Background:Diabetic retinopathy remains one of the leading causes of vision impairment globally and poses diagnostic challenges due to the complexity of clinical imaging data and variability in disease progression.In ...Background:Diabetic retinopathy remains one of the leading causes of vision impairment globally and poses diagnostic challenges due to the complexity of clinical imaging data and variability in disease progression.In this study,we propose an innovative methodology that integrates artificial intelligence and quantum computing to enhance the early detection and clinical management of diabetic retinopathy.Methods:We developed a hybrid model combining machine learning algorithms with simulated quantum circuits to classify retinal images and associated clinical data.Anonymized datasets were used,and deep inductive transfer techniques were applied to improve diagnostic precision and generalizability.Results:The proposed model achieved a classification accuracy of 94.6%,significantly reducing diagnostic time and improving the prioritization of high-risk cases compared to conventional methods.The hybrid approach demonstrated superior performance in processing speed and accuracy for complex clinical scenarios.Conclusion:This study highlights the potential of combining AI and quantum computing to revolutionize the diagnosis of diabetic retinopathy.The proposed model provides a scalable and efficient solution for clinical environments,enabling faster and more accurate decision-making in ophthalmic care.展开更多
Hydrogen evolution reaction(HER)in acidic media has been spotlighted for hydrogen production since it is a favourable kinetics with the supplied protons from a counterpart compared to that within alkaline environment....Hydrogen evolution reaction(HER)in acidic media has been spotlighted for hydrogen production since it is a favourable kinetics with the supplied protons from a counterpart compared to that within alkaline environment.However,there is no choice but to use a platinum-based catalyst yet.As for a noble metal-free electrocatalyst,incorporation of earth-abundant transition metal(TM)atoms into nanocarbon platforms has been extensively adopted.Although a data-driven methodology facilitates the rational design of TM-anchored carbon catalysts,its practical application suffers from either a simplified theoretical model or the prohibitive cost and complexity of experimental data generation.Herein,an effective and facile catalyst design strategy is proposed based on machine learning(ML)and its model verification using electrochemical methods accompanied by density functional theory simulations.Based on a Bayesian genetic algorithm ML model,the Ni-incorporated carbon quantum dots(Ni@CQD)loaded on a three-dimensional reduced graphene oxide conductor are proposed as the best HER catalyst amongst the various TM-incorporated CQDs under the optimal conditions of catalyst loading,electrode type,and temperature and pH of electrolyte.The ML results are validated with electrochemical experiments,where the Ni@CQD catalyst exhibited superior HER activity,requiring an overpotential of 151 mV to achieve 10 mAcm^(−2) with a Tafel slope of 52 mV dec^(−1) and impressive durability in acidic media up to 100 h.This methodology can provide an effective route for the rational design of highly active electrocatalysts for commercial applications.展开更多
Quantifying entanglement measures for quantum states with unknown density matrices is a challenging task.Machine learning offers a new perspective to address this problem.By training machine learning models using expe...Quantifying entanglement measures for quantum states with unknown density matrices is a challenging task.Machine learning offers a new perspective to address this problem.By training machine learning models using experimentally measurable data,we can predict the target entanglement measures.In this study,we compare various machine learning models and find that the linear regression and stack models perform better than others.We investigate the model's impact on quantum states across different dimensions and find that higher-dimensional quantum states yield better results.Additionally,we investigate which measurable data has better predictive power for target entanglement measures.Using correlation analysis and principal component analysis,we demonstrate that quantum moments exhibit a stronger correlation with coherent information among these data features.展开更多
Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tac...Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.展开更多
Neural networks are becoming ubiquitous in various areas of physics as a successful machine learning(ML)technique for addressing different tasks.Based on ML technique,we propose and experimentally demonstrate an effic...Neural networks are becoming ubiquitous in various areas of physics as a successful machine learning(ML)technique for addressing different tasks.Based on ML technique,we propose and experimentally demonstrate an efficient method for state reconstruction of the widely used Sagnac polarization-entangled photon source.By properly modeling the target states,a multi-output fully connected neural network is well trained using only six of the sixteen measurement bases in standard tomography technique,and hence our method reduces the resource consumption without loss of accuracy.We demonstrate the ability of the neural network to predict state parameters with a high precision by using both simulated and experimental data.Explicitly,the mean absolute error for all the parameters is below 0.05 for the simulated data and a mean fidelity of 0.99 is achieved for experimentally generated states.Our method could be generalized to estimate other kinds of states,as well as other quantum information tasks.展开更多
