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

Contact: quisa2025

gmail.com

Publications

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng Experimental implementation of quantum-walk-based portfolio optimisation In: 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 2024, visited: 06.02.2024.

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.

Wu, Yusen; Huang, Zigeng; Sun, Jinzhao; Yuan, Xiao; Wang, Jingbo; Lv, Dingshun Orbital Expansion Variational Quantum Eigensolver In: Quantum Science and Technology (Impact Factor 6.568), vol. 8, pp. 045030, 2023.

Wu, Yusen; Wu, Bujiao; Wang, Jingbo; Yuan, Xiao Quantum Phase Recognition via Quantum Kernel Methods Journal Article In: Quantum (Impact Factor 6.777), vol. 7, pp. 981, 2023.

Abstract

Can we use a quantum computer to speed up classical machine learning in solving problems of practical significance? Here, we study this open question focusing on the quantum phase learning problem, an important task in many-body quantum physics. We prove that, under widely believed complexity theory assumptions, quantum phase learning problem cannot be efficiently solved by machine learning algorithms using classical resources and classical data. Whereas using quantum data, we theoretically prove the universality of quantum kernel Alphatron in efficiently predicting quantum phases, indicating quantum advantages in this learning problem. We numerically benchmark the algorithm for a variety of problems, including recognizing symmetry-protected topological phases and symmetry-broken phases. Our results highlight the capability of quantum machine learning in efficient prediction of quantum phases.

Sotelo, Rafael; Wang, Jingbo; Nakamura, Yuichi; Farouk, Ahmed; Arjona, Rosario; Andraca, Salvador Venegas; James, Alex; Venegas-Gomez, Araceli; Gonzalez, Bill The first Workshop on Quantum in Consumer Technology at IEEE Quantum Week Journal Article In: IEEE Consumer Electronics Magazine (Impact Factor 4.135), vol. 2162, pp. 2256, 2023.

Matwiejew, Edric; Pye, Jason; Wang, Jingbo B. Quantum Optimisation for Continuous Multivariable Functions by a Structured Search Journal Article In: Quantum Science and Technology (Impact Factor 6.568) - In Press (formally accepted) , vol. 8, pp. 045013, 2023.

Abstract

Solving optimisation problems is a promising near-term application of quantum computers. Quantum variational algorithms leverage quantum superposition and entanglement to optimise over exponentially large solution spaces using an alternating sequence of classically tunable unitaries. However, prior work has primarily addressed discrete optimisation problems. In addition, these algorithms have been designed generally under the assumption of an unstructured solution space, which constrains their speedup to the theoretical limits for the unstructured Grover's quantum search algorithm. In this paper, we show that quantum variational algorithms can efficiently optimise continuous multivariable functions by exploiting general structural properties of a discretised continuous solution space with a convergence that exceeds the limits of an unstructured quantum search. We introduce the Quantum Multivariable Optimisation Algorithm (QMOA) and demonstrate its advantage over pre-existing methods, particularly when optimising high-dimensional and oscillatory functions.

Bennett, Tavis; Wang, Jingbo Quantum optimisation via maximally amplified states Journal Article Forthcoming In: arXiv:2111.00796, Forthcoming.

Qu, Dengke; Marsh, Samuel; Wang, Kunkun; Xiao, Lei; Wang, Jingbo; Xue, Peng Deterministic Search on Star Graphs via Quantum Walks Journal Article In: Physical Review Letters (IF 9.161), vol. 128, iss. 5, pp. 050501, 2022.

Abstract

We propose a novel algorithm for quantum spatial search on a star graph using interleaved continuous-time quantum walks and marking oracle queries. Initializing the system in the star’s central vertex, we determine the optimal quantum walk times to reach full overlap with the marked state using ⌈(π/4)√N−(1/2)⌉ oracle queries, matching the well-known lower bound of Grover’s search. We implement the deterministic search in a database of size seven on photonic quantum hardware, and demonstrate the effective scaling of the approach up to size 115. This is the first experimental demonstration of quantum walk-based search on the highly noise-resistant star graph, which provides new evidence for the applications of quantum walk in quantum algorithms and quantum information processing.

Wu, Yusen; Wang, Jingbo Estimating Gibbs partition function with quantum Clifford sampling Journal Article In: Quantum Science and Technology (IF 6.568) , vol. 7, pp. 025006, 2022.

