after thirty years since starting her PhD, Terhal’s focus now centers on bridging the gap between theoretical error correction and practical implementation, a connection she believes is important for realizing functional quantum computers. She explains that every quantum algorithm requires implementation within a code architecture tailored to specific hardware capabilities, emphasizing, “Every algorithm has to be translated to its implementation in a code architecture whose design uses hardware specifications and capabilities.

Therefore I believe there is a lot of important work to do at the boundary between quantum algorithms and quantum error correction.” This interplay extends to software development, as the measurements used to detect errors rely heavily on robust software processing. Terhal specifically hopes to learn more about “logical gate constructions for LDPC codes,” a family of codes potentially requiring fewer physical qubits than current methods.

Since 2025 Terhal has combined her professorship and QuTech with a research fellowship at Riverlane, a company that builds error-correction software. Terhal believes companies can accelerate progress through focused team efforts, and that the field benefits from a blend of academic research and industry application.

Errors and faults occur in most processes, whether it is a CD getting scratched or DNA being altered while it gets copied. To preserve information, mechanisms for error correction thus need to be in place. For quantum information, due to its fragility as compared to classical information, quantum error correction is a necessity at the most basic physical level.

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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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