Machine Learning Assisted Quantum State Tomography

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dc.contributor.advisorLvovsky, Alexander
dc.contributor.advisorSanders, Barry C.
dc.contributor.authorKurmapu, Murali Krishna
dc.contributor.committeememberSimon, Christoph
dc.contributor.committeememberOblak, Daniel
dc.date2020-11
dc.date.accessioned2020-09-10T18:11:10Z
dc.date.available2020-09-10T18:11:10Z
dc.date.issued2020-09-08
dc.description.abstractQuantum state tomography (QST) can be posed as an optimization problem, where the goal is to find the quantum state ρ that makes the observed data more likely. Obtaining the complete description of a quantum state is crucial for many quantum informational and computing tasks, and various approaches have been proposed to solve this fundamental problem. This thesis aims at presenting two models based on feedforward neural network architecture for QST. The first model is based on a multi-layer perceptron network, while the second one is based on autoregressive neural networks. We show that the perceptron model demonstrates less overfit compared to the standard iterative maximum likelihood model, however, the number of parameters in the network increases exponentially with respect to the system size. In contrast to the perceptron model, we show that the autoregressive model scales efficiently since the number of parameters grows polynomially instead of exponentially, making it suitable for QST of many-qubit systems. For the perceptron model, we presented a reconstruction of an engineered 4-qubit W state and an arbitrary optical superposition of up to 2 photons. On the other hand, using the autoregressive model, we performed QST of a 20-qubit engineered quantum state of trapped ions observed in an experiment. To the best of our knowledge, our results are the first of its kind in performing QST of complex and highly entangled out-of-equilibrium states produced by dynamics of an Ising-type Hamiltonian, engineered via laser fields on 1D trapped ion systemsen_US
dc.identifier.citationKurmapu, M. K. (2020). Machine Learning Assisted Quantum State Tomography (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/38174
dc.identifier.urihttp://hdl.handle.net/1880/112504
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectmachine learningen_US
dc.subjectphysicsen_US
dc.subjectquantumen_US
dc.subjectquantum state tomographyen_US
dc.subjectautoregressive networken_US
dc.subject.classificationPhysicsen_US
dc.titleMachine Learning Assisted Quantum State Tomographyen_US
dc.typemaster thesisen_US
thesis.degree.disciplinePhysics & Astronomyen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
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