Local-measurement-based quantum state tomography via neural networks

Xin, Tao; Lu, Sirui; Cao, Ningping; Anikeeva, Galit; Lu, Dawei; Li, Jun; Long, Gui‐Lu; Zeng, Bei

doi:10.1038/s41534-019-0222-3

Cited by 65 publications

(30 citation statements)

References 59 publications

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“…Instead, the neural network will, based on the training examples, find by itself an internal representation of the underlying regression problem. We remark that such a setting, inferring the quantum state from the time evolution of observables, is very different from recent works using neural networks for quantum state tomography of systems of many qubits [23][24][25][26], or for filtering experimental data before performing quantum state tomography [27].…”

Section: Modelmentioning

confidence: 91%

Quantum motional state tomography with nonquadratic potentials and neural networks

Weiß

Romero‐Isart

2019

Phys. Rev. Research

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We propose to use the complex quantum dynamics of a massive particle in a nonquadratic potential to reconstruct an initial unknown motional quantum state. We theoretically show that the reconstruction can be efficiently done by measuring the mean value and the variance of the position quantum operator at different instances of time in a quartic potential. We train a neural network to successfully solve this hard regression problem. We discuss the experimental feasibility of the method by analyzing the impact of decoherence and uncertainties in the potential.

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Section: Modelmentioning

confidence: 91%

Quantum motional state tomography with nonquadratic potentials and neural networks

Weiß

Romero‐Isart

2019

Phys. Rev. Research

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“…ANNs are very powerful tools that have been used for numerous applications such as medicine [34][35][36][37], transportation [23,[38][39][40][41], optimization [31,[42][43][44][45], and even quantum physics [46][47][48][49][50] among others.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

Pineda-Jaramillo

2019

DYNA

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In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice.

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“…There exist a wide range of QCVV protocols designed for targeting a variety of noise mechanisms [3,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. These protocols exhibit an interplay between the number of extracted parameters, the expected accuracy, the number of the measured sequences, and the awareness of systematic errors.…”

Section: Introductionmentioning

confidence: 99%

A context-aware gate set tomography characterization of superconducting qubits

Moueddene¹,

Khammassi²,

Feld³

et al. 2021

Preprint

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The efficiency of Quantum Characterisation, Verification, and Validation (QCVV) protocols highly hinges on the agreement between the assumed noise model and the underlying error mechanisms. As a matter of fact, errors in Quantum Processing Units (QPUs) incorporate various aspects of context-dependability which are overlooked by the majority of the commonly used QCVV protocols. As QCVV protocols are indispensable when it comes to characterizing and evaluating quantum operations, there is a serious need for a detailed characterization taking into account such aspects. In this work, we address these shortcomings by designing a context-aware version of the gate set tomography (GST) protocol. Our experiment selection approach is based on a polynomial quantification of the accumulation of errors within the designed circuits. Using simulated QPUs, we show that this technique enables a characterization with an inaccuracy reaching 10 −5 . Furthermore, we use our proposed protocol to experimentally infer context-dependent errors, namely crosstalk and memory effects, in a publicly accessible cloud-based superconducting qubits platform. Our results show that when the GST is upgraded to include such features of context-awareness, a large coherence in the errors is observed. These findings open up possibilities of drastically reducing the errors within the currently demonstrated QPUs.

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Local-measurement-based quantum state tomography via neural networks

Cited by 65 publications

References 59 publications

Quantum motional state tomography with nonquadratic potentials and neural networks

Quantum motional state tomography with nonquadratic potentials and neural networks

A review of Machine Learning (ML) algorithms used for modeling travel mode choice

A context-aware gate set tomography characterization of superconducting qubits

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