Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2211.11655 · Advanced Quantum Technologies

Improved Tomographic Estimates by Specialized Neural Networks

Massimiliano Guarneri, Ilaria Gianani, Marco Barbieri, Andrea Chiuri

Published Jun 27, 2023• 2 citations
arXivHTMLPDF

Abstract

Abstract Characterization of quantum objects, being states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real‐life components. To this end, machine learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating specific physical parameters. Here, it is shown that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. This technique is applied to quantum process tomography for the characterization of several quantum channels. A stable and reliable operation is demonstrated that is achievable by training the network only with simulated data. The obtained results show the viability of this approach as an effective tool based on a completely new paradigm for the employment of NNs operating on classical data produced by quantum systems.

DOI
10.1002/qute.202300027

Related papers

Neural-network-based parameter estimation for quantum detection

Yue Ban, Javier Echanobe, Yongcheng Ding +2 more

Abstract Artificial neural networks (NNs) bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of the neuron connections and biases. In the context of quantum detection schemes, NNs find a natural playground. In particular, in the presence of a target (e.g. an electromagnetic field), a quantum sensor delivers a response, i.e. the input data, which can be subsequently processed by a NN that outputs the target features. In this work we demonstrate that adequately trained NNs enable to characterize a target with (i) minimal knowledge of the underlying physical model (ii) in regimes where the quantum sensor presents complex responses and (iii) under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for 171 Yb + atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.

arXiv:2012.07677•Quantum Science and Technology•Jul 22, 2021•14 citations

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

Marcel Neugebauer, Laurin E. Fischer, Alexander Jäger +4 more

We study the performance of efficient quantum state tomography methods based on neural-network quantum states using measured data from a two-photon experiment. Machine-learning-inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states, i.e., to positive semidefinite density matrices, greatly improves the quality of the reconstructed states but renders the learning procedure more demanding. Including additional, possibly unjustified, constraints, such as assuming pure states, facilitates learning, but also biases the estimator.

arXiv:2007.16185•Physical Review A•Oct 9, 2020•71 citations

Experimental neural network enhanced quantum tomography

Adriano Macarone Palmieri, E. V. Kovlakov, Federico Bianchi +4 more

Abstract Quantum tomography is currently ubiquitous for testing any implementation of a quantum information processing device. Various sophisticated procedures for state and process reconstruction from measured data are well developed and benefit from precise knowledge of the model describing state-preparation-and-measurement (SPAM) apparatus. However, physical models suffer from intrinsic limitations as actual measurement operators and trial states cannot be known precisely. This scenario inevitably leads to SPAM errors degrading reconstruction performance. Here we develop a framework based on machine learning which generally applies to both the tomography and SPAM mitigation problem. We experimentally implement our method. We trained a supervised neural network to filter the experimental data and hence uncovered salient patterns that characterize the measurement probabilities for the original state and the ideal experimental apparatus free from SPAM errors. We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to an SPAM-agnostic protocol with idealized measurements. The average reconstruction fidelity is shown to be enhanced by 10% and 27%, respectively. The presented methods apply to the vast range of quantum experiments which rely on tomography.

arXiv:1904.05902•npj Quantum Information•Feb 7, 2020•175 citations

Method for Noise‐Induced Regularization in Quantum Neural Networks

Viacheslav Kuzmin, W. Somogyi, Ekaterina Aleksandrovna Pankovets +1 more

ABSTRACT In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the general objective is to reduce the amount of noise qubits are subject to, and in algorithm design, a large effort is underway to provide scalable error correction or mitigation techniques. Yet some previous work has indicated that certain classes of quantum algorithms, such as quantum machine learning, may, in fact, be intrinsically robust to or even benefit from the presence of a small amount of noise. Here, we demonstrate that noise levels in quantum hardware can be effectively tuned to enhance the ability of quantum neural networks to generalize data, acting akin to regularisation in classical neural networks. As an example, we consider two regression tasks, where, by tuning the noise level in the circuit, we demonstrated improvement of the validation mean squared error loss. Moreover, we demonstrate the method's effectiveness by numerically simulating QNN training on a realistic model of a noisy superconducting quantum computer.

