New Milestone in Quantum Research
Google Quantum AI and quantum physicists at Freie Universität Berlin publish groundbreaking results on Hamiltonian operators
A research team including researchers at Freie Universität Berlin and Google Quantum AI has developed an innovative new method for estimating the parameters of Hamiltonian operators. The scientists have proposed a new technique that could be scalable and applicable to large quantum processors. As such, this method could enable quantum simulations to be carried out in a more precise manner in the future. The results of the study were recently published in the journal Nature Communications at: https://www.nature.com/articles/s41467-024-52629-3.
Named after William Rowan Hamilton, the Hamiltonian of a system represents its total energy. The term is used in both quantum mechanics and classical mechanics. In quantum mechanics, the Hamiltonian is typically assumed to be given, but in fact often remains unknown in its specifics. However, it is an important factor in precisely predicting the time evolution of quantum systems. Subsequently, understanding how data can be used to discern the Hamiltonian of a system (“Hamiltonian learning”) could play a decisive role in the development of quantum technology in the future.
Quantum technology, and quantum computing in particular, is considered a key technology of the future. Unlike conventional computers, quantum computers are not subject to the classical laws of physics. They are based on the principles of quantum mechanics, which means that the computing unit makes use of individual atoms or ions as their individual processing units. Tech companies and entire states, including Germany, are currently investing heavily in researching and developing these new technologies.
On the Case: Quantum Research in Action
The collaboration between Google Quantum AI, led by Pedram Roushan, and Jens Eisert’s research group at Freie Universität began with a call to Eisert from a colleague at Google. The colleague was struggling to calibrate Google’s Sycamore superconducting quantum processor with methods of Hamiltonian learning. The researchers’ initial attempts were insufficient, and it became clear that only a technique that made use of super-resolution could provide them with the results they needed. “Under certain conditions, super-resolution methods allow us to go beyond the fundamental limits that resolution can impose,” explains Eisert.
Solving the problem was no easy task, but Eisert and his team, including doctoral researchers Dominik Hangleiter and Ingo Roth, were determined to try. “While the basic principle was clear pretty quickly, it took three years of intensive research before we understood how to make Hamiltonian learning robust enough to apply it to large-scale experiments,” says Eisert. During this time, the team underwent several changes: Hangleiter moved to the University of Maryland, Roth moved to Abu Dhabi, and Jonáš Fuksa joined Eisert’s research group at Freie Universität.
Breakthrough Unlocks New Potential
The result was nothing short of a breakthrough. The researchers were able to calibrate the Sycamore processor – one of the most advanced quantum computers in the world – more precisely than ever before. “This new method greatly increases the predictability and precision of quantum technologies and creates new possibilities for condensed-matter simulations,” says Eisert.
Collaboration Is Key
This project highlights the essential role played by universities and tech companies in advancing the field of quantum technology. It was only by combining the expertise from both areas that the team was able to reach such groundbreaking results.
Wissenschaftlicher Ansprechpartner:
Prof. Dr. Jens Eisert, Physics Department, Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Email: jense@zedat.fu-berlin.de
Originalpublikation:
Hangleiter, Dominik, Ingo Roth, Jonáš Fuksa, et al. “Robustly Learning the Hamiltonian Dynamics of a Superconducting Quantum Processor.” Nature Communications 15 (2024): 9595. doi: https://doi.org/10.1038/s41467-024-52629-3.
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