In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,m...In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,machine learning(ML)algorithm has been migrated to this field to simplify calculations and improve fidelity.This review introduces the basic information on universal electron structures of emerging energy materials and ML algorithms involved in the prediction of material properties.Then,the structure-property relationships based on ML algorithm and QC theory are reviewed.Especially,the summary of recently reported applications on classifying crystal structure,modeling electronic structure,optimizing experimental method,and predicting performance is provided.Last,an outlook on ML assisted QC calculation towards identifying emerging energy materials is also presented.展开更多
Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be...Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.展开更多
Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid appr...Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.展开更多
Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles.The inherent parallelism and exponential computational powe...Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles.The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems,which are pervasive in machine learning.Quantum Support Vector Machine(QSVM)is a quantum machine learning algorithm inspired by classical Support Vector Machine(SVM)that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces.We provide a comprehensive overview of the underlying principles of QSVM,elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks.Through a comparative analysis,we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality.As a case study,we explored the potential of quantum paradigms in the context of a real-world problem:classifying pancreatic cancer biomarker data.The Support Vector Classifier(SVC)algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework.The classical approach as well as the quantum-based techniques reported similar accuracy.This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset.Remarkably,quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency.This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.展开更多
Nowadays,quantum machine learning is attracting great interest in a wide range offields due to its potential superior performance and capabilities.The massive increase in computational capacity and speed of quantum com...Nowadays,quantum machine learning is attracting great interest in a wide range offields due to its potential superior performance and capabilities.The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcarefield.Heart disease seriously threa-tens human health since it is the leading cause of death worldwide.Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis.In this study,an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease.The proposed model is a bagging ensemble learning model where a quantum support vector classifier was used as a base classifier.Further-more,in order to make the model’s outcomes more explainable,the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations(SHAP)framework.In the experimental study,other stand-alone quantum classifiers,namely,Quantum Support Vector Classifier(QSVC),Quantum Neural Network(QNN),and Variational Quantum Classifier(VQC)are applied and compared with classical machine learning classifiers such as Sup-port Vector Machine(SVM),and Artificial Neural Network(ANN).The experi-mental results on the Cleveland dataset reveal the superiority of QSVC compared to the others,which explains its use in the proposed bagging model.The Bagging-QSVC model outperforms all aforementioned classifiers with an accuracy of 90.16%while showing great competitiveness compared to some state-of-the-art models using the same dataset.The results of the study indicate that quantum machine learning classifiers perform better than classical machine learning classi-fiers in predicting heart disease.In addition,the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classifiers.展开更多
Quantum machine learning(QML)is a rapidly rising research eld that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientic research and improving data processing.How to ef...Quantum machine learning(QML)is a rapidly rising research eld that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientic research and improving data processing.How to efciently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing.It can be described as learning or approximating a unitary operator.Since the success of the hybrid-based quantum machine learning model proposed in recent years,we investigate to apply the techniques from QML to tackle this problem.Based on the Choi–Jamiołkowski isomorphism in quantum computing,we transfer the original problem of learning a unitary operator to a min–max optimization problem which can also be viewed as a quantum generative adversarial network.Besides,we select the spectral norm between the target and generated unitary operators as the regularization term in the loss function.Inspired by the hybrid quantum-classical framework widely used in quantum machine learning,we employ the variational quantum circuit and gradient descent based optimizers to solve the min-max optimization problem.In our numerical experiments,the results imply that our proposed method can successfully approximate the desired unitary operator and dramatically reduce the number of quantum gates of the traditional approach.The average delity between the states that are produced by applying target and generated unitary on random input states is around 0.997.展开更多
Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some fo...Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some forecasts based on the given data. Classical machine learning has its quantum part, which is known as quantum machine learning (QML). QML, which is a field of quantum computing, uses some of the quantum mechanical principles and concepts which include superposition, entanglement and quantum adiabatic theorem to assess the data and make some forecasts based on the data. At the present moment, research in QML has taken two main approaches. The first approach involves implementing the computationally expensive subroutines of classical machine learning algorithms on a quantum computer. The second approach concerns using classical machine learning algorithms on a quantum information, to speed up performance of the algorithms. The work presented in this manuscript proposes a quantum support vector algorithm that can be used to forecast solar irradiation. The novelty of this work is in using quantum mechanical principles for application in machine learning. Python programming language was used to simulate the performance of the proposed algorithm on a classical computer. Simulation results that were obtained show the usefulness of this algorithm for predicting solar irradiation.展开更多
Classification of quantum phases is one of the most important areas of research in condensed matter physics.In this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning.Fi...Classification of quantum phases is one of the most important areas of research in condensed matter physics.In this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning.Firstly,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave model.The unsupervised learning results match well with those obtained through traditional numerical diagonalization.Finally,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.展开更多
Neural networks possess formidable representational power,rendering them invaluable in solving complex quantum many-body systems.While they excel at analyzing static solutions,nonequilibrium processes,including critic...Neural networks possess formidable representational power,rendering them invaluable in solving complex quantum many-body systems.While they excel at analyzing static solutions,nonequilibrium processes,including critical dynamics during a quantum phase transition,pose a greater challenge for neural networks.To address this,we utilize neural networks and machine learning algorithms to investigate time evolutions,universal statistics,and correlations of topological defects in a one-dimensional transverse-field quantum Ising model.Specifically,our analysis involves computing the energy of the system during a quantum phase transition following a linear quench of the transverse magnetic field strength.The excitation energies satisfy a power-law relation to the quench rate,indicating a proportional relationship between the excitation energy and the kink numbers.Moreover,we establish a universal power-law relationship between the first three cumulants of the kink numbers and the quench rate,indicating a binomial distribution of the kinks.Finally,the normalized kink-kink correlations are also investigated and it is found that the numerical values are consistent with the analytic formula.展开更多
Machine learning has become a premier tool in physics and other fields of science.It has been shown that the quantum mechanical scattering problem cannot only be solved with such techniques,but it was argued that the ...Machine learning has become a premier tool in physics and other fields of science.It has been shown that the quantum mechanical scattering problem cannot only be solved with such techniques,but it was argued that the underlying neural network develops the Bom series for shallow potentials.However,classical machine learning algorithms fail in the unitary limit of an infinite scattering length.The unitary limit plays an important role in our understanding of bound strongly interacting fermionic systems and can be realized in cold atom experiments.Here,we develop a formalism that explains the unitary limit in terms of what we define as unitary limit surfaces.This not only allows to investigate the unitary limit geometrically in potential space,but also provides a numerically simple approach towards unnaturally large scattering lengths with standard multilayer perceptrons.Its scope is therefore not limited to applications in nuclear and atomic physics,but includes all systems that exhibit an unnaturally large scale.展开更多
Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body.Previously,researchers have developed machine learning algorithms in breast...Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body.Previously,researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine,K-Nearest Neighbor,Convolutional Neural Network,K-means,Fuzzy C-means,Neural Network,Principle Component Analysis(PCA)and Naive Bayes.Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities.For instance,support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage.However this scheme is expensive in terms of execution time.On its part,k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points.However this scheme does not guarantee high accuracy when executed in different iterations.Although the K-nearest Neighbor algorithm employs feature reduction,principle component analysis and 10 fold cross validation methods for enhancing classification accuracy,it is not efficient in terms of processing time.On the other hand,fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets.However,it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved.Similarly,convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining.In addition,the neural network achieves low accuracy in its predictions.Since all these algorithms seem to be expensive and time consuming,it necessary to integrate quantum computing principles with conventional machine learning algorithms.This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs.In this paper,a review of the current machine learning algorithms for breast cancer prediction is provided.Based on the observed shortcomings,a quantum machine learning based classifier is recommended.The proposed working mechanisms of this classifier are elaborated towards the end of this paper.展开更多
