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

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.

Xiao-Hui Ni, Yu-Sen Wu, Bin-Bin Cai, Wen-Min Li, Su-Juan Qin, Fei Gao "An Adaptive Mixer Allocation Algorithm for the Quantum Alternating Operator Ansatz" arXiv:2412.19621

Abstract

Recently, Hadfield et al. proposed the quantum alternating operator ansatz algorithm (QAOA+), an extension of the quantum approximate optimization algorithm (QAOA), to solve constrained combinatorial optimization problems (CCOPs). Compared with QAOA, QAOA+ enables the search for optimal solutions within a feasible solution space by encoding problem constraints into the mixer Hamiltonian, thus reducing the search space and eliminating the possibility of yielding infeasible solutions. However, QAOA+ may require substantial gates and circuit depth when the mixer is applied to all qubits in each layer, and the implementation cost of the mixer is high. To address these challenges, we propose the adaptive mixer allocation (AMA) algorithm. AMA constructs a feasible space by selectively applying the mixer to partial qubits in each layer based on the evolution of the optimization process. The performance of AMA in solving the maximum independent set (MIS) problem is investigated. Numerical simulation results demonstrate that, under the same number of optimization runs, AMA achieves a higher optimal approximation ratio-- () higher than QAOA+ on ER (3-regular) graphs. Additionally, AMA significantly reduces the resource consumption, achieving only () of QAOA+ circuit depth and () of the number of CNOT gates, respectively, on ER (3-regular) graphs. These results highlight the ability of AMA to enhance computational efficiency and solution quality, making it particularly valuable for resource-constrained quantum devices.

Yanqi Song, Jing Li, Yusen Wu, Sujuan Qin, Qiaoyan Wen, Fei Gao "A Resource-Efficient Quantum Convolutional Neural Network" Frontiers in Physics 12:1362690 (2024)

Abstract

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the classical Convolutional Neural Network (CNN) model, the QCNN model requires excellent numerical performance or efficient computational resources to showcase its potential quantum advantages, particularly in classical data processing tasks. In this paper, we propose a computationally resource-efficient QCNN model referred to as RE-QCNN. Specifically, through a comprehensive analysis of the complexity associated with the forward and backward propagation processes in the quantum convolutional layer, our results demonstrate a significant reduction in computational resources required for this layer compared to the classical CNN model. Furthermore, our model is numerically benchmarked on recognizing images from the MNIST and Fashion-MNIST datasets, achieving high accuracy in these multi-class classification tasks.

Yanqi Song, Yusen Wu, Shengyao Wu, Dandan Li, Qiaoyan Wen, Sujuan Qin, Fei Gao "A quantum federated learning framework for classical clients" Science China Physics, Mechanics & Astronomy

Abstract

Quantum Federated Learning (QFL) enables collaborative training of a Quantum Machine Learning (QML) model among multiple clients possessing quantum computing capabilities, without the need to share their respective local data. However, the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing capabilities. This raises a natural question: Can quantum computing capabilities be deployed on the server instead? In this paper, we propose a QFL framework specifically designed for classical clients, referred to as CCQFL, in response to this question. In each iteration, the collaborative training of the QML model is assisted by the shadow tomography technique, eliminating the need for quantum computing capabilities of clients. Specifically, the server constructs a classical representation of the QML model and transmits it to the clients. The clients encode their local data onto observables and use this classical representation to calculate local gradients. These local gradients are then utilized to update the parameters of the QML model. We evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST dataset. Our framework provides valuable insights into QFL, particularly in scenarios where quantum computing resources are scarce.

Yusen Wu, Yukun Zhang, Xiao Yuan "An efficient classical algorithm for simulating short time 2d quantum dynamics" arXiv preprint arXiv:2409.04161

Abstract

Efficient classical simulation of the Schrodinger equation is central to quantum mechanics, as it is crucial for exploring complex natural phenomena and understanding the fundamental distinctions between classical and quantum computation. Although simulating general quantum dynamics is BQP-complete, tensor networks allow efficient simulation of short-time evolution in 1D systems. However, extending these methods to higher dimensions becomes significantly challenging when the area law is violated. In this work, we tackle this challenge by introducing an efficient classical algorithm for simulating short-time dynamics in 2D quantum systems, utilizing cluster expansion and shallow quantum circuit simulation. Our algorithm has wide-ranging applications, including an efficient dequantization method for estimating quantum eigenvalues and eigenstates, simulating superconducting quantum computers, dequantizing quantum variational algorithms, and simulating constant-gap adiabatic quantum evolution. Our results reveal the inherent simplicity in the complexity of short-time 2D quantum dynamics and highlight the limitations of noisy intermediate-scale quantum hardware, particularly those confined to 2D topological structures. This work advances our understanding of the boundary between classical and quantum computation and the criteria for achieving quantum advantage.

