Robustly learning the Hamiltonian dynamics of a superconducting quantum processor

Dominik Hangleiter (), Ingo Roth (), Jonáš Fuksa, Jens Eisert and Pedram Roushan
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Dominik Hangleiter: University of Maryland and NIST
Ingo Roth: Freie Universität Berlin
Jonáš Fuksa: Freie Universität Berlin
Jens Eisert: Freie Universität Berlin
Pedram Roushan: Google Quantum AI

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Precise means of characterizing analog quantum simulators are key to developing quantum simulators capable of beyond-classical computations. Here, we precisely estimate the free Hamiltonian parameters of a superconducting-qubit analog quantum simulator from measured time-series data on up to 14 qubits. To achieve this, we develop a scalable Hamiltonian learning algorithm that is robust against state-preparation and measurement (SPAM) errors and yields tomographic information about those SPAM errors. The key subroutines are a novel super-resolution technique for frequency extraction from matrix time-series, tensorESPRIT, and constrained manifold optimization. Our learning results verify the Hamiltonian dynamics on a Sycamore processor up to sub-MHz accuracy, and allow us to construct a spatial implementation error map for a grid of 27 qubits. Our results constitute an accurate implementation of a dynamical quantum simulation that is precisely characterized using a new diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.

Date: 2024
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DOI: 10.1038/s41467-024-52629-3

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