IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices

Chu, Cheng; Skipper, Grant; Swany, Martin; Chen, Fan

doi:10.1109/icassp49357.2023.10096772

Cited by 8 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improvements to these models to arbitrarily sized graphs have been made with the implementation of ego-graph-based quantum graph neural networks (egoQGNNs) [25]. Quantum analogs of other advanced classical architectures, including generative adversarial networks (GANs), transformers, natural language processors (NLPs), and equivariant networks, have also been proposed [23,[26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Forestano,

Comajoan Cara,

Dahale

et al. 2024

Axioms

View full text Add to dashboard Cite

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

show abstract

Section: Introductionmentioning

confidence: 99%

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Forestano,

Comajoan Cara,

Dahale

et al. 2024

Axioms

View full text Add to dashboard Cite

show abstract

“…Quantum Machine Learning (QML) introduces the power of quantum computing to the existing foundation of machine learning to establish and then exploit the quantum advantage for a performance increase exclusive to quantum algorithms. While gate-based quantum computing differs significantly from classical computing, many equivalents to aforementioned classical generative networks have already been constructed, including Quantum Autoencoders [21] and Quantum GANs [22][23][24][25][26][27]. The notable exception is INNs [28,29], which have not yet been transferred to the realm of QML.…”

Section: Introductionmentioning

confidence: 99%

Generative invertible quantum neural networks

Rousselot,

Spannowsky

2024

SciPost Phys.

View full text Add to dashboard Cite

Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons, a standard candle process for particle collider precision measurements. We compare the QINN’s performance for different loss functions and training scenarios. For this task, we find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data.

show abstract

Successive data injection in conditional quantum GAN applied to time series anomaly detection

Kalfon,

Cherkaoui,

Laprade

et al. 2024

IET Quantum Communication

View full text Add to dashboard Cite

Classical GAN architectures have shown interesting results for solving anomaly detection problems in general and for time series anomalies in particular, such as those arising in communication networks. In recent years, several quantum GAN (QGAN) architectures have been proposed in the literature. When detecting anomalies in time series using QGANs, huge challenges arise due to the limited number of qubits compared to the size of the data. To address these challenges, a new high‐dimensional encoding approach, named Successive Data Injection (SuDaI) is proposed. In this approach, SuDaI explores a larger portion of the quantum state, compared to the conventional angle encoding method used predominantly in the literature. This is achieved through repeated data injections into the quantum state. SuDaI encoding allows the authors to adapt the QGAN for anomaly detection with network data of a much higher dimensionality than with the existing known QGANs implementations. In addition, SuDaI encoding applies to other types of high‐dimensional time series and can be used in contexts beyond anomaly detection and QGANs, opening up therefore multiple fields of application.

show abstract

IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices

Cited by 8 publications

References 12 publications

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Generative invertible quantum neural networks

Successive data injection in conditional quantum GAN applied to time series anomaly detection

Contact Info

Product

Resources

About