Framework for Learning and Control in the Classical and Quantum Domains

Vedaie, Seyed Shakib

Framework for Learning and Control in the Classical and Quantum Domains

dc.contributor.advisor	Sanders, Barry C.
dc.contributor.author	Vedaie, Seyed Shakib
dc.contributor.committeemember	Hobill, David W.
dc.contributor.committeemember	Oblak, Daniel
dc.contributor.committeemember	Far, Behrouz H.
dc.contributor.committeemember	Haljan, Paul C.
dc.date	2024-11
dc.date.accessioned	2024-06-25T17:38:19Z
dc.date.available	2024-06-25T17:38:19Z
dc.date.issued	2024-06-20
dc.description.abstract	Control and learning are essential to technological advancement, both in the classical and quantum domains, yet their interrelationship is insufficiently clear in the literature, especially between classical and quantum definitions of control and learning. In this thesis, we aim to construct a framework that formally relates learning and control, both classical and quantum, to each other, with this formalism showing how learning can aid control. We formulate new versions of quantum learning and control that essentially quantise classical learning and control, respectively. Furthermore, our framework helps identify interesting unsolved problems in the nexus of classical and quantum control and learning and helps choose tools to solve problems. Our unification of these fields relies on diagrammatically representing the state of knowledge, which elegantly summarises existing knowledge and exposes knowledge gaps. As use cases, we cast the well-studied problem of adaptive quantum-enhanced interferometric phase estimation as a supervised learning problem for devising feasible control policies and develop effective quantum control for two-qubit gate design with trapped ions. Informed by the knowledge that the plant, i.e.~ion trap, is a channel, we develop a comprehensive model of controlled open-system dynamics described by a quantum master equation and validate our model based on empirical data gathered from a control system for preparing Bell states. We then employ global optimisation to design pulse sequences for achieving a robust, rapid two-qubit gate for a chain of seven trapped $^{171}$Yb$^{+}$ ions by optimising over numerically integrated quantum master equation solutions. We further explore the nexus of classical and quantum learning through hybrid classical-quantum learning algorithms. We introduce the ``analogue-quantum kitchen sinks'' algorithm, a quantum extension of the classical "random kitchen sinks," which employs an analogue-quantum computer for mapping data features into new features in a non-linear manner. A classical algorithm can then perform machine learning tasks using the new features. We show the effectiveness of our algorithm for performing binary classification on both a synthetic and a real-world data set through computer simulation of a quantum annealer's operation.
dc.identifier.citation	Vedaie, S. S. (2024). Framework for learning and control in the classical and quantum domains (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/119007
dc.identifier.uri	https://doi.org/10.11575/PRISM/46603
dc.language.iso	en
dc.publisher.faculty	Science
dc.publisher.institution	University of Calgary
dc.rights	University 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.
dc.subject	Quantum control
dc.subject	Quantum gates
dc.subject	Trapped ions
dc.subject	Machine learning
dc.subject.classification	Physics
dc.title	Framework for Learning and Control in the Classical and Quantum Domains
dc.type	doctoral thesis
thesis.degree.discipline	Physics & Astronomy
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.