Conference Publications

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http://hdl.handle.net/10204/11486

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Browsing Conference Publications by browse.metadata.impactarea "Bio-Photonics"

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    Comparing amplitude-based and phase-based quantum plasmonic biosensing
    (2024) Mpofu, Kelvin T; Mthunzi-Kufa, Patience
    The utilization of quantum resources can enhance the sensitivity of conventional measurement techniques beyond the standard quantum limit (SQL). The objective of quantum metrology is to enable such quantum enhancements in practical devices. To achieve this objective, it is essential to have devices that are compatible with existing quantum resources operating within the SQL. Plasmonic sensors are promising candidates among these devices since they are extensively employed in biochemical sensing applications. Plasmonic sensors exhibit a response to slight variations in the local refractive index, which manifests as a shift in their resonance response. This shift, in turn, induces changes in the amplitude and phase of the probing light. By utilizing quantum states of light, such as NOON states, squeezed states, or Fock states, to probe these sensors, the measurement noise floor can be lowered, enabling the detection of signals below the SQL. In this study, we compare two configurations of quantum plasmonic sensing: phase-based and amplitude-based. By considering the Quantum Cram´er Rao bound for both configurations, we demonstrate that the phase-based configuration can more effectively exploit the available quantum resources than the amplitude-based configuration. A limitation of this work is that it did not consider loss.
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    Parametrized quantum circuits for reinforcement learning
    (2024) Mpof, Kelvin T; Mthunzi-Kufa, Patience
    Research on quantum computing is still in its infancy, but it has a lot of potential uses. One topic with potential is machine learning, namely in the field of reinforcement learning. This work examines the integration of parametrized quantum circuits (PQC) into reinforcement learning (RL) algorithms, assessing the potential of quantum-enhanced models to solve classical RL tasks. It closely follows the example found on the TensorFlow website. This paper reviews applications of quantum reinforcement learning (QRL). We examine PQCs in a standard RL scenario, the CartPole-vl environment from Gym, using TensorFlow Quantum and Cirq, to evaluate the relative performance of quantum versus conventional models. In comparison to conventional deep neural network (DNN) models, PQCs show slower convergence and higher processing needs, even if they are still able to learn the task and perform competitively. After they are fully trained, the quantum models show unique difficulties during the early training stages and reach a performance stability level like classical methods. This study sheds light on the present constraints as well as possible uses of quantum computing in reinforcement learning, particularly in situations with intricate, high-dimensional settings that prove difficult for classical computers to handle effectively. As we look to the future, we suggest that investigating hybrid quantum-classical algorithms, developing quantum hardware, and using quantum RL for increasingly difficult tasks are essential first steps. The study presents findings from both a classical reinforcement learning algorithm and a quantum integrated reinforcement learning algorithm. To provide a reliable comparison between quantum reinforcement algorithms and their classical equivalents, further work remains. This work lays the groundwork for future advances in the field by investigating the viability and use of quantum algorithms in reinforcement learning, even if it is not particularly unique. The purpose of this work is to help newcomers to this emerging field of study.
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    Quantum enhancement in the limit of detection measurement of a phase-based plasmonic biosensor including loss
    (2024) Mpofu, Kelvin T; Mthunzi-Kufa, Patience
    Quantum states of light allow for highly sensitive biosensing configurations, surpassing the limitations imposed by shot-noise. In this theoretical study, we focus on optical plasmonic sensors, which have extensive applications in disease diagnostics, including detection of diseases like HIV. Our investigation involves simulating the impact of quantum states of light, such as the NOON state and squeezed states, on enhancing the limit of detection in a plasmonic phase-sensing biosensor, surpassing coherent light states’ shot-noise limit. Specifically, we explore the use of quantum states to improve the limit of detection in phase-based biosensors for HIV detection, operating below the shot-noise limit. Through our analysis, we demonstrate that incorporating quantum states of light in surface plasmon resonance (SPR) biosensing leads to enhanced performance compared to classical states. Moreover, we take into account the impact of environmental losses in the biosensing setup, considering the real-world challenges in practical implementation. Our findings emphasize the potential of quantum SPR biosensors in the development of novel disease diagnostics devices.