Student Projects

Supervisors: Dr Ria Rushin Joseph, ria.joseph@deakin.edu.au

Dr Sutharshan Rajasegarar, sutharshan.rajasegarar@deakin.edu.au

Project Description:

Examining the dynamic behaviour of one-dimensional Fermi gases [2, 4] at finite temperatures, has been effectively addressed through the utilization of the Majorana P-function method [1], as outlined in the publication [2]. This project endeavours to reshape the simulation of this intricate problem by adapting it for execution on quantum computing platforms such as IBM Qiskit or similar alternatives [3,5]. The overarching goal is to shift from classical to quantum  computational paradigms, enabling the simulation of authentic quantum problems on quantum computers. This strategic transition aims to circumvent challenges associated with classical computing, particularly those arising with an increase in system size. By thoroughly investigating this quantum approach, valuable insights may be gained, offering a promising avenue for overcoming hurdles in large-scale simulations of quantum problems.

[1] Joseph, R. R., Rosales-Zárate, L. E., & Drummond, P. D. (2018). Phase space methods for Majorana fermions. Journal of Physics A: Mathematical and Theoretical, 51(24), 245302.

[2] Joseph, R. R., Rosales-Zárate, L. E., & Drummond, P. D. (2018). Finite-temperature dynamics of shock waves in an ultracold Fermi gas. Physical Review A, 98(1), 013638.

[3] Brown, K. L., Munro, W. J., & Kendon, V. M. (2010). Using quantum computers for quantum simulation. Entropy, 12(11), 2268-2307.

[4] He, W. B., Chen, Y. Y., Zhang, S., & Guan, X. W. (2016). Universal properties of Fermi gases in one dimension. Physical Review A, 94(3), 031604.

[5] haq Shaik, E., & Rangaswamy, N. (2020, October). Implementation of quantum gates based logic circuits using IBM Qiskit. In 2020 5th International conference on computing, communication and security (ICCCS) (pp. 1-6). IEEE.

The project will unfold through a structured three-step process:

1. Conducting a comprehensive literature review.

2. Developing representations of the quantum system within the quantum simulator.

3. Executing the implementation on an actual quantum computer.

Keywords: 

Quantum computing, Quantum simulations

Necessary Skills:

• Python programming, Mathematics, Qiskit

Supervisors: Dr Lei Pan, l.pan@deakin.edu.au 

                       Dr Sutharshan Rajasegarar, sutharshan.rajasegarar@deakin.edu.au

Project Description:

Quantum computers are becoming a reality in our life with giant companies like Google, Microsoft and IBM experimenting prototypes. The advantages of the quantum computers and quantum algorithms are obvious in superb performance. However, the quantum algorithms have not been fully investigated in the context of machine learning algorithms. Existing studies only present primitive ideas, such as:

• Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N. and Lloyd, S., 2017. Quantum machine learning. Nature, 549(7671), pp.195-202.

• Schuld, M., Sinayskiy, I. and Petruccione, F., 2015. An introduction to quantum machine learning. Contemporary Physics, 56(2), pp.172-185.

• Saggio, V., Asenbeck, B.E., Hamann, A., Strömberg, T., Schiansky, P., Dunjko, V., Friis, N., Harris, N.C., Hochberg, M., Englund, D. and Wölk, S., 2021. Experimental quantum speed-up in reinforcement learning agents. Nature, 591(7849), pp.229-233.

• Liu, N. and Rebentrost, P., 2018. Quantum machine learning for quantum anomaly detection. Physical Review A, 97(4), p.042315.

The project consists of three major parts:

The first part of this project is to conduct a critical literature review on the currently published literature. Then the students are expected to improve one QML algorithms in the reviewed papers with novel contributions. The final part includes empirical evaluation and theoretical analysis of these algorithms.

Keywords: 

Quantum computing, machine learning

Necessary Skills:

• Python programming in machine learning

Supervisor: Dr Sutharshan Rajasegarar, sutharshan.rajasegarar@deakin.edu.au

Project Description:

Detecting anomalous measurements from data is important for finding interesting events or security purposes. Quantum machine learning based methods can be used to model the normal patterns in the data and find the anomalies. The aim of the project includes, performing literature survey on existing quantum machine learning based anomaly detection methods, Implementing, and comparing some of the latest methods for detecting anomalies using publicly available data. Propose improvements to the existing methods and evaluate them.

Keywords: 

Quantum computing, Quantum machine learning, Machine learning, Deep learning, Anomaly detection, Outlier detection.

Necessary Skills:

• Programming knowledge in Python

• Knowledge about Machine learning, Quantum computing

Supervisor: Hon. Assoc. Prof. Jacob L. Cybulski, jacob.cybulski@deakin.edu.au

Project Description:

Quantum Computing is a new and exciting area of Science, which intersects Physics, Mathematics and Computer Science. Quantum algorithms are known to solve problems, which until recently have been considered unsolvable classically due to their inherent complexity. Typically a high-level quantum algorithm in Python generates a quantum circuit, which can be executed on a real quantum machine or a simulator. Unfortunately, quantum circuits hard-code their data, so that an application of an algorithm to new data leads to a new circuit. This means that circuits are static and cannot be trained or optimised directly. However, there exist hybrid classical-quantum techniques that can produce variational (or parametrised) circuits, which act as templates for circuit generation. Quantum machine learning algorithms take advantage of variational circuits to implement quantum-alternatives to many machine learning algorithms. In this way, quantum solutions can be both highly efficient and data rich.

Keywords: 

Quantum Computing, Quantum Machine Learning, Variational Quantum Algorithms, Python Programming, Qiskit or PennyLane.

Necessary Skills:

• Python, Data Science and have excellent knowledge of Maths. 

• Foundations of Quantum Computing

• Some experience with IBM Quantum Lab and Qiskit or Xanadu PennyLane

Readings:

• Cerezo, M., Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, et al. "Variational Quantum Algorithms." Nature Reviews Physics, August 12, 2021, 1-20.

• Bergholm, Ville, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, et al. “PennyLane: Automatic Differentiation of Hybrid Quantum-Classical Computations.” ArXiv:1811.04968 [Physics, Physics:Quant-Ph], February 13, 2020.

• Stęchły, Michał. “Variational Quantum Algorithms – How Do They Work?” Musty Thoughts (blog), December 2, 2020. https://www.mustythoughts.com/vqas-how-do-they-work.

Supervisor: Hon. Assoc. Prof. Jacob L. Cybulski, jacob.cybulski@deakin.edu.au

Project Description:

Quantum Machine Learning combines elements of Quantum Computing and Data Science. Many classical machine learning algorithms have been re-designed and re-implemented for use with quantum machines, often with very significant performance improvements. However, there have been very few attempts at taking advantage of quantum technology when developing time series analysis. One of the reasons for this situation is that quantum circuits have no memory. This project will explore a range of approaches to analysing time series using quantum techniques, some relying on stochastic methods, other using variational quantum algorithms. Previous applications of quantum time series analysis include financial, medical and environmental.

Keywords: 

Quantum Computing, Quantum Machine Learning, Time Series Analysis.

Necessary Skills:

• Data Science

• Maths and Statistics

• Familiarity with Time Series Analysis

• Fundamentals of Quantum Computing

Readings:

• Hyndman, Rob J., and George Athanasopoulos. Forecasting: Principles and Practice. OTexts, 2018. https://otexts.com/fpp2/. (Online)

• Zickert, Frank. Hands-On Quantum Machine Learning With Python. Leanpub, 2021.