Quantum states tomography with noisy measurement channels

Bogdanov, Yu. I.; Bantysh, B. I.; Богданова, Н. А.; Kvasnyy, A. B.; Лукичев, В. Ф.

doi:10.1117/12.2267029

Cited by 13 publications

(13 citation statements)

References 29 publications

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“…Using this model, it is possible to obtain a consistent estimate of a quantum state or a quantum process. 17,20,21,24 Note, however, that this requires knowing the SPAM parameters with sufficient accuracy. Below we consider a possible experimental procedures for estimating these parameters in the trapped ion system.…”

Section: Fuzzy Measurements Modelmentioning

confidence: 99%

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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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Section: Fuzzy Measurements Modelmentioning

confidence: 99%

“…To avoid this, the QT model must take into account SPAM errors. [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.…”

Section: Introductionmentioning

confidence: 99%

Quantum tomography for quantum systems optimization

Bantysh¹,

Bogdanov²

2022

Preprint

Self Cite

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“…(8)) then the protocol maintains the property of unity decomposition (13). 12 However, this statement does not hold in general in the case of presence of preparation errors E p . This could be easily seen if E p has the form of the energy damping channel.…”

Section: Noise-gate-noise (Ngn) Modelmentioning

confidence: 99%

“…The most effective solution to this problem lies in the modification of the reconstruction procedure in order to include SPAM-errors in the model. [11][12][13] In Sect. 3 we introduce a simple method for such a modification.…”

Section: Introductionmentioning

confidence: 99%

High-fidelity quantum tomography with imperfect measurements

Bantysh

Fastovets

Bogdanov

2019

International Conference on Micro- And Nano-Electronics 2018

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In the current work we address the problem of quantum process tomography (QPT) in the case of imperfect preparation and measurement of the states which are used for QPT. The fuzzy measurements approach which helps us to efficiently take these imperfections into account is considered. However, to implement such a procedure one should have a detailed information about the errors. An approach for obtaining the partial information about them is proposed. It is based on the tomography of the ideal identity gate. This gate could be implemented by performing the measurement right after the initial state preparation. By using the result of the identity gate tomography we were able to significantly improve further QPT procedures. The proposed approach has been tested experimentally on the IBM superconducting quantum processor. As a result, we have obtained an increase in fidelity from 89% to 98% for Hadamard transformation and from 77% to 95% for CNOT gate.

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“…Our fully connected neural net was trained to convert measurements results to the parameters of pure one-qubit state (polar and azimuth angles on the Bloch sphere). The basic tomography methods, developed in our laboratory [21,22] (based on maximum likelihood estimation (MLE)) work better then neural network approach (Fig. 13a).…”

Section: Neurotomographymentioning

confidence: 99%

Machine learning methods in quantum computing theory

Fastovets

Bogdanov

Bantysh

et al. 2019

International Conference on Micro- And Nano-Electronics 2018

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Classical machine learning theory and theory of quantum computations are among of the most rapidly developing scientific areas in our days. In recent years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. The quantum machine learning includes hybrid methods that involve both classical and quantum algorithms. Quantum approaches can be used to analyze quantum states instead of classical data. On other side, quantum algorithms can exponentially improve classical data science algorithm. Here, we show basic ideas of quantum machine learning. We present several new methods that combine classical machine learning algorithms and quantum computing methods. We demonstrate multiclass tree tensor network algorithm, and its approbation on IBM quantum processor. Also, we introduce neural networks approach to quantum tomography problem. Our tomography method allows us to predict quantum state excluding noise influence. Such classical-quantum approach can be applied in various experiments to reveal latent dependence between input data and output measurement results.

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Quantum states tomography with noisy measurement channels

Cited by 13 publications

References 29 publications

Quantum tomography for quantum systems optimization

Quantum tomography for quantum systems optimization

High-fidelity quantum tomography with imperfect measurements

Machine learning methods in quantum computing theory

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