UNSW Canberra

Publication Search Results

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  • (2022) Maranan, Noahlyn
    The 2016 vice-presidential election in the Philippines was contested on Facebook, the nation’s most prominent social media platform. Among the contenders was Ferdinand ‘Bongbong’ Marcos, son of former president Ferdinand Marcos Sr, who ruled between 1965 and 1986. Memes played a significant role in the election. They potentially enriched participatory engagement and information dissemination to a broader public. Through them, opposing camps worked through different versions of the Philippines’ past, present, and future. This case presents a novel opportunity to contribute to the growing scholarly debate about the relationship between social media and democratic politics. This study asks, “Can social media contribute to strengthening democracy in the Philippines?” It approaches this question through a conceptual framework that integrates work on democracy and political memory while also taking seriously the propensity of social media to be enlisted in information campaigns of a propagandist nature. Having analysed a sample of Facebook memes for their form and content, the study comes to an ambivalent conclusion. As immensely pliable and flexible texts, created and circulated with ease, the thesis finds that memes play a dual role in democratic politics. In the 2016 Philippine election, they (a) allowed for the inclusion of competing perspectives, narratives, and voices about Marcos Sr’s past regime and his son’s electoral bid. Rational and passionate voices, as one would expect from models of deliberative and agonistic democracy, were visible in this study. Enabled by digital platforms, memes became an important medium for the creative, potentially deliberative, and agonistic (if not outwardly antagonistic) articulation of sidelined memories about the regime of Marcos Sr. At the same time, (b) memes served as instruments for persuasive networked influence. While this may seem contrary to democratic communication, such propagandistic communication carries the potential to enrich reasoned argumentations in the broader public sphere when viewed from the lens of the wider literature on deliberative democracy. This potential, however, also depends on other factors, which include the techno-discursive platform in which propagandistic content circulates and the characteristics of the electorate.

  • (2023) Shindi, Omar
    Optimal and robust quantum control methods are essential for developing quantum technology. This thesis proposes and examines the implementation of reinforcement learning algorithms for three quantum control tasks. First, a modified tabular Q-learning (TQL) algorithm is proposed for optimal quantum state preparation. This algorithm is compared with the standard TQL method and other methods, such as the stochastic gradient descent and Krotov algorithms, in the context of quantum state preparation for a two-qubit system. The results indicate that the modified TQL algorithm outperforms standard TQL methods, in generating high-fidelity control protocols that guide the quantum state closer to the target state. Moreover, modified TQL shows stability in discovering high-fidelity control protocols regardless of changes in the length of the control protocol. The modifications on standard TQL, including a modified action selection procedure, delayed n-step reward function, and dynamic e-greedy method, improve the stability and enhance performance for discovering global optimal solutions in some cases. Subsequently, a modified Deep Q-Learning (DQL) method is proposed for optimal quantum state preparation, considering constraints like limited control resources and fixed pulse duration. The modified DQL algorithm outperforms the standard DQL in discovering high-fidelity control protocols and shows better convergence to a more effective control policy. Additionally, the improved experience replay memory delayed n-step reward function, and modified action selection method boost the exploration-exploitation ability of the DQL agent in discovering high fidelity solutions for longer protocols. For optimal quantum gate design, this thesis introduces a modified dueling DQL method. This method demonstrates superiority in constructing high-fidelity controls that mimic target gates and discover globally optimal or near-global optimal control protocols. Furthermore, the modified dueling DQL method converges more rapidly to a better control policy compared to the standard dueling DQL methods. The second part of this thesis focuses on robust quantum gate design, introducing a modified dueling Deep Q-Learning (DQL) method for the design of singlequbit gates. The proposed method outperforms the standard Dueling DQL in discovering robust high-fidelity control protocols for single-qubit gates. However, robust gate design for multi-qubit systems poses more significant challenges than for single-qubit systems. To address this, this thesis introduces the Trust Region Policy Optimization (TRPO), an on-policy reinforcement learning method, for the design of robust gates for two-qubit and three-qubit systems. Additionally, this thesis proposes an enhanced Krotov method for a robust gate design. The effectiveness of these proposed methods is presented through numerical examples of robust gate design for CNOT and Toffoli gates. Both TRPO and the improved Krotov method successfully construct robust, high-fidelity protocols capable of executing CNOT gates within a specified uncertainty range. For the Toffoli gate, TRPO manages to construct a robust control protocol applicable to varying parameters, while the improved Krotov method is successful only with a longer control protocol. The increase in the number of control protocols increases the complexity and thus increases the challenge for the improved Krotov method. However, the Hamiltonian gradient with respect to the control pulse used in the updating procedure of the improved Krotov method makes it suitable for longer control protocols. In contrast, TRPO demonstrates a stable performance for discovering robust control protocols regardless of the increase in the complexity of the control problem, whether by increasing the number of control protocols or extending the length of the control. Third, this thesis explores the model free-quantum gate design and calibration. Constructing a quantum gate design is hard when the model of a quantum system is not available due to the challenges in mathematical characterizing the quantum systems and considering all the factors in the mathematical model. A modified RL framework based on DQL procedure is proposed for model-free quantum gate design and calibration. This proposed RL framework relies only on the measurement at the end of the evolution process to identify the optimal control strategy without requiring access to the quantum system. The efficacy of the proposed approach is established numerically, demonstrating its application for model-free quantum gate design and calibration, using off-policy reinforcement learning algorithms. In summary, this thesis presents innovative RL methods for optimal and robust quantum control, contributing to the development of more resilient and efficient quantum systems.