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:2012.07677 · Quantum Science and Technology

Neural-network-based parameter estimation for quantum detection

Yue Ban, Javier Echanobe, Yongcheng Ding, Ricardo Puebla, Jorge Casanova

Published Jul 22, 2021• 14 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1088/2058-9565/ac16ed

Related papers

A neural network assisted 171Yb+ quantum magnetometer

Yan Chen, Yue Ban, Ran He +5 more

Abstract A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of 171 Yb + atomic sensors with adequately trained neural networks enables us to investigate target fields in distinct challenging scenarios. In particular, we characterize radio frequency (RF) fields in the presence of large shot noise, including the limit case of continuous data acquisition via single-shot measurements. Furthermore, by incorporating neural networks we significantly extend the working regime of atomic magnetometers into scenarios in which the RF driving induces responses beyond their standard harmonic behavior. Our results indicate the benefits to integrate neural networks at the data processing stage of general quantum sensing tasks to decipher the information contained in the sensor responses.

arXiv:2203.05849•npj Quantum Information•Dec 29, 2022•12 citations

Improved Tomographic Estimates by Specialized Neural Networks

Massimiliano Guarneri, Ilaria Gianani, Marco Barbieri +1 more

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.

arXiv:2211.11655•Advanced Quantum Technologies•Jun 27, 2023•2 citations

Getting large-scale quantum neural networks ready for quantum hardware

Mario Boneberg, Simon Kochsiek, Igor Lesanovsky

Quantum neural networks generalize classical artificial neural networks into the quantum domain. They are formulated as parameterized quantum circuits which are optimized by measuring and minimizing a suitably chosen loss function. The core challenge in understanding, implementing and ultimately using quantum neural networks is that they represent many-body systems with an exponentially large Hilbert space, in combination with a large parameter search space. Moreover, noise -- which is inherent to any quantum measurement -- sets practical limits for the estimation of training loss. Here, we study physics-informed large-scale quantum neural networks that are trained through a finite number of noisy loss function measurements. We show that this architecture permits the construction of nontrivial decision boundaries that enable the classification of quantum states through measuring an order parameter. Our approach can directly process quantum data that is output from quantum simulators and computers and is well suited for implementation on current hardware. Moreover, owed to a close link between the neural network dynamics and the evolution of Markovian open many-body quantum systems, one may expect a certain robustness to noise, which is ubiquitous in the current NISQ era.

quant-pharXiv:2604.24886•Apr 29, 2026•0 citations

The utilization of photonic neural networks in the domain of quantum sensing

Alexander Y. Grebeshkov

Quantum sensing is a quantum-based process that concerns the development of principles, methods, and devices using superposition of quantum states. Quantum sensors find application in a variety of contexts, serving as a base sensitive element in high-precision synchronization systems, functioning as a key part of gravitometers, accelerometers, and electric and magnetic field sensors. They are also being utilized in quantum metrology and lidar technology. The data generated by quantum sensors can be employed for machine learning purposes. A discussed approach involves the direct utilization of quantum sensor data as input parameters in machine learning models based on photonic neural networks. It has been determined that there is an actual problem of integration for results from quantum sensing and photonic neural networks features. A photonic neural network is discussed as a hardware-implemented algorithm, that belongs to the category of artificial intelligence algorithms in general. The hardware-implemented algorithm reveals a complex interconnection between physical processes at the photonic-based neurons. This paper provides an analysis of quantum sensing in general. It discusses the main type of photonic neural networks and their applications for quantum sensing. The paper considers an integration scheme for photonic neural networks for key points control of quantum sensing processes. Furthermore, the paper presents a proposed functional scheme for quantum sensing control based on the regulator with photonic neural networks.

Optical Technologies for Telecommunications (OTT)•Aug 14, 2025•1 citation

Training an Artificial Neural Network Using Qubits as Artificial Neurons: A Quantum Computing Approach

Avinash Chalumuri, Raghavendra Kune, B. S. B. S. Manoj

Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.

