#33: July 31st – August 6th

📰News:

📽Videos:

👨‍💻Developers:

New features
Added a new optional extra experiments that installs the
qiskit-experiments package and also included it in the all target.
You can now install qiskit-experiments with qiskit using
pip install “qiskit[experiments]” or pip install “qiskit[all]”.

Documentation Changes
Modify README.md to include update instructions

📗Papers:


Large-scale quantum machine learning

Tobias Haug,Chris N. Self,M. S. KimAug 03 2021 quant-phcs.LGstat.ML arXiv:2108.01039v1

Quantum computers promise to enhance machine learning for practical applications. Quantum machine learning for real-world data has to handle extensive amounts of high-dimensional data. However, conventional methods for measuring quantum kernels are impractical for large datasets as they scale with the square of the dataset size. Here, we measure quantum kernels using randomized measurements to gain a quadratic speedup in computation time and quickly process large datasets. Further, we efficiently encode high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth. The encoding is characterized by the quantum Fisher information metric and is related to the radial basis function kernel. We demonstrate the advantages and speedups of our methods by classifying images with the IBM quantum computer. Our approach is exceptionally robust to noise via a complementary error mitigation scheme. Using currently available quantum computers, the MNIST database can be processed within 220 hours instead of 10 years which opens up industrial applications of quantum machine learning.

Quantum convolutional neural network for classical data classification

Tak Hur,Leeseok Kim,Daniel K. ParkAug 03 2021 quant-ph arXiv:2108.00661v1

With the rapid advance of quantum machine learning, several proposals for the quantum-analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark fully parametrized quantum convolutional neural networks (QCNNs) for classical data classification. In particular, we propose a quantum neural network model inspired by CNN that only uses two-qubit interactions throughout the entire algorithm. We investigate the performance of various QCNN models differentiated by structures of parameterized quantum circuits, quantum data encoding methods, classical data pre-processing methods, cost functions and optimizers on MNIST and Fashion MNIST datasets. In most instances, QCNN achieved excellent classification accuracy despite having a small number of free parameters. The QCNN models performed noticeably better than CNN models under the similar training conditions. Since the QCNN algorithm presented in this work utilizes fully parameterized and shallow-depth quantum circuits, it is suitable for Noisy Intermediate-Scale Quantum (NISQ) devices.

Spacetime Neural Network for High Dimensional Quantum Dynamics

Jiangran Wang,Zhuo Chen,Di Luo,Zhizhen Zhao,Vera Mikyoung Hur,Bryan K. ClarkAug 05 2021 cond-mat.dis-nncs.LGphysics.comp-phquant-ph arXiv:2108.02200

We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schrödinger equation. In contrast to the standard iterative first order optimization and the time-dependent variational principle, our approach utilizes the implicit mid-point method and generates the solution for all spatial and temporal values simultaneously after optimization. We demonstrate the method in the Schrödinger equation with a self-normalized autoregressive spacetime neural network construction. Future explorations for solving different high dimensional differential equations are discussed.

Variational quantum eigensolver for the Heisenberg antiferromagnet on the kagome lattice

Joris Kattemölle,Jasper van WezelAug 05 2021 quant-phcond-mat.str-el arXiv:2108.02175v1

Establishing the nature of the ground state of the Heisenberg antiferromagnet (HAFM) on the kagome lattice is well known to be a prohibitively difficult problem for classical computers. Here, we give a detailed proposal for a Variational Quantum Eigensolver (VQE) with the aim of solving this physical problem on a quantum computer. At the same time, this VQE constitutes an explicit proposal for showing a useful quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) devices because of its natural hardware compatibility. We classically emulate a noiseless quantum computer with the connectivity of a 2D square lattice and show how the ground state energy of a 20-site patch of the kagome HAFM, as found by the VQE, approaches the true ground state energy exponentially as a function of the circuit depth. Besides indicating the potential of quantum computers to solve for the ground state of the kagome HAFM, the classical emulation of the VQE serves as a benchmark for real quantum devices on the way towards a useful quantum advantage.

Machine learning for secure key rate in continuous-variable quantum key distribution

Min-Gang Zhou,Zhi-Ping Liu,Wen-Bo Liu,Chen-Long Li,Jun-Lin Bai,Yi-Ran Xue,Yao Fu,Hua-Lei Yin,Zeng-Bing ChenAug 06 2021 quant-ph arXiv:2108.02578

Continuous-variable quantum key distribution (CV-QKD) with discrete modulation has received widespread attentions because of its experimental simplicity, lower-cost implementation and ease to multiplex with classical optical communication. Recently, some inspiring numerical methods have been applied to analyse the security of discrete-modulated CV-QKD against collective attacks, which promises to obtain considerable key rate over one hundred kilometers of fiber distance. However, numerical methods require up to ten minutes to calculate a secure key rate one time using a high-performance personal computer, which means that extracting the real-time secure key rate is impossible for discrete-modulated CV-QKD system. Here, we present a neural network model to quickly predict the secure key rate of homodyne detection discrete-modulated CV-QKD with good accuracy based on experimental parameters and experimental results. With the excess noise of about 0.010.01, the speed of our method is improved by about seven orders of magnitude compared to that of the conventional numerical method. Our method can be extended to quickly solve complex security key rate calculation of a variety of other unstructured quantum key distribution protocols.

