UNSW Canberra

Publication Search Results

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  • (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.

  • (2022) Ali, Muhammad
    This thesis addresses a key challenge for creating synthetic distribution networks and open-source datasets by combining the public databases and data synthesis algorithms. Novel techniques for the creation of synthetic networks and open-source datasets that enable model validation and demonstration without the need for private data are developed. The developed algorithms are thoroughly benchmarked against existing approaches and validated on industry servers to highlight their usefulness in solving real-world problems. A review using novel techniques that provides unique insights into the literature is conducted to identify research gaps. Based on this review, three contributions have been made in this thesis. The first contribution is the development of a data protection framework for anonymizing sensitive network data. A novel approach is proposed based on the maximum likelihood estimate for estimating the parameters that represent the actual data. A data anonymization algorithm that uses the estimated parameters to generate realistic anonymized datasets is developed. A Kolmogorov-Smirnov test criteria is used to create realistic anonymized datasets. Validation is carried out by collecting actual network data from an energy company and comparing it to anonymized datasets created using the methods developed in this thesis. The application of this method is shown by performing simulation studies on the IEEE 123-node test feeder. The second contribution is developing a practical approach for creating synthetic networks and datasets by integrating the open-source data platforms and synthesis methods. New data synthesis algorithms are proposed to obtain the network datasets for electricity systems in a chosen geographical area. The proposed algorithms include a topology for designing power lines from road infrastructure, a method for computing the lengths of power lines, a hub-line algorithm for determining the number of consumers connected to a single transformer, a virtual layer approach based on FromNode and ToNode for establishing electrical connectivity, and a technique for ingesting raw data from the developed network to industrial data platforms. The practical feasibility of the proposed solutions is shown by creating a synthetic test network and datasets for a distribution feeder in the Colac region in Australia. The datasets are then validated by deploying them on industry servers. The results are compared with actual datasets using geo-based visualizations and by including feedback from industry experts familiar with the analysis. The third contribution of this thesis is to address the problem of electric load profile classification in the context of buildings. This classification is essential to effectively manage energy resources across power distribution networks. Two new methods based on sparse autoencoders (SAEs), and multi-stage transfer learning (MSTL) are proposed for load profile classification. Different from conventional hand-crafted feature representations, SAEs can learn useful features from vast amounts of building data in an unsupervised automatic way. The problems of missing data and class imbalance for building datasets are addressed by proposing a minority over-sampling algorithm that effectively balances missing or unbalanced data by equalizing minority and majority samples for fair comparisons. The practical feasibility of the methodology is shown using two case studies that include both public benchmark and real-world datasets of buildings. An empirical comparison is conducted between the proposed and the state-of-the-art methods in the literature. The results indicate that the proposed method is superior to traditional methods, with a performance improvement from 1 to 10 percent.