In a new paper, researchers at J.P. Morgan, the U.S. Department of Energy’s (DOE) Argonne National Laboratory and Quantinuum have demonstrated clear evidence of a quantum algorithmic speedup for the quantum approximate optimization algorithm (QAOA). The algorithm has been studied extensively and has been implemented on many quantum computers, with financial modelling among the areas for application.
“This work is a significant step towards reaching quantum advantage, laying the foundation for future impact in production,” says Marco Pistoia, head of Global Technology Applied Research at J.P. Morgan, in a statement.
The team examined whether a quantum algorithm with low implementation costs could provide a quantum speedup over the best-known classical methods. To explore the quantum algorithm’s performance in an ideal noiseless setting, J.P. Morgan and Argonne jointly developed a simulator to evaluate the algorithm’s performance at scale.
To take the first step toward practical realization of the speedup in the algorithm, the researchers demonstrated a small-scale implementation on Quantinuum’s System Model H1 and H2 trapped-ion quantum computers. Using algorithm-specific error detection, the team reduced the impact of errors on algorithmic performance by up to 65%.
“Our long-standing partnership with JPMorgan Chase led to this meaningful and noteworthy three-way research experiment that also brought in Argonne National Lab. The results could not have been achieved without the unprecedented and world leading quality of our H-Series Quantum Computer, which provides a flexible device for executing error-correcting and error-detecting experiments on top of gate fidelities that are years ahead of other quantum computers,” said Ilyas Khan, founder and chief product officer of Quantinuum, in a statement.