We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in cl...We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.展开更多
Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering.In this paper,we use the method of training the lower ...Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering.In this paper,we use the method of training the lower bound of the average log likelihood function on the quantum Boltzmann machine(QBM)to recognize the handwritten number datasets and compare the training results with classical models.We find that,when the QBM is semi-restricted,the training results get better with fewer computing resources.This shows that it is necessary to design a targeted algorithm to speed up computation and save resources.展开更多
Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical c...Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical counterparts.Semi-supervised learning is a machine learning technique that makes use of both labeled and unlabeled data for training,which enables a good classifier with only a small amount of labeled data.In this paper,we propose and theoretically analyze a graph-based semi-supervised learning method with the aid of the quantum annealing technique,which efficiently utilizes the quantum resources while maintaining good accuracy.We illustrate two classification examples,suggesting the feasibility of this method even with a small portion(30%) of labeled data involved.展开更多
Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning:...Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.展开更多
文摘Background:Diabetic retinopathy remains one of the leading causes of vision impairment globally and poses diagnostic challenges due to the complexity of clinical imaging data and variability in disease progression.In this study,we propose an innovative methodology that integrates artificial intelligence and quantum computing to enhance the early detection and clinical management of diabetic retinopathy.Methods:We developed a hybrid model combining machine learning algorithms with simulated quantum circuits to classify retinal images and associated clinical data.Anonymized datasets were used,and deep inductive transfer techniques were applied to improve diagnostic precision and generalizability.Results:The proposed model achieved a classification accuracy of 94.6%,significantly reducing diagnostic time and improving the prioritization of high-risk cases compared to conventional methods.The hybrid approach demonstrated superior performance in processing speed and accuracy for complex clinical scenarios.Conclusion:This study highlights the potential of combining AI and quantum computing to revolutionize the diagnosis of diabetic retinopathy.The proposed model provides a scalable and efficient solution for clinical environments,enabling faster and more accurate decision-making in ophthalmic care.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(NRF-2022R1A2C1011559,No.RS-2024-00405818 and NRF-2021M3H4A6A01045764)by the National Supercomputing Center with supercomputing resources including technical support(KSC-2024-CRE-0196)by Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Science and ICT(No.RS-2024-00404712).
文摘Hydrogen evolution reaction(HER)in acidic media has been spotlighted for hydrogen production since it is a favourable kinetics with the supplied protons from a counterpart compared to that within alkaline environment.However,there is no choice but to use a platinum-based catalyst yet.As for a noble metal-free electrocatalyst,incorporation of earth-abundant transition metal(TM)atoms into nanocarbon platforms has been extensively adopted.Although a data-driven methodology facilitates the rational design of TM-anchored carbon catalysts,its practical application suffers from either a simplified theoretical model or the prohibitive cost and complexity of experimental data generation.Herein,an effective and facile catalyst design strategy is proposed based on machine learning(ML)and its model verification using electrochemical methods accompanied by density functional theory simulations.Based on a Bayesian genetic algorithm ML model,the Ni-incorporated carbon quantum dots(Ni@CQD)loaded on a three-dimensional reduced graphene oxide conductor are proposed as the best HER catalyst amongst the various TM-incorporated CQDs under the optimal conditions of catalyst loading,electrode type,and temperature and pH of electrolyte.The ML results are validated with electrochemical experiments,where the Ni@CQD catalyst exhibited superior HER activity,requiring an overpotential of 151 mV to achieve 10 mAcm^(−2) with a Tafel slope of 52 mV dec^(−1) and impressive durability in acidic media up to 100 h.This methodology can provide an effective route for the rational design of highly active electrocatalysts for commercial applications.
