Characterization and Verification of Trotterized Digital Quantum Simulation via Hamiltonian and Liouvillian Learning
Lorenzo Pastori, Tobias Olsacher, Christian Kokail, Peter Zoller

TL;DR
This paper introduces a method for learning and verifying Floquet Hamiltonians in digital quantum simulation, enabling efficient characterization of quantum devices and their errors, with extensions to dissipative processes.
Contribution
It proposes a scalable Floquet Hamiltonian learning technique for verifying and calibrating digital quantum simulators, including non-unitary dynamics.
Findings
Efficient polynomial-scaling measurement protocol
Application to error characterization in quantum devices
Extension to learning Floquet Liouvillians
Abstract
The goal of digital quantum simulation is to approximate the dynamics of a given target Hamiltonian via a sequence of quantum gates, a procedure known as Trotterization. The quality of this approximation can be controlled by the so called Trotter step, that governs the number of required quantum gates per unit simulation time. The stroboscopic dynamics generated by Trotterization is effectively described by a time-independent Hamiltonian, referred to as the Floquet Hamiltonian. In this work, we propose Floquet Hamiltonian learning to reconstruct the experimentally realized Floquet Hamiltonian order-by-order in the Trotter step. This procedure is efficient, i.e., it requires a number of measurements that scales polynomially in the system size, and can be readily implemented in state-of-the-art experiments. With numerical examples, we propose several applications of our method in the…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Neural Networks and Reservoir Computing
