Ultrafast Quantum State Generation and Measurement in Nonlinear Nanophotonics
Year: 2025
Type: dissertation
Abstract: While many physical systems, including superconductors, trapped atoms, molecules, and acoustic resonators can process quantum information, photonics holds several fundamental advantages. Most photonics systems not only offer the convenience of room temperature operation but also shed the scalability limitations imposed by cryogenic and high vacuum environments. Integrated photonics has shrunk room-sized experiments to a chip-scale device while improving performance and versatility. Operating at optical frequencies offers information bandwidths orders of magnitude larger than what is achievable with microwave or trapped atom experiments. In this thesis, we propose nanophotonic optical parametric amplifiers (OPAs) on a thin-film lithium niobate (TFLN) chip-scale platform for quantum information processing. Through dispersion-engineering, we achieve the distortion-free propagation of ultrafast pulses necessary for information clock rates above 1 THz. We investigate OPAs as ultrashort entangled pair sources and generate biphotons with a 165-fs temporal duration. We show that their generation efficiency and signal-to-noise performance is state-of-the-art at 2 µm and on-par with contemporary telecom-band sources. We explore OPAs as quantum measurement devices, and demonstrate all-optical single-photon level detection with a dead time of 75 fs. Finally, we show that OPAs can be used to recover continuous-variable quantum information by reconstructing the Wigner function of a 2.41 dB squeezed state encoded in a 154-fs pulse. This technique is loss-tolerant and offers a maximum clock speed of 6.5 THz. TFLN hosts a variety of high-performance optical devices including filters, modulators, resonators, III-V gain media, all of which are compatible with OPAs. Our results highlight ultrafast OPAs as the fundamental building blocks needed to realize large-scale circuits for all-optical quantum information processing.
Source: Caltech Library
DOI: 10.7907/s4n5-2405
Author: Williams, James Anthony
Institution: California Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G3639383972, G3655398478, G6606551169
OpenAlex ID: W6977688763
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