arXiv:2410.19921•Advanced Quantum Technologies•Nov 28, 2025•3 citations

Estimation of Quantum Channels Using Neural Networks

Hailan Ma, Zhenhong Sun, Shuixin Xiao +2 more

Quantum process tomography is an essential task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this work, we propose a machine learning-based quantum process tomography method to reconstruct the Choi matrices of quantum channels from the measurements of the output states. Numerical results demonstrate that the proposed method exhibits a significant potential to achieve accurate reconstruction of different quantum channels.

IEEE Conference on Decision and Control (CDC)•Dec 13, 2023•1 citation

Efficient quantum state tomography with convolutional neural networks

Tobias Schmale, Moritz Reh, Martin Gärttner

Abstract Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.

arXiv:2109.13776•npj Quantum Information•Sep 23, 2022•76 citations
Abstract Characterization of quantum objects, being states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real‐life components. To this end, machine learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating specific physical parameters. Here, it is shown that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. This technique is applied to quantum process tomography for the characterization of several quantum channels. A stable and reliable operation is demonstrated that is achievable by training the network only with simulated data. The obtained results show the viability of this approach as an effective tool based on a completely new paradigm for the employment of NNs operating on classical data produced by quantum systems.
Abstract Artificial neural networks (NNs) bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of the neuron connections and biases. In the context of quantum detection schemes, NNs find a natural playground. In particular, in the presence of a target (e.g. an electromagnetic field), a quantum sensor delivers a response, i.e. the input data, which can be subsequently processed by a NN that outputs the target features. In this work we demonstrate that adequately trained NNs enable to characterize a target with (i) minimal knowledge of the underlying physical model (ii) in regimes where the quantum sensor presents complex responses and (iii) under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for 171 Yb + atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.
We study the performance of efficient quantum state tomography methods based on neural-network quantum states using measured data from a two-photon experiment. Machine-learning-inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. While the power of these methods has been demonstrated on synthetic data, applications to real experimental data remain scarce. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states, i.e., to positive semidefinite density matrices, greatly improves the quality of the reconstructed states but renders the learning procedure more demanding. Including additional, possibly unjustified, constraints, such as assuming pure states, facilitates learning, but also biases the estimator.
Abstract Quantum tomography is currently ubiquitous for testing any implementation of a quantum information processing device. Various sophisticated procedures for state and process reconstruction from measured data are well developed and benefit from precise knowledge of the model describing state-preparation-and-measurement (SPAM) apparatus. However, physical models suffer from intrinsic limitations as actual measurement operators and trial states cannot be known precisely. This scenario inevitably leads to SPAM errors degrading reconstruction performance. Here we develop a framework based on machine learning which generally applies to both the tomography and SPAM mitigation problem. We experimentally implement our method. We trained a supervised neural network to filter the experimental data and hence uncovered salient patterns that characterize the measurement probabilities for the original state and the ideal experimental apparatus free from SPAM errors. We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to an SPAM-agnostic protocol with idealized measurements. The average reconstruction fidelity is shown to be enhanced by 10% and 27%, respectively. The presented methods apply to the vast range of quantum experiments which rely on tomography.
ABSTRACT In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the general objective is to reduce the amount of noise qubits are subject to, and in algorithm design, a large effort is underway to provide scalable error correction or mitigation techniques. Yet some previous work has indicated that certain classes of quantum algorithms, such as quantum machine learning, may, in fact, be intrinsically robust to or even benefit from the presence of a small amount of noise. Here, we demonstrate that noise levels in quantum hardware can be effectively tuned to enhance the ability of quantum neural networks to generalize data, acting akin to regularisation in classical neural networks. As an example, we consider two regression tasks, where, by tuning the noise level in the circuit, we demonstrated improvement of the validation mean squared error loss. Moreover, we demonstrate the method's effectiveness by numerically simulating QNN training on a realistic model of a noisy superconducting quantum computer.
Abstract Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.