Abstract

The partition function is an essential quantity in statistical mechanics, and its accurate computation is a key component of any statistical analysis of quantum systems and phenomena. However, for interacting many-body quantum systems, its calculation generally involves summing over an exponential number of terms and can thus quickly grow to be intractable. Accurately and efficiently estimating the partition function of its corresponding system Hamiltonian then becomes the key in solving quantum many-body problems. In this paper we develop a hybrid quantum-classical algorithm to estimate the partition function, utilizing a novel Clifford sampling technique. Note that previous works on the estimation of partition functions require $mathcal{O}(1/epsilonsqrt{Delta})$-depth quantum circuits, where $Delta$ is the minimum spectral gap of stochastic matrices and $epsilon$ is the multiplicative error. Our algorithm requires only a shallow $mathcal{O}(1)$-depth quantum circuit, repeated $mathcal{O}(n/epsilon^2)$ times, to provide a comparable $epsilon$ approximation. Shallow-depth quantum circuits are considered vitally important for currently available NISQ (Noisy Intermediate-Scale Quantum) devices.

Matwiejew, Edric; Wang, Jingbo QuOp_MPI: A framework for parallel simulation of quantum variational algorithms Journal Article In: Journal of Computational Science (IF 3.817), vol. 62, pp. 101711, 2022.

Publications

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng Experimental implementation of quantum-walk-based portfolio optimisation In: 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 2024, visited: 06.02.2024.

Abstract

Wu, Yusen; Huang, Zigeng; Sun, Jinzhao; Yuan, Xiao; Wang, Jingbo; Lv, Dingshun Orbital Expansion Variational Quantum Eigensolver In: Quantum Science and Technology (Impact Factor 6.568), vol. 8, pp. 045030, 2023.

Wu, Yusen; Wu, Bujiao; Wang, Jingbo; Yuan, Xiao Quantum Phase Recognition via Quantum Kernel Methods Journal Article In: Quantum (Impact Factor 6.777), vol. 7, pp. 981, 2023.

Abstract

Sotelo, Rafael; Wang, Jingbo; Nakamura, Yuichi; Farouk, Ahmed; Arjona, Rosario; Andraca, Salvador Venegas; James, Alex; Venegas-Gomez, Araceli; Gonzalez, Bill The first Workshop on Quantum in Consumer Technology at IEEE Quantum Week Journal Article In: IEEE Consumer Electronics Magazine (Impact Factor 4.135), vol. 2162, pp. 2256, 2023.

Matwiejew, Edric; Pye, Jason; Wang, Jingbo B. Quantum Optimisation for Continuous Multivariable Functions by a Structured Search Journal Article In: Quantum Science and Technology (Impact Factor 6.568) - In Press (formally accepted) , vol. 8, pp. 045013, 2023.

Abstract

Bennett, Tavis; Wang, Jingbo Quantum optimisation via maximally amplified states Journal Article Forthcoming In: arXiv:2111.00796, Forthcoming.

Qu, Dengke; Marsh, Samuel; Wang, Kunkun; Xiao, Lei; Wang, Jingbo; Xue, Peng Deterministic Search on Star Graphs via Quantum Walks Journal Article In: Physical Review Letters (IF 9.161), vol. 128, iss. 5, pp. 050501, 2022.

Abstract

Wu, Yusen; Wang, Jingbo Estimating Gibbs partition function with quantum Clifford sampling Journal Article In: Quantum Science and Technology (IF 6.568) , vol. 7, pp. 025006, 2022.

Abstract

Matwiejew, Edric; Wang, Jingbo QuOp_MPI: A framework for parallel simulation of quantum variational algorithms Journal Article In: Journal of Computational Science (IF 3.817), vol. 62, pp. 101711, 2022.

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng Experimental implementation of quantum-walk-based portfolio optimisation In: 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 2024, visited: 06.02.2024.

Wu, Yusen; Huang, Zigeng; Sun, Jinzhao; Yuan, Xiao; Wang, Jingbo; Lv, Dingshun Orbital Expansion Variational Quantum Eigensolver In: Quantum Science and Technology (Impact Factor 6.568), vol. 8, pp. 045030, 2023.

