Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs: (Invited Paper)

Wang, Zhepeng; Liang, Zhiding; Zhou, Shanglin; Ding, Caiwen; Shi, Yiyu; Jiang, Weiwen

doi:10.1109/iccad51958.2021.9643575

Cited by 24 publications

(8 citation statements)

References 21 publications

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“…Previous studies [8] have demonstrated the exponential speedup potential of training SVMs using quantum computing, particularly using quantum techniques, to invert the kernel matrix from polynomial to logarithmic complexity. Currently, quantum computing is being gradually applied in image processing and other fields, and it has obtained significantly good results [39]. Therefore, quantum advantage is also required in the field of malicious code detection to improve and break the existing bottleneck.…”

Section: Quantum Machine Learningmentioning

confidence: 99%

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Huang¹,

Li²,

Qiao³

et al. 2022

Electronics

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Currently, among the millions of Android applications, there exist numerous malicious programs that pose significant threats to people’s security and privacy. Therefore, it is imperative to develop approaches for detecting Android malware. Recently developed malware detection methods usually rely on various features, such as application programming interface (API) sequences, images, and permissions, thereby ignoring the importance of source code and the associated comments, which are not usually included in malware. Therefore, we propose Android-SEM, which is an Android source code semantic enhancement model based on transfer learning. Our proposed model is built upon the Transformer architecture to achieve a pretraining framework for generating code comments from malware source code. The performance of the pretraining framework is optimized using a generative adversarial network. Our proposed model relies on a novel regression model-based filter to retain high-quality comments and source code for feature fusion pertinent to semantic enhancement. Creatively, and contrary to conventional methods, we incorporated a quantum support vector machine (QSVM) for classifying malicious Android code by combining quantum machine learning and classical deep learning models. The results proved that Android-SEM achieves accuracy levels of 99.55% and 99.01% for malware detection and malware categorization, respectively.

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Section: Quantum Machine Learningmentioning

confidence: 99%

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Huang¹,

Li²,

Qiao³

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…Quantum computing has the properties of superposition and entanglement, which grants quantum speedup for certain problems [2,8,10]. Compared with classical computing, exponential speedup has been demonstrated in areas such as quantum chemistry [21,6], finance [9], and machine learning [11,29,12]. Therefore, quantum computing has been considered among the best candidates for solving some complex computational problems that cannot be solved by classical computers.…”

Section: Introductionmentioning

confidence: 99%

Variational Quantum Pulse Learning

Liang¹,

Wang²,

Cheng³

et al. 2022

Preprint

Self Cite

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Quantum computing is among the most promising emerging techniques to solve problems that are computationally intractable on classical hardware. A large body of existing works focus on using variational quantum algorithms on the gate level for machine learning tasks, such as the variational quantum circuit (VQC). However, VQC has limited flexibility and expressibility due to limited number of parameters, e.g. only one parameter can be trained in one rotation gate. On the other hand, we observe that quantum pulses are lower than quantum gates in the stack of quantum computing and offers more control parameters. Inspired by the promising performance of VQC, in this paper we propose variational quantum pulses (VQP), a novel paradigm to directly train quantum pulses for learning tasks. The proposed method manipulates variational quantum pulses by pulling and pushing the amplitudes of pulses in an optimization framework. Similar to variational quantum algorithms, our framework to train pulses maintains the robustness to noise on Noisy Intermediate-Scale Quantum (NISQ) computers. In an example task of binary classification, VQP learning achieves 30% and 40% higher accuracy compared with VQC learning on the qiskit noise simulatosr (FakeMachines) and ibmq-jarkata, respectively, demonstrating its effectiveness and feasibility. Stability for VQP to obtain reliable results has also been verified in the presence of noise.

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“…Quantum Computing (QC) presents a new computational paradigm that has the potential to address classically intractable problems with much higher efficiency and speed. It has been shown to have an exponential or polynomial advantage in various domains such as combinatorial optimization [12], molecular dynamics [27,35], and machine learning [3,7,16,21,28,29,38,57], etc. By virtue of breakthroughs in physical implementation technologies, QC hardware has advanced quickly during the last two decades.…”

Section: Introductionmentioning

confidence: 99%

QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

Wang¹,

Liu²,

Cheng³

et al. 2022

Preprint

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Quantum Computing has attracted much research attention because of its potential to achieve fundamental speed and efficiency improvements in various domains. Among different quantum algorithms, Parameterized Quantum Circuits (PQC) for Quantum Machine Learning (QML) show promises to realize quantum advantages on the current Noisy Intermediate-Scale Quantum (NISQ) Machines. Therefore, to facilitate the QML and PQC research, a recent python library called TorchQuantum has been released. It can construct, simulate, and train PQC for machine learning tasks with high speed and convenient debugging supports. Besides quantum for ML, we want to raise the community's attention on the reversed direction: ML for quantum. Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.This paper presents a case study of the ML for quantum part in TorchQuantum. Since estimating the noise impact on circuit reliability is an essential step toward understanding and mitigating noise, we propose to leverage classical ML to predict noise impact on circuit fidelity. Inspired by the natural graph representation of quantum circuits, we propose to leverage a graph transformer model to predict the noisy circuit fidelity. We firstly collect a large dataset with a variety of quantum circuits and obtain their fidelity on noisy simulators and real machines. Then we embed each circuit into a graph with gate and noise properties as node features, and adopt a graph transformer to predict the fidelity. We can avoid exponential classical simulation cost and efficiently estimate fidelity with polynomial complexity.Evaluated on 5 thousand random and algorithm circuits, the graph transformer predictor can provide accurate fidelity estimation with RMSE error 0.04 and outperform a simple neural networkbased model by 0.02 on average. It can achieve 0.99 and 0.95 R 2 scores for random and algorithm circuits, respectively. Compared with circuit simulators, the predictor has over 200× speedup for Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs: (Invited Paper)

Cited by 24 publications

References 21 publications

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Variational Quantum Pulse Learning

QuEst: Graph Transformer for Quantum Circuit Reliability Estimation

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