Simulation of Open Quantum Dynamics with Bootstrap-Based Long Short-Term Memory Recurrent Neural Network

Kunni Lin,Jiawei Peng,Feng Long Gu,Zhenggang LanAug 04 2021 physics.chem-phquant-ph arXiv:2108.01310

The recurrent neural network with the long short-term memory cell (LSTM-NN) is employed to simulate the long-time dynamics of open quantum system. Particularly, the bootstrap resampling method is applied in the LSTM-NN construction and prediction, which provides a Monte-Carlo approach in the estimation of forecasting confidence interval. In this bootstrap-based LSTM-NN approach, a large number of LSTM-NNs are constructed under the resampling of time-series data sequences that were obtained from the early-stage quantum evolution given by numerically-exact multilayer multiconfigurational time-dependent Hartree method. The built LSTM-NN ensemble is used for the reliable propagation of the long-time quantum dynamics, and the forecasting uncertainty that partially reflects the reliability of the LSTM-NN prediction is given at the same time. The long-time quantum dissipative dynamics simulated by the current bootstrap-based LSTM-NN approach is highly consistent with the exact quantum dynamics results. This demonstrates that the LSTM-NN prediction combined with the bootstrap approach is a practical and powerful tool to propagate the long-time quantum dynamics of open systems with high accuracy and low computational cost.

Hybrid Quantum-Classical Neural Network for Incident Detection

Zadid Khan,Sakib Mahmud Khan,Jean Michel Tine,Ayse Turhan Comert,Diamon Rice,Gurcan Comert,Dimitra Michalaka,Judith Mwakalonge,Reek Majumdar,Mashrur ChowdhuryAug 04 2021 cs.LGcs.SYeess.SYquant-ph arXiv:2108.01127

The efficiency and reliability of real-time incident detection models directly impact the affected corridors’ traffic safety and operational conditions. The recent emergence of cloud-based quantum computing infrastructure and innovations in noisy intermediate-scale quantum devices have revealed a new era of quantum-enhanced algorithms that can be leveraged to improve real-time incident detection accuracy. In this research, a hybrid machine learning model, which includes classical and quantum machine learning (ML) models, is developed to identify incidents using the connected vehicle (CV) data. The incident detection performance of the hybrid model is evaluated against baseline classical ML models. The framework is evaluated using data from a microsimulation tool for different incident scenarios. The results indicate that a hybrid neural network containing a 4-qubit quantum layer outperforms all other baseline models when there is a lack of training data. We have created three datasets; DS-1 with sufficient training data, and DS-2 and DS-3 with insufficient training data. The hybrid model achieves a recall of 98.9%, 98.3%, and 96.6% for DS-1, DS-2, and DS-3, respectively. For DS-2 and DS-3, the average improvement in F2-score (measures model’s performance to correctly identify incidents) achieved by the hybrid model is 1.9% and 7.8%, respectively, compared to the classical models. It shows that with insufficient data, which may be common for CVs, the hybrid ML model will perform better than the classical models. With the continuing improvements of quantum computing infrastructure, the quantum ML models could be a promising alternative for CV-related applications when the available data is insufficient.

Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack

Reek Majumder,Sakib Mahmud Khan,Fahim Ahmed,Zadid Khan,Frank Ngeni,Gurcan Comert,Judith Mwakalonge,Dimitra Michalaka,Mashrur ChowdhuryAug 04 2021 quant-phcs.CRcs.LG arXiv:2108.01125v1

Image classification must work for autonomous vehicles (AV) operating on public roads, and actions performed based on image misclassification can have serious consequences. Traffic sign images can be misclassified by an adversarial attack on machine learning models used by AVs for traffic sign recognition. To make classification models resilient against adversarial attacks, we used a hybrid deep-learning model with both the quantum and classical layers. Our goal is to study the hybrid deep-learning architecture for classical-quantum transfer learning models to support the current era of intermediate-scale quantum technology. We have evaluated the impacts of various white box adversarial attacks on these hybrid models. The classical part of hybrid models includes a convolution network from the pre-trained Resnet18 model, which extracts informative features from a high dimensional LISA traffic sign image dataset. The output from the classical processor is processed further through the quantum layer, which is composed of various quantum gates and provides support to various quantum mechanical features like entanglement and superposition. We have tested multiple combinations of quantum circuits to provide better classification accuracy with decreasing training data and found better resiliency for our hybrid classical-quantum deep learning model during attacks compared to the classical-only machine learning models.

Quantum Neural Networks: Concepts, Applications, and Challenges

Yunseok Kwak,Won Joon Yun,Soyi Jung,Joongheon KimAug 04 2021 quant-phcs.LG arXiv:2108.01468v1

Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks. The research topics and directions of deep learning and quantum computing have been separated for long time, however by discovering that quantum circuits can act like artificial neural networks, quantum deep learning research is widely adopted. This paper explains the backgrounds and basic principles of quantum deep learning and also introduces major achievements. After that, this paper discusses the challenges of quantum deep learning research in multiple perspectives. Lastly, this paper presents various future research directions and application fields of quantum deep learning.

Categories: Week-in-QML

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