We consider how to characterize the dynamics of a quantum system from a restricted set of initial states and measurements using Bayesian analysis. Previous work has shown that Hamiltonian systems can be well estimated from analysis of noisy data. Here we show how to generalize this approach to systems with moderate dephasing in the eigenbasis of the Hamiltonian. We illustrate the process for a range of three-level quantum systems. The results suggest that the Bayesian estimation of the frequencies and dephasing rates is generally highly accurate and the main source of errors are errors in the reconstructed Hamiltonian basis
In quantum information processing, knowledge of the noise in the system is crucial for high-precisio...
The identification of parameters in the Hamiltonian that describes complex many-body quantum systems...
Abstract. Using Bayesian experimental design techniques, we have shown that for a single two-level q...
We consider how to characterize the dynamics of a quantum system from a restricted set of initial st...
We present an empirical strategy to determine the Hamiltonian dynamics of a two-qubit system using o...
Identifying the Hamiltonian of a quantum system from experimental data is considered. General limits...
We compare the accuracy, precision, and reliability of different methods for estimating key system p...
© 2016 IEEE.Identifying parameters in the system Hamiltonian is a vitally important task in the deve...
Abstract: Quantum mechanics is one of the most interesting field in modern physics. In spite of its ...
Quantum computers promise a considerable speedup over classical computers in solving various classes...
We focus on quantum systems subject to external interactions (laser, magnetic fields) taken as contr...
In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Baye...
The identification of parameters in the Hamiltonian that describes complex many-body quantum systems...
Identifying an accurate model for the dynamics of a quantum system is a vexing problem that underlie...
In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Baye...
In quantum information processing, knowledge of the noise in the system is crucial for high-precisio...
The identification of parameters in the Hamiltonian that describes complex many-body quantum systems...
Abstract. Using Bayesian experimental design techniques, we have shown that for a single two-level q...
We consider how to characterize the dynamics of a quantum system from a restricted set of initial st...
We present an empirical strategy to determine the Hamiltonian dynamics of a two-qubit system using o...
Identifying the Hamiltonian of a quantum system from experimental data is considered. General limits...
We compare the accuracy, precision, and reliability of different methods for estimating key system p...
© 2016 IEEE.Identifying parameters in the system Hamiltonian is a vitally important task in the deve...
Abstract: Quantum mechanics is one of the most interesting field in modern physics. In spite of its ...
Quantum computers promise a considerable speedup over classical computers in solving various classes...
We focus on quantum systems subject to external interactions (laser, magnetic fields) taken as contr...
In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Baye...
The identification of parameters in the Hamiltonian that describes complex many-body quantum systems...
Identifying an accurate model for the dynamics of a quantum system is a vexing problem that underlie...
In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Baye...
In quantum information processing, knowledge of the noise in the system is crucial for high-precisio...
The identification of parameters in the Hamiltonian that describes complex many-body quantum systems...
Abstract. Using Bayesian experimental design techniques, we have shown that for a single two-level q...