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Simulating material pairings could unlock topological quantum computers

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September 15, 2026 by Ivy Delaney

Researchers at SISSA have developed a new simulation method to model how one material can become superconducting when placed near another. This computational approach addresses a key challenge in materials science; the effect can extend over hundreds of nanometers, previously making accurate simulations computationally prohibitive. The team validated their method by comparing results for the NbSe₂ and CrBr₃ interface against existing experimental measurements, and the work was selected as an Editors’ Suggestion by Physical Review Research. This tool could accelerate the development of topological superconductors, materials considered essential for building topological quantum computers.

Proximity Effect Simulations Advance Topological Superconductor Search

Simulations now accurately model the proximity effect extending up to hundreds of nanometers, a distance previously prohibitive for conventional computational methods. The new technique concentrates computational power on critical regions and blends approaches of varying complexity, eliminating the need to model extensive structures directly. This advancement addresses a major obstacle in materials science, enabling predictive analysis of how materials interact at superconducting interfaces. The research team, collaborating with the University of Trieste and the California Institute of Technology, initially validated the method using simplified models before applying it to the NbSe₂ and CrBr₃ interface.

Results from these simulations directly align with existing experimental measurements, confirming the method’s reliability and predictive capability. The team states that their work provides a technique capable of describing and predicting the proximity effect more accurately, opening avenues for designing novel material combinations. These computers rely on qubits, and the ability to accurately simulate material pairings could unlock new ways to store and manipulate these fundamental units of information.

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Quantum Computing simulations Superconductors topological materials
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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