Procedia Computer Science•Jan 1, 2020•18 citations

A Quantum Approach to Neural Networks

Prof. Ahlam Ansari, Ashhar Shaikh, Faraz Shaikh +1 more

Artificial neural networks, usually just called neural networks, computing systems indefinitely inspired by the biological neural networks and they are extensive in both research as well as industry. It is critical to design quantum Neural Networks for complete quantum learning tasks. In this project, we suggest a computational neural network model based on principles of quantum mechanics which form a quantum feed-forward neural network proficient in universal quantum computation. This structure takes input from one layer of qubits and drives that input onto another layer of qubits. This layer of qubits evaluates this information and drives on the output to the next layer. Eventually, the path leads to the final layer of qubits. The layers do not have to be of the same breadth, meaning they need not have the same number of qubits as the layer before and/or after it. This assembly is trained on which path to take identical to classical ANN. The intended project can be compiled by the subsequent points provided here: 1. The expert training of the quantum neural network utilizing the fidelity as a cost function, providing both conventional and efficient quantum implementations. 2. Use of methods that enable quick optimization with reduced memory requirements. 3. Benchmarking our proposal for the quantum task of learning an unknown unitary and find extraordinary generality and a remarkable sturdiness to noisy training data.

International Journal of Advanced Research in Science Communication and Technology•May 12, 2021•0 citations
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.
Abstract A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of 171 Yb + atomic sensors with adequately trained neural networks enables us to investigate target fields in distinct challenging scenarios. In particular, we characterize radio frequency (RF) fields in the presence of large shot noise, including the limit case of continuous data acquisition via single-shot measurements. Furthermore, by incorporating neural networks we significantly extend the working regime of atomic magnetometers into scenarios in which the RF driving induces responses beyond their standard harmonic behavior. Our results indicate the benefits to integrate neural networks at the data processing stage of general quantum sensing tasks to decipher the information contained in the sensor responses.
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.
Quantum neural networks generalize classical artificial neural networks into the quantum domain. They are formulated as parameterized quantum circuits which are optimized by measuring and minimizing a suitably chosen loss function. The core challenge in understanding, implementing and ultimately using quantum neural networks is that they represent many-body systems with an exponentially large Hilbert space, in combination with a large parameter search space. Moreover, noise -- which is inherent to any quantum measurement -- sets practical limits for the estimation of training loss. Here, we study physics-informed large-scale quantum neural networks that are trained through a finite number of noisy loss function measurements. We show that this architecture permits the construction of nontrivial decision boundaries that enable the classification of quantum states through measuring an order parameter. Our approach can directly process quantum data that is output from quantum simulators and computers and is well suited for implementation on current hardware. Moreover, owed to a close link between the neural network dynamics and the evolution of Markovian open many-body quantum systems, one may expect a certain robustness to noise, which is ubiquitous in the current NISQ era.
Quantum sensing is a quantum-based process that concerns the development of principles, methods, and devices using superposition of quantum states. Quantum sensors find application in a variety of contexts, serving as a base sensitive element in high-precision synchronization systems, functioning as a key part of gravitometers, accelerometers, and electric and magnetic field sensors. They are also being utilized in quantum metrology and lidar technology. The data generated by quantum sensors can be employed for machine learning purposes. A discussed approach involves the direct utilization of quantum sensor data as input parameters in machine learning models based on photonic neural networks. It has been determined that there is an actual problem of integration for results from quantum sensing and photonic neural networks features. A photonic neural network is discussed as a hardware-implemented algorithm, that belongs to the category of artificial intelligence algorithms in general. The hardware-implemented algorithm reveals a complex interconnection between physical processes at the photonic-based neurons. This paper provides an analysis of quantum sensing in general. It discusses the main type of photonic neural networks and their applications for quantum sensing. The paper considers an integration scheme for photonic neural networks for key points control of quantum sensing processes. Furthermore, the paper presents a proposed functional scheme for quantum sensing control based on the regulator with photonic neural networks.
Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.
Artificial neural networks, usually just called neural networks, computing systems indefinitely inspired by the biological neural networks and they are extensive in both research as well as industry. It is critical to design quantum Neural Networks for complete quantum learning tasks. In this project, we suggest a computational neural network model based on principles of quantum mechanics which form a quantum feed-forward neural network proficient in universal quantum computation. This structure takes input from one layer of qubits and drives that input onto another layer of qubits. This layer of qubits evaluates this information and drives on the output to the next layer. Eventually, the path leads to the final layer of qubits. The layers do not have to be of the same breadth, meaning they need not have the same number of qubits as the layer before and/or after it. This assembly is trained on which path to take identical to classical ANN. The intended project can be compiled by the subsequent points provided here: 1. The expert training of the quantum neural network utilizing the fidelity as a cost function, providing both conventional and efficient quantum implementations. 2. Use of methods that enable quick optimization with reduced memory requirements. 3. Benchmarking our proposal for the quantum task of learning an unknown unitary and find extraordinary generality and a remarkable sturdiness to noisy training data.