The famous yet unsolved, Fermi-Hubbard model for strongly-correlated electronic systems is a prominent target for quantum computers. However, accurately representing the Fermi-Hubbard ground state for large instances may be beyond the reach of near-term quantum hardware.
Only modest, drastically simplified Fermi-Hubbard examples have previously been solved on a quantum computer. Scientists from the University of Bristol, quantum start-up, Phasecraft, and Google Quantum AI show that much more ambitious results are possible. They used a new, highly efficient algorithm and better error-mitigation techniques to run an experiment that is four times larger – and consists of 10 times more quantum gates – than anything previously recorded.
The algorithm is the first genuinely scalable algorithm that can be used to observe ground-state properties of the Fermi-Hubbard model on a quantum computer.
Professor of Quantum Computation at the University of Bristol, Ashley Montanaro, and Phasecraft co-founder said, “The Fermi-Hubbard instance in this experiment represents a crucial step towards solving natural materials systems using a quantum computer. We succeeded by developing the first truly scalable algorithm that anyone has managed to implement for the Fermi-Hubbard model. That’s particularly exciting because it suggests that we will be able to scale our methods to leverage more powerful quantum computers as the hardware improves.”
Ryan Babbush, Head of Quantum Algorithms at Google AI, said, “We are delighted to see this experiment designed and executed by Phasecraft, representing one of the largest digital fermionic simulations to date, and also one of the largest variational algorithms to date, performed on Google’s quantum computing hardware. The scalability of their approach derives from being state-of-the-art in terms of error mitigation and algorithm compilation for near-term quantum hardware.”
Stasia Stanisic, Senior Quantum Engineer at Phasecraft, the paper’s lead author, said, “This experiment represents a new milestone. It tells us what today’s quantum computers are capable of when we apply the best algorithmic technology available. We can build on this work to develop better algorithms and encodings of realistic problems for today’s devices.”