Quantum Machine Learning on Limited-Qubit Hardware

Quantum Machine Learning (QML) integrates quantum algorithms into machine learning programs to enhance and expedite classical machine learning techniques. It leverages quantum data and hybrid quantum-classical models, utilizing the principles of superposition and entanglement to handle complex joint probability distributions that would require exponential classical computational resources. QML aims to harness the unique properties of quantum computers for practical machine learning tasks, with the goal of achieving quantum advantage over classical approaches. QML finds applications in diverse fields such as chemical and quantum matter simulation, quantum control, quantum communication networks, and quantum metrology. Quantum algorithms can analyze quantum states and improve computational speed and data storage. Two main frameworks, quantum kernel methods and variational quantum algorithms, are widely used due to their implement ability on quantum hardware and capacity to work on general datasets. Quantum computers can solve linear algebraic problems, calculate eigenvectors and eigenvalues, and perform support vector algorithms at exponentially faster rates than classical computers. Despite its potential, QML faces significant challenges related to quantum hardware capabilities, including limited qubit connectivity, noise, coherence times, and errors in state preparation and measurement. Hardware constraints, compatibility issues, high computational and memory requirements, and the difficulty of quantum programming also pose obstacles. Overcoming these hardware and software limitations is crucial for realizing the full potential of quantum machine learningQuantum Machine Learning (QML) integrates quantum algorithms into machine learning programs to enhance and expedite classical machine learning techniques. It leverages quantum data and hybrid quantum-classical models, utilizing the principles of superposition and entanglement to handle complex joint probability distributions that would require exponential classical computational resources. QML aims to harness the unique properties of quantum computers for practical machine learning tasks, with the goal of achieving quantum advantage over classical approaches. QML finds applications in diverse fields such as chemical and quantum matter simulation, quantum control, quantum communication networks, and quantum metrology. Quantum algorithms can analyze quantum states and improve computational speed and data storage. Two main frameworks, quantum kernel methods and variational quantum algorithms, are widely used due to their implement ability on quantum hardware and capacity to work on general datasets. Quantum computers can solve linear algebraic problems, calculate eigenvectors and eigenvalues, and perform support vector algorithms at exponentially faster rates than classical computers. Despite its potential, QML faces significant challenges related to quantum hardware capabilities, including limited qubit connectivity, noise, coherence times, and errors in state preparation and measurement. Hardware constraints, compatibility issues, high computational and memory requirements, and the difficulty of quantum programming also pose obstacles. Overcoming these hardware and software limitations is crucial for realizing the full potential of quantum machine learning

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Reducing qubit budget in quantum machine learning

Jie Pan

Nature Computational ScienceSep 16, 20210 citations

Higgs analysis with quantum classifiers

Vasilis Belis, Samuel González-Castillo, Christina Reissel +4 more

We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.

arXiv:2104.07692EPJ Web of ConferencesJan 1, 202143 citations

Quantum Computing in Machine Learning

Sanjay Chakraborty, Lopamudra Dey

Data-intensive researchJan 1, 20232 citations

25th International Conference on Computing in High Energy & Nuclear Physics

Vasilis Belis

We have developed two quantum classifier models for the $t\bar{t}H$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits -to accommodate for limitations in both simulation hardware and real quantum hardware- we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.We have developed two quantum classifier models for the $t\bar{t}H$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits -to accommodate for limitations in both simulation hardware and real quantum hardware- we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.

Jan 1, 20210 citations

We have developed two quantum classifier models for the

t\bar{t}H

classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits -to accommodate for limitations in both simulation hardware and real quantum hardware- we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.