October: quantum computing breakthrough | News and Features

Researchers from the University of Bristol, start-up Quantum, Phasecraft and Google Quantum AI have revealed properties of electronic systems that could be used for the development of more efficient batteries and solar cells.

The results, published in Nature Communication today describes how the team took an important first step toward using quantum computers to determine low-energy properties of strongly correlated electronic systems that cannot be solved by classical computers. To do this, they developed the first truly scalable algorithm to observe the ground state properties of the Fermi-Hubbard model on a quantum computer. The Fermi-Hubbard model is a way to discover crucial information about the electronic and magnetic properties of materials.

Modeling quantum systems of this form has important practical implications, including the design of new materials that could be used in the development of more efficient solar cells and batteries, and even high-temperature superconductors. However, this remains beyond the capability of the world’s most powerful supercomputers. The Fermi-Hubbard model is widely recognized as an excellent benchmark for near-term quantum computers because it is the simplest system of materials that includes non-trivial correlations beyond what is captured by classical methods. . The approximate production of the lowest energy (background) state of the Fermi-Hubbard model allows the user to calculate key physical properties of the model.

In the past, researchers have only succeeded in solving small, highly simplified Fermi-Hubbard instances on a quantum computer. This research shows that much more ambitious results are possible. Leveraging a new, highly efficient algorithm and better error-mitigation techniques, they successfully conducted an experiment that was four times larger – and made up of 10 times more quantum gates – than anything previously recorded.

“The Fermi-Hubbard instance in this experiment represents a crucial step towards solving realistic material systems using a quantum computer,” said University of Bristol quantum computing professor Ashley Montanaro. and co-founder of Phasecraft. “We succeeded by developing the first truly scalable algorithm that anyone has managed to implement for the Fermi-Hubbard model. This is particularly exciting because it suggests that we will be able to adapt our methods to take advantage of more powerful quantum computers as hardware improves.

Phasecraft brings together many of the world’s leading quantum scientists and engineers and partners with the world’s leading developers of quantum hardware. Their research has led to fundamental breakthroughs in quantum science and aims to dramatically reduce the time scale for quantum advantage in several critical areas. Beyond developing algorithms that will be able to scale to larger quantum computers, the Phasecraft team is also focused on continuing to incorporate practically relevant features into their models so that they more accurately represent systems. of the real world.

“We are thrilled to see this experiment designed and executed by Phasecraft, representing one of the largest numerical Fermionic simulations to date, and also one of the largest variational algorithms to date, performed on Google’s quantum computing hardware. “, says Ryan Babbush, Head of Quantum Algorithms at Google AI. “The scalability of their approach stems from being state-of-the-art in terms of error mitigation and algorithm compilation for near-term quantum hardware.”

“This experience represents a new step. This tells us what today’s quantum computers are capable of when we apply the best available algorithmic technology,” says Stasja Stanisic, senior quantum engineer at Phasecraft, lead author of the paper. “We can build on this work to develop better algorithms and encodings of realistic problems for today’s devices.”

The work was partly funded by ERC through Professor Ashley Montanaro’s Consolidator grant “Quantum Algorithms: from Foundations to Applications”, and partly by UKRI through the EPSRC Prosperity Partnership program, which enabled collaboration between partners.


“Observing Ground State Properties of the Fermi-Hubbard Model Using an Evolutionary Algorithm on a Quantum Computer” by Stasja Stanisic, Ashley Montanaro et al in Communication Nature.

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