University of Illinois Urbana-Champaign

Nonclassicality in noisy quantum networks

Doolittle, Brian Dopkins

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DOOLITTLE-DISSERTATION-2023.pdf

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https://hdl.handle.net/2142/121945

Description

Title
Nonclassicality in noisy quantum networks
Author(s)
Doolittle, Brian Dopkins
Issue Date
2023-09-28
Director of Research (if dissertation) or Advisor (if thesis)
Chitambar, Eric A.
Doctoral Committee Chair(s)
Clark, Bryan K.
Committee Member(s)
  • Kwiat, Paul G.
  • Dahmen, Karin A.
Department of Study
Physics
Discipline
Physics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Quantum
  • Nonclassicality
  • Quantum Networks
  • Noisy Quantum Networks
  • Variational Quantum Optimization
  • Quantum Network Automation
  • Quantum Network Optimization
  • Quantum Network Simulation
  • Communication Networks
  • Nonlocality
  • Variational Quantum Networking
  • Hybrid Quantum-Classical Computing
Abstract
Quantum networks are rapidly being developed using the noisy quantum devices available today. As quantum networks scale, noise will lead to significant challenges in quantum network characterization, design, and automation, challenges that classical methods may be ill equipped to tackle. Moreover, the advantage and value of quantum networks is not well understood in the presence of noise, making it difficult to justify the cost of quantum network development for real-world applications. In this dissertation, we define operational nonclassicality as a quantifier of quantum advantage in general multipoint communication networks and describe a procedure for deriving operational tests of nonclassicality in general communication networks. Then, we develop a quantum-hardware-compatible variational optimization framework for optimizing quantum networks to exhibit nonclassicalilty. In a wide range of communication network topologies, including nonsignaling networks, multiaccess networks, broadcast networks, and interference networks, we derive operational tests that witness nonclassicality. We then use our variational framework to optimize various quantum resource configurations for maximal performance against these operational tests of nonclassicality. In all communication network topologies, we find examples where quantum resources lead to observable violations of the nonclassicality witnesses, implying that quantum resources provide a strict advantage over classical resources. Furthermore, we investigate how the presence of noise diminishes these advantages, and by extension, the value of quantum resources. Finally, we demonstrate that our variational optimization techniques can be deployed on quantum hardware and applied well beyond the scope of finding nonclassical quantum behaviors. In conclusion, we find that nearly all quantum resource configurations in communication networks can provide operational advantage as witnessed by operational tests of nonclassicality. These nonclassical network behaviors show novel ways that quantum physics defies the classical assumptions of locality, causality, and realism, but nonclassicality can also be used to test and certify quantum resources in communication networks. Furthermore, nonclassicality can also provide advantages in information security and distributed computing. We assert that variational quantum optimization techniques are well-suited to design and automation tasks in quantum networks. The advantages of these methods are that they are hardware agnostic, do not require full network characterization, and can optimize quantum systems against their inherent and unknown noise models. Thus, we introduce variational quantum networking as an engineering paradigm for designing and automating noisy quantum networks. In many ways, variational quantum networking circumvents the challenges of characterizing, designing, and automating noisy quantum networks.
Graduation Semester
2023-12
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/121945
Copyright and License Information
Copyright 2023 Brian Doolittle

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