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" Control and Characterization of Open Quantum Systems "
Seif Tabrizi, Seyed Alireza
Hafezi, Mohammad
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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1058085
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| Doc. No
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TL57202
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| Main Entry
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Seif Tabrizi, Seyed Alireza
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| Title & Author
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Control and Characterization of Open Quantum Systems\ Seif Tabrizi, Seyed AlirezaHafezi, Mohammad
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| College
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University of Maryland, College Park
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| Date
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2020
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| Degree
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Ph.D.
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2020
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| Note
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136 p.
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| Abstract
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The study of open physical systems concerns finding ways to incorporate the lack of information about the environment into a theory that best describes the behavior of the system. We consider characterizing the environment by using the system as a sensor, mitigating errors, and learning the physics governing systems out of equilibrium with computer algorithms. We characterize long-range correlated errors and crosstalk, which are important factors that negatively impacts the performance of noisy intermediate-scale quantum (NISQ) computing devices. We propose a compressed sensing method for detecting correlated dephasing errors, assuming only that the correlations are sparse (i.e., at most s pairs of qubits have correlated errors, where s << n(n − 1)/2, and n is the total number of qubits). Our method uses entangled many-qubit GHZ states, and it can detect long-range correlations whose distribution is completely arbitrary, independent of the geometry of the system. Our method is also highly scalable: it requires only s log n measurement settings, in contrast to the naive O(n2) estimate, and efficient classical postprocessing based on convex optimization. For mitigating the effect of errors, we consider measurements in a quantum computer. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. We experimentally illustrate this approach in the readout of trapped-ion qubits using additional spatial and temporal features in the data. Using this neural network classifier, we efficiently treat qubit readout crosstalk, resulting in a 30% improvement in detection error over the conventional threshold method. Our approach does not depend on the specific details of the system and can be readily generalized to other quantum computing platforms. To learn about physical systems using computer algorithms, we consider the problem of arrow of time. We show that a machine learning algorithm can learn to discern the direction of time’s arrow when provided with a system’s microscopic trajectory as input. Examination of the algorithm’s decision-making process reveals that it discovers the underlying thermodynamic mechanism and the relevant physical observables. Our results indicate that machine learning techniques can be used to study systems out of equilibrium, and ultimately to uncover physical principles.
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| Descriptor
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Computational physics
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Quantum physics
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Statistical physics
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Hafezi, Mohammad
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University of Maryland, College Park
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