Invited: Trainable Discrete Feature Embeddings for Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

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Advanced patterns of predictions and cavernous data analytics using quantum machine learning

Materials Today Proceedings ◽

10.1016/j.matpr.2020.11.062 ◽

2021 ◽

Author(s):

G. Arunakranthi ◽

B. Rajkumar ◽

V. Chandra Shekhar Rao ◽

A. Harshavardhan

Keyword(s):

Machine Learning ◽

Data Analytics ◽

Quantum Machine Learning

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Quantum Machine Learning: A tutorial

Neurocomputing ◽

10.1016/j.neucom.2021.02.102 ◽

2021 ◽

Author(s):

José D. Martín-Guerrero ◽

Lucas Lamata

Keyword(s):

Machine Learning ◽

Quantum Machine Learning

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Quantum Reinforcement Learning with Quantum Photonics

Photonics ◽

10.3390/photonics8020033 ◽

2021 ◽

Vol 8 (2) ◽

pp. 33

Author(s):

Lucas Lamata

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Quantum Computation ◽

Exchange Information ◽

Quantum Machine Learning ◽

Quantum Technology ◽

Quantum Photonics

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

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Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01588-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kinshuk Sengupta ◽

Praveen Ranjan Srivastava

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Image Classification ◽

Machine Learning Algorithms ◽

Classification Task ◽

Clinical Image ◽

Prototype Model ◽

Learning Models ◽

Accuracy Measure ◽

Quantum Machine Learning

Abstract Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

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