The QUISA (Quantum Information, Simulation and Algorithms) Research Centre, hosted at The University of Western Australia, aims to foster collaboration and entrepreneurship, bringing together academic staff, research students, government and industrial partners to develop innovative quantum solutions to tackle otherwise intractable problems and complex phenomena.

Centre for Quantum Information, Simulation and Algorithms

TEAM

RESEARCH

Recent Publications

Yusen Wu, Bujiao Wu, Yanqi Song, Xiao Yuan, Jingbo Wang "Learning the complexity of weakly noisy quantum states", ICLR 2025 – International Conference on Learning Representations (31.75% acceptance rate)

Abstract

Quantifying the complexity of quantum states is a longstanding key problem in various subfields of science, ranging from quantum computing to the black-hole theory. The lower bound on quantum pure state complexity has been shown to grow linearly with system size (Haferkamp et al., 2022). However, extending this result to noisy circuit environments, which better reflect real quantum devices, re- mains an open challenge. In this paper, we explore the complexity of weakly noisy quantum states via the quantum learning method. We present an efficient learning algorithm, that leverages the classical shadow representation of target quantum states, to predict the circuit complexity of weakly noisy quantum states. Our al- gorithm is proved to be optimal in terms of sample complexity accompanied with polynomial classical processing time. Our result builds a bridge between the learn- ing algorithm and quantum state complexity, meanwhile highlighting the power of learning algorithm in characterizing intrinsic properties of quantum states.

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng "Experimental implementation of quantum-walk-based portfolio optimisation", Quantum Science and Technology (IF 6.568) , vol. 9, pp. 025014, 2024.

Abstract

The application of quantum algorithms has attracted much attention as it holds the promise of solving practical problems that are intractable to classical algorithms. One such application is the recent development of a quantum-walk-based optimization algorithm approach to portfolio optimization under the modern portfolio theory framework. In this paper, we demonstrate an experimental realization of the alternating phase-shift and continuous-time quantum walk unitaries that underpin this quantum algorithm using optical networks and single photons. The experimental analysis confirms that the probability of states corresponding to high-quality solutions is efficiently amplified by increasing the number of phase-shift and quantum walk iterations. This work provides strong evidence for practical applications of quantum-walk-based algorithms such as financial portfolio optimization.

Zhuang, Shengxin; Tanner, John; Wu, Yusen; Huynh, Du Q.; Cadet, Wei Liu Xavier F.; Fontaine, Nicolas; Charton, Philippe; Damour, Cedric; Cadet, Frederic; Wang, Jingbo "Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine", Quantum Information Processing 23 (11), 379, 2024

Abstract

Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world applications of QML remains active. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets, we apply and contrast the performance of the QSVM, numerous classical SVMs, and the best published results on the same peptide classification task, out of which the QSVM performs best. The contributions of this work include (i) the first application of the QSVM to this specific peptide classification task, (ii) an explicit demonstration of QSVMs outperforming the best published results attained with classical machine learning models on this classification task and (iii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification task. This foundational work paves the way to verifiable quantum advantages in the field of computational biology and facilitates safer therapeutic development.

Sotelo, R., Giusto, E., Nakamura, Y., & Wang, J. "The 2nd Workshop on Quantum in Consumer Technology At IEEE Quantum Week" IEEE Consumer Electronics Magazine, 13(5), 1-2, 2024; https://doi.org/10.1109/MCE.2024.3407740

Abstract

The 2nd Workshop on Quantum in Consumer Technology, held during the IEEE Quantum Week 2023, continued to explore the integration and application of quantum technologies in consumer electronics. Organized by the Quantum in Consumer Technology Technical Committee (QCT TC) of the IEEE Consumer Technology Society (CTSoc), this year's workshop featured two insightful panels discussing the cutting-edge advancements and applications of quantum technologies that aim to revolutionize consumer products and services.

T Bennett, L Noakes, JB Wang "Analysis of the non-variational quantum walk-based optimisation algorithm" arXiv preprint arXiv:2408.06368

Abstract

This paper introduces in detail a non-variational quantum algorithm designed to solve a wide range of combinatorial optimisation problems, including constrained problems and problems with non-binary variables. The algorithm returns optimal and near-optimal solutions from repeated preparation and measurement of an amplified state. The amplified state is prepared via repeated application of two unitaries; one which phase-shifts solution states dependent on objective function values, and the other which mixes phase-shifted probability amplitudes via a continuous-time quantum walk (CTQW) on a problem-specific mixing graph. The general interference process responsible for amplifying optimal solutions is derived in part from statistical analysis of objective function values as distributed over the mixing graph. The algorithm's versatility is demonstrated through its application to various problems: weighted maxcut, k-means clustering, quadratic assignment, maximum independent set and capacitated facility location. In all cases, efficient circuit implementations of the CTQWs are discussed. A penalty function approach for constrained problems is also introduced, including a method for optimising the penalty function. For each of the considered problems, the algorithm's performance is simulated for a randomly generated problem instance, and in each case, the amplified state produces a globally optimal solution within a small number of iterations.

