Quantum state tomography with gradient descent

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dc.contributor.authorTorres Hernandez, Manuel Sebastian
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mikroteknologi och nanovetenskap (MC2)sv
dc.contributor.departmentChalmers University of Technology / Department of Microtechnology and Nanoscience (MC2)en
dc.contributor.examinerFrisk Kockum, Anton
dc.contributor.examinerBauch, Thilo
dc.contributor.examinerVan de Vondel, Joris
dc.contributor.supervisorFrisk Kockum, Anton
dc.date.accessioned2024-08-18T09:30:42Z
dc.date.available2024-08-18T09:30:42Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractQuantum technologies, particularly quantum computing, are advancing by leaps and bounds in recent years. Quantum computing can be implemented using two approaches: one with discrete variables utilizing qubits and the other with continuous variables inspired by the components of the electromagnetic field. A crucial aspect of quantum computing is knowing the system’s quantum state. With the quantum state, it is possible to understand and control the system, and know if an algorithm was executed correctly. However, since it is a quantum system, we cannot know the quantum state directly, but we need to infer it from different measurements. Eventually, with the collected data from the measurements, the state of the system can be reconstructed. This reconstruction process is known as quantum state tomography, which is not trivial. Different approaches have been implemented to solve the reconstruction task. The most standard approach is the maximum-likelihood method. However, it has been shown that there may be other methods that outperform this standard method in different aspects, e.g., a recently proposed machine-learning method with a conditional generative adversarial neural network (CGAN). In this thesis, we propose three methods based on gradient descent to perform quantum state tomography. We benchmark these methods against the standard method of maximum likelihood; for continuous variables, we also benchmark them against the CGAN-based method. Our results indicate that in certain parameter regimes, some gradient descent-based methods are more efficient and/or reconstruct the state better than the maximumlikelihood method and the CGAN method. The advantages we find are in terms of better overall reconstruction times, using fewer measurement operators, and reconstructing effectively even in the presence of noise in the system.
dc.identifier.coursecodeMCCX04
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308412
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectquantum computing, quantum state tomography, maximum likelihood, machine learning, gradient descent, measurement operators, discrete variables, continuous variable
dc.titleQuantum state tomography with gradient descent
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeNanotechnology (MPNAT), MSc
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