基金supported by the NNSF of China(Grant No.11871089).
文摘Quantifying entanglement measures for quantum states with unknown density matrices is a challenging task.Machine learning offers a new perspective to address this problem.By training machine learning models using experimentally measurable data,we can predict the target entanglement measures.In this study,we compare various machine learning models and find that the linear regression and stack models perform better than others.We investigate the model's impact on quantum states across different dimensions and find that higher-dimensional quantum states yield better results.Additionally,we investigate which measurable data has better predictive power for target entanglement measures.Using correlation analysis and principal component analysis,we demonstrate that quantum moments exhibit a stronger correlation with coherent information among these data features.
基金the National Natural Science Foun-dation of China(Grant Nos.12105090 and 12175057).
文摘Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.
基金Project supported by the National Key Research and Development Program of China (Grant No.2019YFA0705000)Leading-edge technology Program of Jiangsu Natural Science Foundation (Grant No.BK20192001)the National Natural Science Foundation of China (Grant No.11974178)。
文摘Neural networks are becoming ubiquitous in various areas of physics as a successful machine learning(ML)technique for addressing different tasks.Based on ML technique,we propose and experimentally demonstrate an efficient method for state reconstruction of the widely used Sagnac polarization-entangled photon source.By properly modeling the target states,a multi-output fully connected neural network is well trained using only six of the sixteen measurement bases in standard tomography technique,and hence our method reduces the resource consumption without loss of accuracy.We demonstrate the ability of the neural network to predict state parameters with a high precision by using both simulated and experimental data.Explicitly,the mean absolute error for all the parameters is below 0.05 for the simulated data and a mean fidelity of 0.99 is achieved for experimentally generated states.Our method could be generalized to estimate other kinds of states,as well as other quantum information tasks.
基金supported by the National Natural Science Foundation of China(grant number 51872157)Shenzhen Technical Plan Project(grant number KQJSCX20160226191136 and JCYJ20170412170911187)Research Grants Council of the Hong Kong Special Administrative Region,China[grant number PF17-10186]。
文摘In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,machine learning(ML)algorithm has been migrated to this field to simplify calculations and improve fidelity.This review introduces the basic information on universal electron structures of emerging energy materials and ML algorithms involved in the prediction of material properties.Then,the structure-property relationships based on ML algorithm and QC theory are reviewed.Especially,the summary of recently reported applications on classifying crystal structure,modeling electronic structure,optimizing experimental method,and predicting performance is provided.Last,an outlook on ML assisted QC calculation towards identifying emerging energy materials is also presented.
基金supported by the National Natural Science Foundation of China(61502082)National Key R&D Program of China,Grant No.(2018YFA0306703).
文摘Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.
基金funded by eVIDA Research group IT-905-16 from Basque Government.
文摘Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.
文摘Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles.The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems,which are pervasive in machine learning.Quantum Support Vector Machine(QSVM)is a quantum machine learning algorithm inspired by classical Support Vector Machine(SVM)that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces.We provide a comprehensive overview of the underlying principles of QSVM,elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks.Through a comparative analysis,we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality.As a case study,we explored the potential of quantum paradigms in the context of a real-world problem:classifying pancreatic cancer biomarker data.The Support Vector Classifier(SVC)algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework.The classical approach as well as the quantum-based techniques reported similar accuracy.This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset.Remarkably,quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency.This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R196),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Nowadays,quantum machine learning is attracting great interest in a wide range offields due to its potential superior performance and capabilities.The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcarefield.Heart disease seriously threa-tens human health since it is the leading cause of death worldwide.Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis.In this study,an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease.The proposed model is a bagging ensemble learning model where a quantum support vector classifier was used as a base classifier.Further-more,in order to make the model’s outcomes more explainable,the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations(SHAP)framework.In the experimental study,other stand-alone quantum classifiers,namely,Quantum Support Vector Classifier(QSVC),Quantum Neural Network(QNN),and Variational Quantum Classifier(VQC)are applied and compared with classical machine learning classifiers such as Sup-port Vector Machine(SVM),and Artificial Neural Network(ANN).The experi-mental results on the Cleveland dataset reveal the superiority of QSVC compared to the others,which explains its use in the proposed bagging model.The Bagging-QSVC model outperforms all aforementioned classifiers with an accuracy of 90.16%while showing great competitiveness compared to some state-of-the-art models using the same dataset.The results of the study indicate that quantum machine learning classifiers perform better than classical machine learning classi-fiers in predicting heart disease.In addition,the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classifiers.