Yusen Wu, Bujiao Wu, Yanqi Song, Xiao Yuan, Jingbo Wang "Learning the complexity of weakly noisy quantum states", ICLR 2025 – International Conference on Learning Representations (31.75% acceptance rate)

Abstract

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng "Experimental implementation of quantum-walk-based portfolio optimisation", Quantum Science and Technology (IF 6.568) , vol. 9, pp. 025014, 2024.

Abstract

Zhuang, Shengxin; Tanner, John; Wu, Yusen; Huynh, Du Q.; Cadet, Wei Liu Xavier F.; Fontaine, Nicolas; Charton, Philippe; Damour, Cedric; Cadet, Frederic; Wang, Jingbo "Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine", Quantum Information Processing 23 (11), 379, 2024

Abstract

Sotelo, R., Giusto, E., Nakamura, Y., & Wang, J. "The 2nd Workshop on Quantum in Consumer Technology At IEEE Quantum Week" IEEE Consumer Electronics Magazine, 13(5), 1-2, 2024; https://doi.org/10.1109/MCE.2024.3407740

Abstract

T Bennett, L Noakes, JB Wang "Analysis of the non-variational quantum walk-based optimisation algorithm" arXiv preprint arXiv:2408.06368

Abstract

Yusen Wu, Bujiao Wu, Yanqi Song, Xiao Yuan, Jingbo Wang "Learning the complexity of weakly noisy quantum states", ICLR 2025 – International Conference on Learning Representations (31.75% acceptance rate)

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng "Experimental implementation of quantum-walk-based portfolio optimisation", Quantum Science and Technology (IF 6.568) , vol. 9, pp. 025014, 2024.

Zhuang, Shengxin; Tanner, John; Wu, Yusen; Huynh, Du Q.; Cadet, Wei Liu Xavier F.; Fontaine, Nicolas; Charton, Philippe; Damour, Cedric; Cadet, Frederic; Wang, Jingbo "Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine", Quantum Information Processing 23 (11), 379, 2024

Sotelo, R., Giusto, E., Nakamura, Y., & Wang, J. "The 2nd Workshop on Quantum in Consumer Technology At IEEE Quantum Week" IEEE Consumer Electronics Magazine, 13(5), 1-2, 2024; https://doi.org/10.1109/MCE.2024.3407740

T Bennett, L Noakes, JB Wang "Analysis of the non-variational quantum walk-based optimisation algorithm" arXiv preprint arXiv:2408.06368

Yusen Wu, Bujiao Wu, Yanqi Song, Xiao Yuan, Jingbo Wang "Learning the complexity of weakly noisy quantum states", ICLR 2025 – International Conference on Learning Representations (31.75% acceptance rate)

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng "Experimental implementation of quantum-walk-based portfolio optimisation", Quantum Science and Technology (IF 6.568) , vol. 9, pp. 025014, 2024.

Zhuang, Shengxin; Tanner, John; Wu, Yusen; Huynh, Du Q.; Cadet, Wei Liu Xavier F.; Fontaine, Nicolas; Charton, Philippe; Damour, Cedric; Cadet, Frederic; Wang, Jingbo "Non-Hemolytic Peptide Classification Using A Quantum Support Vector Machine", Quantum Information Processing 23 (11), 379, 2024

Sotelo, R., Giusto, E., Nakamura, Y., & Wang, J. "The 2nd Workshop on Quantum in Consumer Technology At IEEE Quantum Week" IEEE Consumer Electronics Magazine, 13(5), 1-2, 2024; https://doi.org/10.1109/MCE.2024.3407740

T Bennett, L Noakes, JB Wang "Analysis of the non-variational quantum walk-based optimisation algorithm" arXiv preprint arXiv:2408.06368

Quantum Games Final Presentation

Formally verify high-level specifications for neural networks

Protein fold and protein folding: from 1D sequence to 3D structure

Quantum Games Workshop (Nov 18 to Dec 19, 2024)

RNA design problems and beyond

GALLERY