HiQ-ProjectQ: Towards user-friendly and high-performance quantum computing on GPUs

Nguyen, Damien; Mikushin, Dmitry; Man-Hong, Yung

doi:10.23919/date51398.2021.9474170

“…For the future development of this field, it is important to further develop quantum classification theories and models to find more potential applications. With the help of the softwares used for simulating these quantum models [191][192][193][194][195][196][197][198][199][200][201][202][203][204], it gets easier to quantify the performance of these models. Moreover, the size of the quantum platforms, such as the superconducting platforms and the ion trap systems, are expected to grow fast to meet the requirement of the theories of the various quantum classifiers.…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Recent advances for quantum classifiers

Li,

Deng

2021

Preprint

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Machine learning has achieved dramatic success in a broad spectrum of applications. Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications, giving rise to an emergent research frontier of quantum machine learning. Along this line, quantum classifiers, which are quantum devices that aim to solve classification problems in machine learning, have attracted tremendous attention recently. In this review, we give a relatively comprehensive overview for the studies of quantum classifiers, with a focus on recent advances. First, we will review a number of quantum classification algorithms, including quantum support vector machine, quantum kernel methods, quantum decision tree, and quantum nearest neighbor algorithm. Then, we move on to introduce the variational quantum classifiers, which are essentially variational quantum circuits for classifications. We will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem, where the training of quantum classifiers might be hindered by the exponentially vanishing gradient. In addition, the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

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“…When designing new QNN models or testing a QNN's performance on a given dataset, an important step is to efficiently simulate the QNN's training dynamics. At the current stage, a number of quantum simulation platforms have been built by institutes and companies worldwide [64][65][66][67][68][69][70][71][72][73][74][75][76][77]. In this paper and the accompanying open-source code, we will utilize Yao.jl [64], a quantum simulation framework written in Julia Language [65], to construct the models introduced and benchmarked in the following sections.…”

Section: Introductionmentioning

confidence: 99%

Quantum Neural Network Classifiers: A Tutorial

Li

¹

,

Lu

²

,

Deng

³

2022

SciPost Phys. Lect. Notes

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Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.

show abstract

“…When designing new QNN models or testing a QNN's performance on a given dataset, an important step is to efficiently simulate the QNN's training dynamics. At the current stage, a number of quantum simulation platforms have been built by institutes and companies worldwide [56][57][58][59][60][61][62][63][64][65][66][67][68][69]. In this paper and the accompanying open-source code, we will utilize Yao.jl [56], a quantum simulation framework written in Julia Language [57], to construct the models introduced and benchmarked in the following sections.…”

Section: Introductionmentioning

confidence: 99%

Quantum Neural Network Classifiers: A Tutorial

Li¹,

Lu²,

Deng³

2022

Preprint

0

View full text Add to dashboard Cite

Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.

show abstract

HiQ-ProjectQ: Towards user-friendly and high-performance quantum computing on GPUs

Cited by 5 publications

References 9 publications

Recent advances for quantum classifiers

Recent advances for quantum classifiers

Quantum Neural Network Classifiers: A Tutorial

Quantum Neural Network Classifiers: A Tutorial

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