Suppression of Noise in Classical and Quantum Optics

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


Noise is a fundamental feature of quantum mechanics. It also defines the practical limitations of tools used across science and technology, such as sensors or measurement apparatus. In this thesis, we introduce theoretical and experimental work
that focusses on reducing noise, from both classical and quantum sources, in a number of different scenarios.
We begin by looking at absorbance estimation through the Beer-Lambert law.
We introduce a method of reducing the effect of amplitude or intensity noise of the
laser source on the produced estimate of the absorbance of the sample. We show
that optimising the length of material that the light passes through can nearly entirely mitigate the effects of excess noise on the input optical beam, which limits
the improvement gained by applying quantum states (Fock states) to this sensor to
around a 20% improvement over the best classical strategy. We show experimental
validation of this theory.
We also look at an experimental technique for reducing classical amplitude noise
in pulsed laser systems, using an asymmetric interferometer. We discuss a number
of practical limitations of the scheme and demonstrate how these can be avoided
by implementation in solid and hollow-core optical fibre. We show that all of the
classical amplitude noise can be removed (in a particular bandwidth) to achieve light
that is only limited by quantum fluctuations. We report theoretical and experimental
work that show how this technique can be extended to suppress noise in multiple
frequency bands.
Finally, we investigate practical ways of generating squeezed light in integrated
silicon and silicon nitride devices. We show that both platforms display promising signs that they are appropriate to generate squeezing in, but demonstration of
quantum noise reduction is yet to be achieved.
Date of Award19 Mar 2019
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorJonathan C F Matthews (Supervisor) & Dylan Mahler (Supervisor)

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