Unsupervised Classification of Quantum Data

Sentís, Gael; Monràs, Alex; Muñoz-Tapia, R.; Calsamiglia, John; Bagán, E.

doi:10.1103/physrevx.9.041029

Cited by 38 publications

(29 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One can exploit classical ML to improve quantum tasks ("QC" ML, see refs. 7,8 for a discussion of this terminology) such as the simulation of many-body systems 9 , adaptive quantum computation 10 or quantum metrology 11 , or one can exploit quantum algorithms to speed up classical ML ("CQ" ML) [12][13][14][15] , or, finally, one can exploit quantum computing devices to carry out learning tasks with quantum data ("QQ" ML) [16][17][18][19][20][21][22][23][24] . A good review on this topic can be found in ref.…”

mentioning

confidence: 99%

Training deep quantum neural networks

et al. 2020

View full text Add to dashboard Cite

Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.

show abstract

mentioning

confidence: 99%

Training deep quantum neural networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This study conducted an experimental analysis with a new variant of a learning model to further take advantage of quantum computing devices to perform learning tasks with quantum data [30]. We assumed that Quanvolutional neural network or Quantum neural network (QNN) would solve classical deep learning problems to be computationally faster from the design paradigm.…”

Section: Quantum Neural Network Modelmentioning

confidence: 99%

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

Sengupta

Srivastava

2021

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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.

show abstract

“…Quantum machine learning, whereby classical ML is generalized to the quantum realm, has enjoyed a recent renaissance, leading to a dizzying array of formulations and applications (see [6][7][8] and references therein for a cross section). Broadly speaking one has the following taxonomy [9]: (i) quantum speedups for classical ML [10][11][12][13]; (ii) classical ML to characterize quantum systems [14][15][16]; or (iii) quantum devices to learn quantum data ("full" QML) [17][18][19][20][21][22][23][24][25][26][27]. Our focus here is on the last category, as it is this scenario where quantum speedups are not only most likely, but also most urgently required owing to the aforementioned exponential difficulty of tomography.…”

Section: Introductionmentioning

confidence: 99%

“…A variety of quantum architectures for QML have been considered, from variational quantum circuits [24,28] to quantum analogues of artificial neural networks [20,22,23,25,26,29]. We believe that the quantum neural network (QNN) architecture introduced in [26] offers a most promising platform for full QML.…”

Section: Introductionmentioning

confidence: 99%

Quantum machine learning of graph-structured data

Beer¹,

Khosla²,

Julius³

et al. 2021

Preprint

View full text Add to dashboard Cite

Graph structures are ubiquitous throughout the natural sciences. Here we consider graph-structured quantum data and describe how to carry out its quantum machine learning via quantum neural networks. In particular, we consider training data in the form of pairs of input and output quantum states associated with the vertices of a graph, together with edges encoding correlations between the vertices. We explain how to systematically exploit this additional graph structure to improve quantum learning algorithms. These algorithms are numerically simulated and exhibit excellent learning behavior. Scalable quantum implementations of the learning procedures are likely feasible on the next generation of quantum computing devices.

show abstract

Unsupervised Classification of Quantum Data

Cited by 38 publications

References 59 publications

Training deep quantum neural networks

Training deep quantum neural networks

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

Quantum machine learning of graph-structured data

Contact Info

Product

Resources

About