medical-6

Quantum State Tomography

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Quantum tomography benchmarking

B. Bantysh, A. Chernyavskiy, Y. Bogdanov•Wed Dec 30 2020

This study has developed a general methodology for comparing quantum-state tomography methods, and developed a software library (in MATLAB and Python) that makes it easy to analyze any QT method implementation through a series of numerical experiments.

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Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging

C. Tsai, Hao-Chung Cheng, Yen-Huan Li•Sat Nov 04 2023

This work proposes a stochastic first-order algorithm named B-sample stochastic dual averaging with the logarithmic barrier, which improves on the time complexities of existing stochastic first-order methods by a factor of d^{\omega-2}$ and those of batch methods by a factor of d^2, where $\omega$ denotes the matrix multiplication exponent.

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Variational Quantum Circuits for Quantum State Tomography

Yong Liu, Dongyang Wang, Shichuan Xue, Anqi Huang, Xiang Fu, X. Qiang, P. Xu, Heliang Huang, Mingtang Deng, Chu Guo, Xuejun Yang, Junjie Wu•Sun Dec 15 2019

This work employs quantum machine learning for state tomography by maximizing the fidelity between the output of a variational quantum circuit and this state, which grows linearly with the number of qubits and the circuit depth.

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Self-concordant analysis of Frank-Wolfe algorithms

P. Dvurechensky, Shimrit Shtern, Mathias Staudigl, P. Ostroukhov, K. Safin•Mon Feb 10 2020

The theory of SC functions is used to provide a new adaptive step size for FW methods and prove global convergence rate O(1/k) after k iterations, and if the problem admits a stronger local linear minimization oracle, a novel FW method with linear convergence rate for SC functions.

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Quantum State Tomography with Conditional Generative Adversarial Networks

Shahnawaz Ahmed, F. Nori, A. F. Kockum, C. Munoz•Thu Aug 06 2020

This work applies conditional generative adversarial networks (CGANs) to QST and demonstrates that the QST-CGAN reconstructs optical quantum states with high fidelity, using orders of magnitude fewer iterative steps, and less data, than both accelerated projected-gradient-based and iterative maximum-likelihood estimation.

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Classification and reconstruction of optical quantum states with deep neural networks

Shahnawaz Ahmed, C. Muñoz, F. Nori, A. F. Kockum•Wed Dec 02 2020

Deep-neural-network-based techniques are applied to quantum state classification and reconstruction and it is shown that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection.

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Spin qudit tomography and state reconstruction error

M. Perlin, D. Barberena, A. Rey•Thu Dec 10 2020

This work considers the task of performing quantum state tomography on a d-level spin qudit, using only measurements of spin projection onto different quantization axes, and motivates a simple randomized tomography protocol, for which it is found that using more measurement axes can yield substantial benefits that plateau after $r\approx3d$.

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NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

Filippo Vicentini, Damian Hofmann, A. Szab'o, Dian Wu, Christopher Roth, Clemens Giuliani, Gabriel Pescia, J. Nys, Vladimir Vargas-Calderón, Nikita Astrakhantsev, Giuseppe Carleo•Sun Dec 19 2021

The most significant new feature is the possibility to define arbitrary neural network ansätze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation.

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Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography

C. Tsai, Hao-Chung Cheng, Yen-Huan Li•Tue Nov 22 2022

An algorithm called stochastic mirror descent with the Burg entropy is proposed, which is currently the computationally fastest stoChastic first-order method for maximum-likelihood quantum state tomography.

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MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-Classification

Jindi Wu, Tianjie Hu, Qun Li•Thu Jul 20 2023

This paper proposes an efficient quantum multi-classifier called MORE, which stands for measurement and correlation based variational quantummulti- classifier, and implements this approach using the Qiskit Python library and evaluates it through extensive experiments on both noise-free and noisy quantum systems.

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