There exist several well–known characterizations of Poisson and mixed Poisson point processes (Cox processes) by thinning and splitting procedures. So a point process is necessarily a Cox process if for arbitrary small thinning parameter it can be obtained by a thinning of some other point process [30]. Poisson processes are characterized by the independence of the two random subconfigurations obtained by an independent splitting of the configuration into two parts [11]. For quantum mechanical particle systems beam splittings which are well–known in quantum optics provide analogous procedures. It is shown that coherent states respectively mixtures of them can be characterized in the same way as Poisson processes and Cox processes. Moreover, for the position distributions of these states which are “classical” point processes just the above mentioned characterizations are obtained. As example of mixed coherent states we consider Gaussian states which arise as equilibrium states of ideal Bose gases.There exist several well–known characterizations of Poisson and mixed Poisson point processes (Cox processes) by thinning and splitting procedures. So a point process is necessarily a Cox process if for arbitrary small thinning parameter it can be obtained by a thinning of some other point process [30]. Poisson processes are characterized by the independence of the two random subconfigurations obtained by an independent splitting of the configuration into two parts [11]. For quantum mechanical particle systems beam splittings which are well–known in quantum optics provide analogous procedures. It is shown that coherent states respectively mixtures of them can be characterized in the same way as Poisson processes and Cox processes. Moreover, for the position distributions of these states which are “classical” point processes just the above mentioned characterizations are obtained. As example of mixed coherent states we consider Gaussian states which arise as equilibrium states of ideal Bose gases.

Mathematische NachrichtenOct 1, 20004 citations

Parametric processes

Avigail Yariv, James E. Pearson

Progress in Quantum ElectronicsJan 1, 19698 citations

Characterizing Gaussian quantum processes with Gaussian resources

Logan W. Grove, Pratik J. Barge, Kevin Valson Jacob

Characterizing quantum processes is indispensable for the implementation of any task in quantum information processing. In this paper, we develop an efficient method to fully characterize arbitrary Gaussian processes in continuous-variable quantum systems. This is done by directly obtaining all elements of the symplectic matrix that describes the process. Only Gaussian resources such as coherent probes and quadrature measurements are needed for this task. The method is efficient, involving only $O({N}^{2})$ steps to characterize an $N$ -mode system. Further, the method is resilient to uniform loss. We simulate this procedure using the Python package Strawberry Fields. We observe that heterodyne measurements outperform homodyne measurements for reconstructing Gaussian processes.Characterizing quantum processes is indispensable for the implementation of any task in quantum information processing. In this paper, we develop an efficient method to fully characterize arbitrary Gaussian processes in continuous-variable quantum systems. This is done by directly obtaining all elements of the symplectic matrix that describes the process. Only Gaussian resources such as coherent probes and quadrature measurements are needed for this task. The method is efficient, involving only $O({N}^{2})$ steps to characterize an $N$-mode system. Further, the method is resilient to uniform loss. We simulate this procedure using the Python package Strawberry Fields. We observe that heterodyne measurements outperform homodyne measurements for reconstructing Gaussian processes.

arXiv:2503.13698Physical Review AAug 13, 20250 citations

Parametrized process characterization with reduced resource requirements

Vicente Leyton‐Ortega, Tyler Kharazi, Raphael C. Pooser

Quantum process tomography (QPT) is a powerful tool to characterize quantum operations, but it requires considerable resources, making it impractical for more than two-qubit systems. This work proposes an alternative approach that requires significantly fewer resources for unitary process characterization with a built-in method for state preparation and measurement error mitigation. By measuring the quantum process as rotated through the $X$ and $Y$ axes on the Bloch sphere, we can acquire enough information to reconstruct the quantum process matrix $\ensuremath{\chi}$ and measure its fidelity. We test the algorithm's performance against standard QPT using simulated and physical experiments on several IBM quantum processors and compare the resulting process matrices. We demonstrate with numerical experiments that the method can improve gate fidelity via a noise reduction in the imaginary part of the process matrix, along with a stark decrease in the number of experiments needed to perform the characterization.Quantum process tomography (QPT) is a powerful tool to characterize quantum operations, but it requires considerable resources, making it impractical for more than two-qubit systems. This work proposes an alternative approach that requires significantly fewer resources for unitary process characterization with a built-in method for state preparation and measurement error mitigation. By measuring the quantum process as rotated through the $X$ and $Y$ axes on the Bloch sphere, we can acquire enough information to reconstruct the quantum process matrix $\ensuremath{\chi}$ and measure its fidelity. We test the algorithm's performance against standard QPT using simulated and physical experiments on several IBM quantum processors and compare the resulting process matrices. We demonstrate with numerical experiments that the method can improve gate fidelity via a noise reduction in the imaginary part of the process matrix, along with a stark decrease in the number of experiments needed to perform the characterization.

arXiv:2109.10873Physical Review AMay 5, 20220 citations

Experiment Characterisation

Vera M. Schäfer

Springer thesesJan 1, 20200 citations

O({N}^{2})

steps to characterize an

N

-mode system. Further, the method is resilient to uniform loss. We simulate this procedure using the Python package Strawberry Fields. We observe that heterodyne measurements outperform homodyne measurements for reconstructing Gaussian processes.

X

and

Y

axes on the Bloch sphere, we can acquire enough information to reconstruct the quantum process matrix $\ensuremath{\chi}$ and measure its fidelity. We test the algorithm's performance against standard QPT using simulated and physical experiments on several IBM quantum processors and compare the resulting process matrices. We demonstrate with numerical experiments that the method can improve gate fidelity via a noise reduction in the imaginary part of the process matrix, along with a stark decrease in the number of experiments needed to perform the characterization.