Transfer learning in hybrid classical-quantum neural networks

Mari, Andrea; Bromley, Thomas R.; Izaac, Josh; Schuld, Maria; Killoran, Nathan

doi:10.22331/q-2020-10-09-340

Cited by 231 publications

(160 citation statements)

References 35 publications

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“…Quantum transfer learning is a hybrid machine learning method consisting of a feature extractor classical network and a quantum variational classifier circuit[25]. In our study, we used the ResNet18[33] convolution network as feature extractor.…”

Section: Matarial and Methodsmentioning

confidence: 99%

“…In the hybrid classical-quantum machine learning, where the data are classic, the data are first coded into the quantum language and the operations are done with quantum operators. In this context, Mari et al [25] investigated transfer learning in hybrid classical-quantum neural networks. Zen et al [26] used transfer learning for the scalability of neural network quantum states.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

Acar

Yılmaz

2020

Preprint

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Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. Also, according to the results obtained, machine learning process in classical computers requiring more processors and time than quantum computers can be realized in a very short time with a very small quantum processor such as 4 qubits in quantum computers. If the size of the data set is small; Due to the superior properties of quantum, it is seen that according to the classification of COVID-19 and Normal, in terms of machine learning, quantum computers seem to outperform traditional computers.

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Section: Matarial and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

Acar

Yılmaz

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Content may change prior to final publication. CNOT qq (17) where CNOT qq is the usual controlled NOT operation between control qubit q and target q acting on the space of all 4-qubits. For n cycles, the total number n θ of θ-parameters, including the initial rotation layer R(θ 0,1 .…”

Section: Case Study: Pattern Recognitionmentioning

confidence: 99%

Variational Learning for Quantum Artificial Neural Networks

Tacchino

Mangini

Barkoutsos

et al. 2021

IEEE Trans. Quantum Eng.

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In the last few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed. The rapidly growing field of Quantum Machine Learning aims at bringing together these two ongoing revolutions. Here we first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. We investigate different learning strategies involving global and local layer-wise cost functions, and we assess their performances also in the presence of statistical measurement noise. While keeping full compatibility with the overall memory-efficient feed-forward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons upon input of the relevant data-encoding quantum states. This suggests a viable approach towards the use of quantum neural networks for pattern classification on near-term quantum hardware.

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“…The most notable progress is the development of variational algorithms [ 17 , 18 , 19 ] which enable the quantum machine learning on NISQ devices [ 20 ]. Recent efforts have demonstrated the promising application of NISQ devices in several machine learning tasks [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

Federated Quantum Machine Learning

Chen

Yoo

2021

Entropy

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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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Transfer learning in hybrid classical-quantum neural networks

Cited by 231 publications

References 35 publications

COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

Variational Learning for Quantum Artificial Neural Networks

Federated Quantum Machine Learning

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