Characterization and Verification of Trotterized Digital Quantum Simulation via Hamiltonian and Liouvillian Learning

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 context of verification of quantum devices: from the characterization of the distinct sources of errors in digital quantum simulators to determining the optimal operating regime of the device. We show that our protocol provides the basis for feedback-loop design and calibration of new types of quantum gates. Furthermore it can be extended to the case of non-unitary dynamics and used to learn Floquet Liouvillians, thereby offering a way of characterizing the dissipative processes present in NISQ quantum devices.

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Cited by in corpus (18)

• Learning Quantum Systems
• Non-Abelian Floquet Spin Liquids in a Digital Rydberg Simulator
• Scalably learning quantum many-body Hamiltonians from dynamical data
• Adaptive Trotterization for time-dependent Hamiltonian quantum dynamics using piecewise conservation laws
• Experimental virtual distillation of entanglement and coherence
• Reconstructing effective Hamiltonians from nonequilibrium (pre-)thermal steady states
• Hamiltonian and Liouvillian learning in weakly-dissipative quantum many-body systems
• Hamiltonian Learning in Quantum Field Theories
• The power and limitations of learning quantum dynamics incoherently
• Efficient Lindblad synthesis for noise model construction
• Cost of Emulating a Small Quantum Annealing Problem in the Circuit-Model
• Effective (Floquet) Lindblad generators from spectral unwinding
• Non-Hermitian Parent Hamiltonian from Generalized Quantum Covariance Matrix
• Lindblad-like quantum tomography for non-Markovian quantum dynamical maps
• Learning agent-based approach to the characterization of open quantum systems
• Bounded-Error Quantum Simulation via Hamiltonian and Lindbladian Learning
• Noninvertible Kramers-Wannier duality symmetries for the discrete-time quantum Ising chain
• Generalized pulse design in Floquet engineering: Application to interacting spin systems