基金support from the National Key Research and Devel-opment Plan of China under Grant No.2018YFA0306703.
文摘Quantum machine learning(QML)is a rapidly rising research eld that incorporates ideas from quantum computing and machine learning to develop emerging tools for scientic research and improving data processing.How to efciently control or manipulate the quantum system is a fundamental and vexing problem in quantum computing.It can be described as learning or approximating a unitary operator.Since the success of the hybrid-based quantum machine learning model proposed in recent years,we investigate to apply the techniques from QML to tackle this problem.Based on the Choi–Jamiołkowski isomorphism in quantum computing,we transfer the original problem of learning a unitary operator to a min–max optimization problem which can also be viewed as a quantum generative adversarial network.Besides,we select the spectral norm between the target and generated unitary operators as the regularization term in the loss function.Inspired by the hybrid quantum-classical framework widely used in quantum machine learning,we employ the variational quantum circuit and gradient descent based optimizers to solve the min-max optimization problem.In our numerical experiments,the results imply that our proposed method can successfully approximate the desired unitary operator and dramatically reduce the number of quantum gates of the traditional approach.The average delity between the states that are produced by applying target and generated unitary on random input states is around 0.997.
文摘Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some forecasts based on the given data. Classical machine learning has its quantum part, which is known as quantum machine learning (QML). QML, which is a field of quantum computing, uses some of the quantum mechanical principles and concepts which include superposition, entanglement and quantum adiabatic theorem to assess the data and make some forecasts based on the data. At the present moment, research in QML has taken two main approaches. The first approach involves implementing the computationally expensive subroutines of classical machine learning algorithms on a quantum computer. The second approach concerns using classical machine learning algorithms on a quantum information, to speed up performance of the algorithms. The work presented in this manuscript proposes a quantum support vector algorithm that can be used to forecast solar irradiation. The novelty of this work is in using quantum mechanical principles for application in machine learning. Python programming language was used to simulate the performance of the proposed algorithm on a classical computer. Simulation results that were obtained show the usefulness of this algorithm for predicting solar irradiation.
基金Project supported by the Natural Science Foundation of Nanjing University of Posts and Telecommunications(Grant Nos.NY223109,NY220119,and NY221055)China Postdoctoral Science Foundation(Grant No.2022M721693)the National Natural Science Foundation of China(Grant No.12404365)。
文摘Classification of quantum phases is one of the most important areas of research in condensed matter physics.In this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised learning.Firstly,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave model.The unsupervised learning results match well with those obtained through traditional numerical diagonalization.Finally,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.
基金partially supported by the National Natural Science Foundation of China(Grants No.11875095 and 12175008).
文摘Neural networks possess formidable representational power,rendering them invaluable in solving complex quantum many-body systems.While they excel at analyzing static solutions,nonequilibrium processes,including critical dynamics during a quantum phase transition,pose a greater challenge for neural networks.To address this,we utilize neural networks and machine learning algorithms to investigate time evolutions,universal statistics,and correlations of topological defects in a one-dimensional transverse-field quantum Ising model.Specifically,our analysis involves computing the energy of the system during a quantum phase transition following a linear quench of the transverse magnetic field strength.The excitation energies satisfy a power-law relation to the quench rate,indicating a proportional relationship between the excitation energy and the kink numbers.Moreover,we establish a universal power-law relationship between the first three cumulants of the kink numbers and the quench rate,indicating a binomial distribution of the kinks.Finally,the normalized kink-kink correlations are also investigated and it is found that the numerical values are consistent with the analytic formula.
