Photonic approaches to multi-party entanglement in solids and learning in the brain

Understanding the fundamental quantum and classical properties of photons is crucial in a wide range of applications. Photons exhibit quantum entanglement, the strongest form of correlation that has been observed between subsystems. Entanglement is perhaps the most valuable resource for many quantum information and quantum computation tasks, empowering them to be superior to their classical counterparts. Undoubtedly, understanding how entanglement can be generated, manipulated, and measured are among the most pre-eminent research questions in quantum information processing. However, the realm of influence of photons is not just limited to information technologies. Photons are, surprisingly, being produced constantly in biological systems as well. Modeling and characterizing the involvement of photons in various biological processes are active areas of research. In the first part of my Ph.D. program, I focused on the problem of quantifying multi-partite entanglement in a solid, an atomic frequency comb (AFC) crystal, using a single photon. We proposed a novel entanglement witness and showed that our witness is well suited for demonstrating and quantifying multi-partite entanglement in AFC systems. Our results are the first demonstration of multi-partite entanglement in a solid between over two hundred ensembles each containing a billion atoms. The second project of my Ph.D. was motivated by the question of why brain cells are producing biophotons. Photons are known as perfect carriers for information. The brain is the computation centre where all the learning and decision makings are happening. Learning, in theory, is an informational task, and its biological realization in the brain is not completely understood. We asked if photons could play a role in the process of learning in the brain. A common form of learning involves multiple cycles of training, evaluating, and receiving feedback. Both forward and backward parts of these cycles require an information flow. Existing theories mainly focus on neural electrochemical signals. We proposed that biophotons can propagate the feedback teaching signals required for learning in the brain. By multiple numerical experiments simulated on an artificial neural network, we demonstrated that stochastic emissions of biophotons carrying partial backward information enable learning in the network.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Physics & Astronomy

Grantor dc:publisher.institution

Science

Year dc:date.issued

2021

Author dc:creator

• Zarkeshian, Parisa

Advisor dc:contributor.advisor

• Simon, Christoph

Committee members dc:contributor.committeemember

• Barzanjeh, Shabir
• Oblak, Daniel
• Craddock, Travis
• Jackel, Brian

• Quantum
• Multipartite Entanglement
• Backpropagation
• Learning in the Brain
• Biophoton

Statement 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.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/39094

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/113732