Machine learning methods in quantum computing theory

Fastovets, D. V.; Bogdanov, Yu. I.; Bantysh, B. I.; Лукичев, В. Ф.

doi:10.1117/12.2522427

Cited by 23 publications

(11 citation statements)

References 14 publications

(13 reference statements)

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“…Representing these gates with simple gates available on a NISQ device however results in long circuits that are prone to noise. Therefore, simplified ansaetze for the two-qubit gates are commonly used [35,40,84,89,90]. For quantum input, the performance of these simplified ansaetze can yield comparable maximum performance to a general unitary ansatz, but they seem to be harder to train.…”

Section: Quantum Tensor Network Machine Learningmentioning

confidence: 99%

Tensor networks for quantum machine learning

2023

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Once developed for quantum theory, tensor networks (TNs) have been established as a successful machine learning (ML) paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum ML to assess problems that classical computers are unable to solve efficiently. Their nature at the interface between physics and ML makes TNs easily deployable on quantum computers. In this review article, we shed light on one of the major architectures considered to be predestined for variational quantum ML. In particular, we discuss how layouts like matrix product state, projected entangled pair states, tree tensor networks and multi-scale entanglement renormalization ansatz can be mapped to a quantum computer, how they can be used for ML and data encoding and which implementation techniques improve their performance.

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Section: Quantum Tensor Network Machine Learningmentioning

confidence: 99%

Tensor networks for quantum machine learning

2023

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“…They showed procedure of estimating expectation values of gradients for quantum measurements. Fastovets et al [12] proposed approaches through which classical machine learning algorithms can be executed on quantum computers. They demonstrated their approach by executing a multiclass tensor network algorithm on a quantum computer provided by IBM Quantum Experience.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers

Joshi

Katyayan

Ahmed³

2021

J. Phys.: Conf. Ser.

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In this paper, we have studied the performance and role of local optimizers in quantum variational circuits. We studied the performance of the two most popular optimizers and compared their results with some popular classical machine learning algorithms. The classical algorithms we used in our study are support vector machine (SVM), gradient boosting (GB) and random forest (RF). These were compared with a variational quantum classifier (VQC) using two sets of local optimizers viz AQGD and COBYLA. For experimenting with VQC, IBM Quantum Experience and IBM Qiskit was used while for classical machine learning models, scikit-learn was used. The results show that machine learning on noisy immediate scale quantum machines can produce comparable results as on classical machines. For our experiments, we have used a popular restaurant sentiment analysis dataset. The extracted features from this dataset and then after applying PCA reduced the feature set into 5 features. Quantum ML models were trained using 100 epochs and 150 epochs on using EfficientSU2 variational circuit. Overall, four Quantum ML models were trained and three Classical ML models were trained. The performance of the trained models was evaluated using standard evaluation measures viz, Accuracy, Precision, Recall, F-Score. In all the cases AQGD optimizer-based model with 100 Epochs performed better than all other models. It produced an accuracy of 77% and an F-Score of 0.785 which were highest across all the trained models.

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“…[17][18][19][20][21][22][23][24] One way to achieve this is through the use of machine learning methods. 25,26 However, errors in the training data can lead to the overestimated accuracy of subsequent results. The gate set tomography (GST) approach allows one to estimate the parameters of several transformations at once and automatically take into account SPAM errors.…”

Section: Introductionmentioning

confidence: 99%

Quantum tomography for quantum systems optimization

Bantysh¹,

Bogdanov²

2022

Preprint

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Debugging quantum states transformations is an important task of modern quantum computing. The use of quantum tomography for these purposes significantly expands the range of possibilities. However, the presence of preparation and measurement errors complicates the practical use of this procedure. In this work, we investigate the possibility of estimating these errors from experiment. These estimates are subsequently used to build a robust quantum tomography model. The model allows one to accurately reconstruct unitary errors of singlequbit gates. We show that, having such imperfect single-qubit gates with pre-estimated errors, one can obtain transformations close to ideal ones. A similar approach can also significantly mitigate single-qubit gates crosstalk.

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Machine learning methods in quantum computing theory

Cited by 23 publications

References 14 publications

Tensor networks for quantum machine learning

Tensor networks for quantum machine learning

Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers

Quantum tomography for quantum systems optimization

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