文摘Machine learning has become a premier tool in physics and other fields of science.It has been shown that the quantum mechanical scattering problem cannot only be solved with such techniques,but it was argued that the underlying neural network develops the Bom series for shallow potentials.However,classical machine learning algorithms fail in the unitary limit of an infinite scattering length.The unitary limit plays an important role in our understanding of bound strongly interacting fermionic systems and can be realized in cold atom experiments.Here,we develop a formalism that explains the unitary limit in terms of what we define as unitary limit surfaces.This not only allows to investigate the unitary limit geometrically in potential space,but also provides a numerically simple approach towards unnaturally large scattering lengths with standard multilayer perceptrons.Its scope is therefore not limited to applications in nuclear and atomic physics,but includes all systems that exhibit an unnaturally large scale.
文摘Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body.Previously,researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine,K-Nearest Neighbor,Convolutional Neural Network,K-means,Fuzzy C-means,Neural Network,Principle Component Analysis(PCA)and Naive Bayes.Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities.For instance,support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage.However this scheme is expensive in terms of execution time.On its part,k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points.However this scheme does not guarantee high accuracy when executed in different iterations.Although the K-nearest Neighbor algorithm employs feature reduction,principle component analysis and 10 fold cross validation methods for enhancing classification accuracy,it is not efficient in terms of processing time.On the other hand,fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets.However,it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved.Similarly,convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining.In addition,the neural network achieves low accuracy in its predictions.Since all these algorithms seem to be expensive and time consuming,it necessary to integrate quantum computing principles with conventional machine learning algorithms.This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs.In this paper,a review of the current machine learning algorithms for breast cancer prediction is provided.Based on the observed shortcomings,a quantum machine learning based classifier is recommended.The proposed working mechanisms of this classifier are elaborated towards the end of this paper.
基金Project supported by the Natural Science Foundation of Shandong Province,China (Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos.ZR2022LLZ012 and ZR2021LLZ001)。
文摘We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.
基金supported by the National Natural Science Foundation of China under Grant No.11725524the Hubei Provincal Natural Science Foundation of China under Grant No.2019CFA003
文摘Quantum machine learning based on quantum algorithms may achieve an exponential speedup over classical algorithms in dealing with some problems such as clustering.In this paper,we use the method of training the lower bound of the average log likelihood function on the quantum Boltzmann machine(QBM)to recognize the handwritten number datasets and compare the training results with classical models.We find that,when the QBM is semi-restricted,the training results get better with fewer computing resources.This shows that it is necessary to design a targeted algorithm to speed up computation and save resources.
文摘Recent advances in quantum technology have led to the development and the manufacturing of programmable quantum annealers that promise to solve certain combinatorial optimization problems faster than their classical counterparts.Semi-supervised learning is a machine learning technique that makes use of both labeled and unlabeled data for training,which enables a good classifier with only a small amount of labeled data.In this paper,we propose and theoretically analyze a graph-based semi-supervised learning method with the aid of the quantum annealing technique,which efficiently utilizes the quantum resources while maintaining good accuracy.We illustrate two classification examples,suggesting the feasibility of this method even with a small portion(30%) of labeled data involved.
文摘Applying quantum computing techniques to machine learning has attracted widespread attention recently and quantum machine learning has become a hot research topic. There are three major categories of machine learning: supervised, unsupervised, and reinforcement learning (RL). However, quantum RL has made the least progress when compared to the other two areas. In this study, we implement the well-known RL algorithm Q learning with a quantum neural network and evaluate it in the grid world environment. RL is learning through interactions with the environment, with the aim of discovering a strategy to maximize the expected cumulative rewards. Problems in RL bring in unique challenges to the study with their sequential nature of learning, potentially long delayed reward signals, and large or infinite size of state and action spaces. This study extends our previous work on solving the contextual bandit problem using a quantum neural network, where the reward signals are immediate after each action.
