Yusen Wu, Bujiao Wu, Yanqi Song, Xiao Yuan, Jingbo Wang
Learning the complexity of weakly noisy quantum states
In: International Conference on Learning Representations 2025
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
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.
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.
Exploring Barren Plateau Phenomenon in Quantum Data Re-Uploading
Matthaus Zering
1
DEC
Quantum Phase Recognition via Quantum Kernel Methods
Yusen Wu
1
DEC
Learning out-of-time-ordered correlators with quantum kernel methods
John Tanner
28
NOV
Screening of molecules with quantum enhanced feature space
Shengxin Zhuang
GALLERY
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QUISA group photo in 2015
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QUISA group photo in 2013
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The Research Centre for Quantum Information, Simulation and Algorithms (QUISA), hosted at The University of Western Australia, fosters 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.