Yukun Zhang, Yusen Wu, Xiao Yuan "A Dequantized Algorithm for the Guided Local Hamiltonian Problem" arXiv:2411.16163

Abstract

The local Hamiltonian (LH) problem, the quantum analog of the classical constraint satisfaction problem, is a cornerstone of quantum computation and complexity theory. It is known to be QMA-complete, indicating that it is challenging even for quantum computers. Interestingly, the guided local Hamiltonian (GLH) problem -- an LH problem with a guiding state that has a non-trivial overlap with the ground state -- can be efficiently solved on a quantum computer and is proved to be BQP-complete. This makes the GLH problem a valuable framework for exploring the fundamental separation between classical and quantum computation. Remarkably, the quantum algorithm for solving the GLH problem can be `dequantized' (i.e., made classically simulatable) under certain conditions, such as when only constant accuracy is required and when the Hamiltonian satisfies an unrealistic constant operator norm constraint. In this work, we relieve these restrictions by introducing a dequantized classical algorithm for a randomized quantum imaginary-time evolution quantum algorithm. We demonstrate that it achieves either limited or arbitrary constant accuracy, depending on whether the guiding state's overlap is general or exceeds a certain threshold. Crucially, our approach eliminates the constant operator norm constraint on the Hamiltonian, opening its applicability to realistic problems.Our results advance the classical solution of the GLH problem in practical settings and provide new insights into the boundary between classical and quantum computational power.

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

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

Xiao-Hui Ni, Yu-Sen Wu, Bin-Bin Cai, Wen-Min Li, Su-Juan Qin, Fei Gao "An Adaptive Mixer Allocation Algorithm for the Quantum Alternating Operator Ansatz" arXiv:2412.19621

Abstract

Yanqi Song, Jing Li, Yusen Wu, Sujuan Qin, Qiaoyan Wen, Fei Gao "A Resource-Efficient Quantum Convolutional Neural Network" Frontiers in Physics 12:1362690 (2024)

Abstract

Yanqi Song, Yusen Wu, Shengyao Wu, Dandan Li, Qiaoyan Wen, Sujuan Qin, Fei Gao "A quantum federated learning framework for classical clients" Science China Physics, Mechanics & Astronomy

Abstract

Yusen Wu, Yukun Zhang, Xiao Yuan "An efficient classical algorithm for simulating short time 2d quantum dynamics" arXiv preprint arXiv:2409.04161

Abstract

Yukun Zhang, Yusen Wu, Xiao Yuan "A Dequantized Algorithm for the Guided Local Hamiltonian Problem" arXiv:2411.16163

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

Xiao-Hui Ni, Yu-Sen Wu, Bin-Bin Cai, Wen-Min Li, Su-Juan Qin, Fei Gao "An Adaptive Mixer Allocation Algorithm for the Quantum Alternating Operator Ansatz" arXiv:2412.19621

Yanqi Song, Jing Li, Yusen Wu, Sujuan Qin, Qiaoyan Wen, Fei Gao "A Resource-Efficient Quantum Convolutional Neural Network" Frontiers in Physics 12:1362690 (2024)

Yanqi Song, Yusen Wu, Shengyao Wu, Dandan Li, Qiaoyan Wen, Sujuan Qin, Fei Gao "A quantum federated learning framework for classical clients" Science China Physics, Mechanics & Astronomy

Yusen Wu, Yukun Zhang, Xiao Yuan "An efficient classical algorithm for simulating short time 2d quantum dynamics" arXiv preprint arXiv:2409.04161

Yukun Zhang, Yusen Wu, Xiao Yuan "A Dequantized Algorithm for the Guided Local Hamiltonian Problem" arXiv:2411.16163

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

Xiao-Hui Ni, Yu-Sen Wu, Bin-Bin Cai, Wen-Min Li, Su-Juan Qin, Fei Gao "An Adaptive Mixer Allocation Algorithm for the Quantum Alternating Operator Ansatz" arXiv:2412.19621

Yanqi Song, Jing Li, Yusen Wu, Sujuan Qin, Qiaoyan Wen, Fei Gao "A Resource-Efficient Quantum Convolutional Neural Network" Frontiers in Physics 12:1362690 (2024)

Yanqi Song, Yusen Wu, Shengyao Wu, Dandan Li, Qiaoyan Wen, Sujuan Qin, Fei Gao "A quantum federated learning framework for classical clients" Science China Physics, Mechanics & Astronomy

Yusen Wu, Yukun Zhang, Xiao Yuan "An efficient classical algorithm for simulating short time 2d quantum dynamics" arXiv preprint arXiv:2409.04161

Yukun Zhang, Yusen Wu, Xiao Yuan "A Dequantized Algorithm for the Guided Local Hamiltonian Problem" arXiv:2411.16163

Introduction to category theory, zx-calculus, and their applications in quantum computing

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

RNA structure prediction

Introduction to category theory, zx-calculus, and their applications in quantum computing

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

RNA structure prediction

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