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OSA Advanced Photonics Congress

Quantum filtering theory and the reduction of optical noises in optical detectors

Armien John Samson

Published Jan 1, 2016• 0 citations
Publisher

Abstract

In developing sensitive optical sensors, issues associated with optical crosstalk, dark count, and dark current persist. While designs have been created to mitigate the effects of these optical noises on the performance of the sensors, they are never fully addressed. This is mainly due to the highly probabilistic nature of the interaction of light and matter. This paper theoretically explores possible solutions that could lead to the significant reduction of these optical noises by presenting the problem in both the Markovian and Bayesian quantum feedback schemes and identifying the parameters that can be controlled and/or regulated. This paper then attempts to translate the quantum processes into realizable materials and/or systems suitable for fabrication or integration to existing system.

DOI
10.1364/iprsn.2016.jtu4a.26

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In developing sensitive optical sensors, issues associated with optical crosstalk, dark count, and dark current persist. While designs have been created to mitigate the effects of these optical noises on the performance of the sensors, they are never fully addressed. This is mainly due to the highly probabilistic nature of the interaction of light and matter. This paper theoretically explores possible solutions that could lead to the significant reduction of these optical noises by presenting the problem in both the Markovian and Bayesian quantum feedback schemes and identifying the parameters that can be controlled and/or regulated. This paper then attempts to translate the quantum processes into realizable materials and/or systems suitable for fabrication or integration to existing system.
In developing sensitive optical sensors, issues associated with optical crosstalk, dark count, and dark current persist. While designs have been created to mitigate the effects of these optical noises on the performance of the sensors, they are never fully addressed. This is mainly due to the highly probabilistic nature of the interaction of light and matter. This paper theoretically explores possible solutions that could lead to the significant reduction of these optical noises by presenting the problem in both the Markovian and Bayesian quantum feedback schemes and identifying the parameters that can be controlled and/or regulated. This paper then attempts to translate the quantum processes into realizable materials and/or systems suitable for fabrication or integration to existing system.
Quantum sensors represent a revolutionary technology with the potential to surpass the limits of classical sensors across a wide range of applications, including precision measurement, navigation, and medical diagnostics. However, the realization of this potential is significantly hindered by the pervasive presence of noise. This work addresses the critical need for accurate noise modeling and effective suppression techniques within quantum sensors. We introduce a novel noise model that incorporates both thermal and coherent noise sources, explicitly accounting for the quantum mechanical nature of these sensors. This model is then utilized to design and analyze various noise suppression strategies, including feedback control algorithms and advanced filtering methods. The theoretical framework presented here provides a foundation for improved sensor design and operation, ultimately driving advancements in quantum sensing technology. The core claim of this work is that precise noise modeling and effective suppression techniques are paramount for enhancing the precision of quantum sensors. This is achieved through the establishment of a new noise model and the subsequent design of suppression techniques such as feedback control and filtering. The novelty lies in the detailed and accurate representation of noise sources within the quantum sensor system.
General relativity predicts the existence of Gravitational Waves (GWs), ripples in spacetime that are now observable to highly sensitive interferometers. However, Quantum Noise (QN) poses a significant challenge for Gravitational Wave (GW) interferometers, influencing the entire bandwidth range (10-10,000 Hz) of the current GW detectors. At higher frequencies, Shot Noise (SN) predominates, while at lower frequencies, Radiation Pressure Noise (RPN) becomes the limiting factor, reducing both sensitivity and measurement precision. Squeezing techniques are implemented to address this issue by confining uncertainty within one quadrature of the light field. During O4, LIGO successfully implemented Frequency-Dependent Squeezing (FDS), while Virgo integrated and commissioned the FDS system without actively operating it during the science run. By employing a 300-meterlong detuned Filter Cavity (FC), the squeezing ellipse is rotated, allowing phase squeezing at high frequencies to reduce and amplitude squeezing at low frequencies to mitigate RPN, ultimately refining detection capabilities across the frequency spectrum. This doctoral thesis focuses on a promising alternative technique for future GW detectors, that achieves broadband QN reduction by exploiting Einstein–Podolsky–Rosen (EPR) quantum entanglement. This approach involves injecting two entangled, squeezed beams at different frequencies into the dark port of the interferometer, enabling it to act as both a FC and a GW detector. To experimentally investigate these techniques, the suspended Interferometer for Ponderomotive Squeezing (SIPS) platform adopts a large-scale GW detector layout featuring a Michelson configuration with Fabry-Perot (FP) arm cavities. To guarantee the extreme optical alignment necessary for quantum noise mitigation, a high-precision, real-time local control system was developed for the SIPS main optics. Using a dummy mirror suspended in a double-pendulum configuration with monolithic fibers, a custom real-time signal processing and feedback control architecture was developed. This subsystem utilizes a PXI hardware framework and position-sensitive detectors (Power Spectral Densitys (PSDs)) implemented in LabVIEW to actively damp the mechanical resonances of the suspensions. Extending these core activities toward future infrastructures, this work directly contributed to the realization and commissioning of the new PNRR ETIC-GALILEO quantum optics cleanroom facility in Genoa for the Einstein Telescope (ET). Within this infrastructure, preliminary optical studies were initiated for Einstein Telescope Low Frequency (ET-LF) low-loss Faraday isolators operating at 1550 nm. Concurrently, numerical simulations were performed using Finesse3 to model and investigate candidate excess noise sources currently limiting the sensitivity of Advanced Virgo Plus
This paper explores a novel approach to Bayesian inference within quantum systems, specifically addressing the significant challenge posed by decoherence. Traditional Bayesian inference relies on maintaining quantum coherence, which is highly susceptible to environmental noise. We introduce a framework where decoherence is actively mitigated through dynamically controlled environmental interactions. The core concept involves utilizing precisely engineered external fields, coupled with real-time feedback loops, to counteract decoherence effects during the inference process. Unlike passive isolation or conventional error correction strategies, our method offers an active, responsive solution that maintains coherence for extended periods. We detail the theoretical underpinnings of this approach, focusing on the mathematical formulation of the inference process, incorporating decoherence models and the control strategies employed. The proposed system leverages quantum measurement to update Bayesian probabilities, with the control parameters designed to minimize the impact of decoherence on the measurement outcomes. This work lays the groundwork for performing accurate Bayesian inference on quantum systems, potentially unlocking new possibilities in quantum computing and information processing. The key mathematical concepts employed include the Schrödinger equation, density matrix formalism, and Bayesian probability theory. The system is designed to achieve a target accuracy in the Bayesian inference, even in the presence of significant decoherence.