Wu, Yusen; Wu, Bujiao; Wang, Jingbo; Yuan, Xiao Quantum Phase Recognition via Quantum Kernel Methods Journal Article In: Quantum (Impact Factor 6.777), vol. 7, pp. 981, 2023.

Matwiejew, Edric; Pye, Jason; Wang, Jingbo B. Quantum Optimisation for Continuous Multivariable Functions by a Structured Search Journal Article In: Quantum Science and Technology (Impact Factor 6.568) - In Press (formally accepted) , vol. 8, pp. 045013, 2023.

Bennett, Tavis; Wang, Jingbo Quantum optimisation via maximally amplified states Journal Article Forthcoming In: arXiv:2111.00796, Forthcoming.

Qu, Dengke; Marsh, Samuel; Wang, Kunkun; Xiao, Lei; Wang, Jingbo; Xue, Peng Deterministic Search on Star Graphs via Quantum Walks Journal Article In: Physical Review Letters (IF 9.161), vol. 128, iss. 5, pp. 050501, 2022.

Wu, Yusen; Wang, Jingbo Estimating Gibbs partition function with quantum Clifford sampling Journal Article In: Quantum Science and Technology (IF 6.568) , vol. 7, pp. 025006, 2022.

Matwiejew, Edric; Wang, Jingbo QuOp_MPI: A framework for parallel simulation of quantum variational algorithms Journal Article In: Journal of Computational Science (IF 3.817), vol. 62, pp. 101711, 2022.

Qu, Dengke; Matwiejew, Edric; Wang, Kunkun; Wang, Jingbo; Xue, Peng Experimental implementation of quantum-walk-based portfolio optimisation In: 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 2024, visited: 06.02.2024.

Wu, Yusen; Huang, Zigeng; Sun, Jinzhao; Yuan, Xiao; Wang, Jingbo; Lv, Dingshun Orbital Expansion Variational Quantum Eigensolver In: Quantum Science and Technology (Impact Factor 6.568), vol. 8, pp. 045030, 2023.

Wu, Yusen; Wu, Bujiao; Wang, Jingbo; Yuan, Xiao Quantum Phase Recognition via Quantum Kernel Methods Journal Article In: Quantum (Impact Factor 6.777), vol. 7, pp. 981, 2023.

Matwiejew, Edric; Pye, Jason; Wang, Jingbo B. Quantum Optimisation for Continuous Multivariable Functions by a Structured Search Journal Article In: Quantum Science and Technology (Impact Factor 6.568) - In Press (formally accepted) , vol. 8, pp. 045013, 2023.

Bennett, Tavis; Wang, Jingbo Quantum optimisation via maximally amplified states Journal Article Forthcoming In: arXiv:2111.00796, Forthcoming.

Qu, Dengke; Marsh, Samuel; Wang, Kunkun; Xiao, Lei; Wang, Jingbo; Xue, Peng Deterministic Search on Star Graphs via Quantum Walks Journal Article In: Physical Review Letters (IF 9.161), vol. 128, iss. 5, pp. 050501, 2022.

Wu, Yusen; Wang, Jingbo Estimating Gibbs partition function with quantum Clifford sampling Journal Article In: Quantum Science and Technology (IF 6.568) , vol. 7, pp. 025006, 2022.

Matwiejew, Edric; Wang, Jingbo QuOp_MPI: A framework for parallel simulation of quantum variational algorithms Journal Article In: Journal of Computational Science (IF 3.817), vol. 62, pp. 101711, 2022.

CSIRO On-Prime program for Research Translation

Exploring Barren Plateau Phenomenon in Quantum Data Re-Uploading

Quantum Phase Recognition via Quantum Kernel Methods

Learning out-of-time-ordered correlators with quantum kernel methods

Screening of molecules with quantum enhanced feature space

Quantum Phase Recognition via Quantum Convolutional Neural Networks

Quantum State Amplitude Preparation for Stochastic Modelling

CSIRO On-Prime program for Research Translation

Exploring Barren Plateau Phenomenon in Quantum Data Re-Uploading

Quantum Phase Recognition via Quantum Kernel Methods

Learning out-of-time-ordered correlators with quantum kernel methods

Screening of molecules with quantum enhanced feature space

Quantum Phase Recognition via Quantum Convolutional Neural Networks

Quantum State Amplitude Preparation for Stochastic Modelling

GALLERY

GALLERY

roblems and complex phenomena.