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Quantum Simulations

QS — Leveraging Quantum Computers and Algorithms for Materials Discovery. This Quantum Simulations project is driven by an interdisciplinary team that aims to develop and deploy advanced quantum algorithms for electronic structure simulations and material science.


The development of unprecedented capabilities in quantum computing hardware has marked the beginning of a potentially revolutionary new era for scientific discovery of quantum phenomena. The technology is perceived as having remarkable potential for scientific discovery, suggesting access to entirely unexplored computational realms that were unattainable in the classical setting.


While quantum hardware development is growing at an unprecedented pace, successful near- and mid-term applications of quantum computing to key scientific and technological discoveries will undoubtedly happen within the context of hybrid classical-quantum computing approaches. This project allows us to establish new key synergies between the classical and the quantum world, aiming at closing the gap with more traditional approaches and set the path towards unexplored electronic structure calculations beyond the accuracy currently attainable with classical computing only. 


We are focusing on two main aspects. On one hand, we are pursuing the development of core quantum algorithms for the simulation of electronic problems, leveraging the most advanced hybrid classical-quantum algorithms based on variational methods. On the other hand, we are tackling the important task of interfacing well established classical software for electronic structure calculations with quantum computing accelerators through IBM's Qiskit platform. These approaches are of strategic scientific interest for Switzerland and instrumental in devising the next generation of applied quantum computing software and complementing the strong development of experimental platforms.


The project is led by Giuseppe Carleo.

Group Leaders

Giuseppe Carleo
Project leader
EPFL, Lausanne
Jürg Hutter
Group leader
UZH, Zürich
Ivano Tavernelli
Group leader
IBM, Rüschlikon
Zoë Holmes
Group Leader
EPFL, Lausanne
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Example of a quantum machine-learning algorithm for the classification of chemical structures. After extractions of chemical features from a database (e.g., MARVEL database on 2D materials) a distance kernel is trained in a quantum computer. In a second step, the same kernel is used to classify different materials using for instance a quantum support vector machine based on the same quantum kernel.

Related publications (until January 2025)