Evaluating Machine Learning Approaches for Discovering Optimal Sets of Projection Operators for Quantum State Tomography of Qubit Systems

Ivanova-Rohling, Violeta N.; Rohling, Niklas

doi:10.2478/cait-2020-0061

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Different experiments are conducted by considering MetaCentrum, Grid5000 and DAS-2 problem scenarios. The DAS-2 workload has two different variants of grid devices which are referred as DAS-2-L and DAS-2-M. Table 1 gives the summary of the problems and experiments conducted details [24,25]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

Bhargavi

Shiva

2022

Cybernetics and Information Technologies

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Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling gets affected by behavioral and primary uncertainties. Behavioral uncertainty arises due to variability in the workload characteristics, size of data and dynamic partitioning of applications. Primary uncertainty arises due to variability in data handling capabilities, processor context switching and interplay between the computation intensive applications. In this paper behavioral uncertainty and primary uncertainty with respect to tasks and resources parameters are managed using Type-2-Soft-Set (T2SS) theory. Dyna-Q-Learning task scheduling technique is designed over the uncertainty free tasks and resource parameters. The results obtained are further validated through simulation using GridSim simulator. The performance is good based on metrics such as learning rate, accuracy, execution time and resource utilization rate.

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Section: Resultsmentioning

confidence: 99%

Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

Bhargavi

Shiva

2022

Cybernetics and Information Technologies

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“…Further future research might include the application and tailoring of machine learning methods to the high-dimensional optimization problem. In reference [60], we have already applied machine learning methods and obtained rank-1 QST quorums in dimension eight which are improved compared to the result achieved by standard numerical methods used in [29].…”

Section: Discussionmentioning

confidence: 99%

Quantum state tomography as a numerical optimization problem

2021

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We present a framework that formulates the quest for the most efficient quantum state tomography measurement set as an optimization problem which can be solved numerically, where the optimization goal is the maximization of the information gain. This approach can be applied to a broad spectrum of relevant setups including measurements restricted to a subsystem. To illustrate the power of this method we present results for the six-dimensional Hilbert space constituted by a qubit-qutrit system, which could be realized e.g. by the N-14 nuclear spin-1 and two electronic spin states of a nitrogen-vacancy center in diamond. Measurements of the qubit subsystem are expressed by projectors of rank three, i.e., projectors on half-dimensional subspaces. For systems consisting only of qubits, it was shown analytically that a set of projectors on half-dimensional subspaces can be arranged in an informationally optimal fashion for quantum state tomography, thus forming so-called mutually unbiased subspaces. Our method goes beyond qubits-only systems and we find that in dimension six such a set of mutually-unbiased subspaces can be approximated with a deviation irrelevant for practical applications.

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“…In Ref. [58], we have already applied machine learning methods and obtained rank-1 QST quorums in dimension eight which are improved compared to the result achieved by standard numerical methods used in [27].…”

Section: Discussionmentioning

confidence: 99%

Quantum state tomography as a numerical optimization problem

Ivanova-Rohling,

Burkard,

Rohling

2020

Preprint

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We present a framework that formulates the quest for the most efficient quantum state tomography scheme as an optimization problem which can be solved numerically. This approach can be applied to a broad spectrum of relevant setups including measurements restricted to a subsystem. To illustrate the power of this method we present results for the six-dimensional Hilbert space constituted by a qubit-qutrit system, which could be realized e.g. by the 14 N nuclear spin-1 and two electronic spin states of a nitrogen-vacancy center in diamond. Measurements of the qubit subsystem are expressed by projectors of rank three, i.e., projectors on half-dimensional subspaces. For systems consisting only of qubits, it was shown analytically that a set of projectors on half-dimensional subspaces can be arranged in an informationally optimal fashion for quantum state tomography, thus forming socalled mutually unbiased subspaces. Our method goes beyond qubits-only systems and we find that in dimension six such a set of mutually-unbiased subspaces can be approximated with a deviation irrelevant for practical applications.

show abstract

Evaluating Machine Learning Approaches for Discovering Optimal Sets of Projection Operators for Quantum State Tomography of Qubit Systems

Cited by 6 publications

References 16 publications

Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing

Quantum state tomography as a numerical optimization problem

Quantum state tomography as a numerical optimization problem

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