Neural-network quantum state tomography in a two-qubit experiment

Neugebauer, Marcel; Fischer, Laurin E.; Jäger, Alexander; Czischek, Stefanie; Jochim, Selim; Weidemüller, Matthias; Gärttner, Martin

doi:10.1103/physreva.102.042604

Cited by 67 publications

(39 citation statements)

References 38 publications

(48 reference statements)

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“…The technique extracts information about the quantum state of a system. Inverse techniques offer significant improvement over the direct quantum tomography approach [4][5][6][7][8][9][10][11][12][13][14]. Yet, very little work was done to experimentally demonstrate image enhancement and the superiority of the inverse techniques.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we compare images of birefringent samples obtained from the reconstructed density matrix of entangled states between two techniques, direct quantum state tomography (DQST) and inverse numerical optimization quantum state tomography (IQST). Both are linearly related to a set of measured coincidence rates [4][5][6][7][8][9][10][11][12][13][14]. A number of inverse algorithms were proposed with faster convergence time [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Polarization Sensitive Imaging with Qubits

Sukharenko

Dorsinville

2022

Applied Sciences

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We compare reconstructed quantum state images of a birefringent sample using direct quantum state tomography and inverse numerical optimization technique. Qubits are used to characterize birefringence in a flat transparent plastic sample by means of polarization sensitive measurement using density matrices of two-level quantum entangled photons. Pairs of entangled photons are generated in a type-II nonlinear crystal. About half of the generated photons interact with a birefringent sample, and coincidence counts are recorded. Coincidence rates of entangled photons are measured for a set of sixteen polarization states. Tomographic and inverse numerical techniques are used to reconstruct the density matrix, the degree of entanglement, and concurrence for each pixel of the investigated sample. An inverse numerical optimization technique is used to obtain a density matrix from measured coincidence counts with the maximum probability. Presented results highlight the experimental noise reduction, greater density matrix estimation, and overall image enhancement. The outcome of the entanglement distillation through projective measurements is a superposition of Bell states with different amplitudes. These changes are used to characterize the birefringence of a 3M tape. Well-defined concurrence and entanglement images of the birefringence are presented. Our results show that inverse numerical techniques improve overall image quality and detail resolution. The technique described in this work has many potential applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Polarization Sensitive Imaging with Qubits

Sukharenko

Dorsinville

2022

Applied Sciences

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“…These are capable of handling large data sets and of solving tasks for which they have not been explicitly programmed; applications range from stock-price predictions [11,12] to the analysis of medical diseases [13]. In the past few years, several applications of machine-learning methods in the quantum domain have been reported [14][15][16], including state and unitary tomography [17][18][19][20][21][22][23][24][25], the design of quantum experiments [26][27][28][29][30][31][32], the validation of quantum technology [33][34][35], the identification of quantum features [36,37], and the adaptive control of quantum devices [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Also, photonic platforms can be exploited for the realization of machine-learning protocols [55,56]...…”

Section: Introductionmentioning

confidence: 99%

Calibration of Multiparameter Sensors via Machine Learning at the Single-Photon Level

et al. 2021

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Calibration of sensors is a fundamental step in validating their operation. This can be a demanding task, as it relies on acquiring detailed modeling of the device, which can be aggravated by its possible dependence upon multiple parameters. Machine learning provides a handy solution to this issue, operating a mapping between the parameters and the device response, without needing additional specific information on its functioning. Here, we demonstrate the application of a neural-network-based algorithm for the calibration of integrated photonic devices depending on two parameters. We show that a reliable characterization is achievable by carefully selecting an appropriate network training strategy. These results show the viability of this approach as an effective tool for the multiparameter calibration of sensors characterized by complex transduction functions. Furthermore, the approach is proven to be versatile and promising for mass production, as the same neural network is able to calibrate different devices that have the same structure.

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“…Machine learning can quickly make predictions on given new data with reasonable accuracy by learning from a large amount of existing data. Recently, machine learning has been applied in quantum physics, such as entanglement [36], nonlocality [37,38], phase transitions identification [39,40], quantum state tomography [41], Markovianity [42] and steering [31,43]. Compared with the SDP method, machine learning requires much less resources and can quickly determine whether a quantum state is steerable.…”

Section: Introductionmentioning

confidence: 99%

Detected the steerability bounds of the generalized Werner states via BackPropagation neural network

Zhang,

He,

Zhang

et al. 2021

Preprint

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We use error BackPropagation (BP) neural network to determine whether an arbitrary two-qubit quantum state is steerable and optimize the steerability bounds of the generalized Werner state. The results show that no matter how we choose the features for the quantum states, we can use the BP neural network to construct several models to realize high-performance quantum steering classifiers compared with the support vector machine (SVM). In addition, we predict the steerability bounds of the generalized Werner states by using the classifiers which are newly constructed by the BP neural network, that is, the predicted steerability bounds are closer to the theoretical bounds. In particular, high-performance classifiers with partial information of the quantum states which we only need to measure in three fixed measurement directions are obtained.

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Neural-network quantum state tomography in a two-qubit experiment

Cited by 67 publications

References 38 publications

Polarization Sensitive Imaging with Qubits

Polarization Sensitive Imaging with Qubits

Calibration of Multiparameter Sensors via Machine Learning at the Single-Photon Level

Detected the steerability bounds of the generalized Werner states via BackPropagation neural network

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