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:2504.12810 · Quantum Science and Technology

Learning Time-Varying Gaussian Quantum Lossy Channels

Angela Rosy Morgillo, Stefano Mancini, Massimiliano Federico Sacchi, Chiara Macchiavello

Published Aug 1, 2025• 0 citations
arXivHTMLPDF

Abstract

Abstract Time-varying quantum channels are essential for modeling realistic quantum systems with evolving noise properties. Here, we consider Gaussian lossy channels varying from one use to another and we employ neural networks to classify, regress, and forecast the behavior of these channels from their Choi-Jamiołkowski states. The networks achieve at least 87% of accuracy in distinguishing between non-Markovian, Markovian, memoryless, compound, and deterministic channels. In regression tasks, the model accurately reconstructs the loss parameter sequences, and in forecasting, it predicts future values, with improved performance as the memory parameter approaches 1 for Markovian channels. These results demonstrate the potential of neural networks in characterizing and predicting the dynamics of quantum channels.

DOI
10.1088/2058-9565/adf6d2

Related papers

Detecting Markovianity of quantum processes via recurrent neural networks

Angela Rosy Morgillo, Massimiliano Federico Sacchi, Chiara Macchiavello

Abstract We present a novel methodology utilizing recurrent neural networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from Choi states. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios, including dephasing and Pauli channels in an arbitrary basis, generalized amplitude damping dynamics, and even in the presence of noise. Additionally, the developed model shows efficient forecasting capabilities for the analyzed time series data. These results suggest the potential of RNNs in discerning and predicting the Markovian nature of quantum processes.

arXiv:2406.07226•Quantum Machine Intelligence•Mar 27, 2025•1 citation

Quantum state reconstruction in a noisy environment via deep learning

Angela Rosy Morgillo, Stefano Mangini, Marco Piastra +1 more

Abstract Quantum noise is currently limiting efficient quantum information processing and computation, impacting on the fidelity and reliability of quantum states. In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical feed-forward neural networks. By framing reconstruction as a regression problem, we show how such an approach can be used to recover with fidelities exceeding 99% the noiseless density matrices of quantum states of up to three qubits undergoing noisy evolution, and we test its performance with both single-qubit (bit-flip, phase-flip, depolarizing, and amplitude damping) and two-qubit quantum channels (correlated amplitude damping). Furthermore, a critical aspect of our investigation involves also a comprehensive comparison between mean squared error and infidelity as loss functions. Our findings reveal that these two metrics yield comparable results in the context of state reconstruction. Moreover, we also consider the task of distinguishing between different quantum noisy channels, and show how a neural network-based classifier is able to solve such a classification problem with perfect accuracy.

arXiv:2309.11949•Quantum Machine Intelligence•Jul 2, 2024•8 citations

Quantum Neural Networks with Non-Markovian Dynamics

Jincheng Zhang

This paper investigates the potential of quantum neural networks (QNNs) enhanced by non-Markovian dynamics. Traditional Markovian QNNs suffer from limitations in capturing long-range dependencies, a crucial aspect in complex data processing. We propose a novel QNN architecture that incorporates quantum memory and entanglement to facilitate non-Markovian information processing. This approach allows the network to retain and utilize the entire past history of the input, fundamentally altering the dynamics of information flow. Through theoretical analysis and mathematical formulations, we demonstrate how this architecture can significantly improve the network's ability to learn and represent complex patterns, particularly those exhibiting long-range correlations. Specifically, we introduce a modified update rule leveraging the concept of quantum memory and entanglement, expressed as: $ \Delta w_{ij} = \alpha \cdot E[\rho(t)|x_1, x_2, ..., x_K] \cdot \langle \psi_i | \rho(t) | \psi_j \rangle - (1 - \alpha) \cdot w_{ij}$ Where: * $ \Delta w_{ij}$ is the change in the synaptic weight between neurons *i* and *j*. * $\alpha$ is the learning rate. * $E[\rho(t)|x_1, x_2, ..., x_K]$ represents the expected state of the quantum memory at time *t*, given the entire input sequence $x_1, x_2, ..., x_K$. * $\rho(t)$ is the density matrix representing the quantum state at time *t*. * $\langle \psi_i | \rho(t) | \psi_j \rangle$ is the inner product between the quantum states $\psi_i$ and $\psi_j$ within the density matrix $\rho(t)$. * $w_{ij}$ is the current synaptic weight between neurons *i* and *j*. The core of our innovation lies in the utilization of quantum memory, modeled as a quantum state $\rho(t)$, which retains information about the entire past sequence. The entanglement between neurons further enables correlated updates, bypassing the limitations of Markovian models that only consider immediate dependencies. We explore the impact of varying the strength of entanglement and the capacity of the quantum memory on the network's performance using mathematical models. The results suggest a significant improvement in accuracy and convergence speed compared to standard Markovian QNNs when dealing with datasets exhibiting long-range dependencies. Further investigation into the scalability and practical implementation of this architecture is warranted.

•Aug 29, 2026•0 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

Quantum Neural Network for Time Series Forecasting: Harnessing Quantum Computing's Potential in Predictive Modeling

D. Balakrishnan, Umasree Mariappan, Pagadala Geetha Manikanta Raghavendra +3 more

Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications.

International Conference on Futuristic Technologies (INCOFT)•Nov 24, 2023•8 citations

Quantum Machine Learning in Finance: Time Series Forecasting

Dimitrios Emmanoulopoulos, Sofija Dimoska

We explore the efficacy of the novel use of parametrised quantum circuits (PQCs) as quantum neural networks (QNNs) for forecasting time series signals with simulated quantum forward propagation. The temporal signals consist of several sinusoidal components (deterministic signal), blended together with trends and additive noise. The performance of the PQCs is compared against that of classical bidirectional long short-term memory (BiLSTM) neural networks. Our results show that for time series signals consisting of small amplitude noise variations (up to 40 per cent of the amplitude of the deterministic signal) PQCs, with only a few parameters, perform similar to classical BiLSTM networks, with thousands of parameters, and outperform them for signals with higher amplitude noise variations. Thus, QNNs can be used effectively to model time series having, at the same time, the significant advantage of being trained significantly faster than a classical machine learning model in a quantum computer.

arXiv:2202.00599•Feb 1, 2022•33 citations
Abstract Time-varying quantum channels are essential for modeling realistic quantum systems with evolving noise properties. Here, we consider Gaussian lossy channels varying from one use to another and we employ neural networks to classify, regress, and forecast the behavior of these channels from their Choi-Jamiołkowski states. The networks achieve at least 87% of accuracy in distinguishing between non-Markovian, Markovian, memoryless, compound, and deterministic channels. In regression tasks, the model accurately reconstructs the loss parameter sequences, and in forecasting, it predicts future values, with improved performance as the memory parameter approaches 1 for Markovian channels. These results demonstrate the potential of neural networks in characterizing and predicting the dynamics of quantum channels.
Abstract We present a novel methodology utilizing recurrent neural networks (RNNs) to classify Markovian and non-Markovian quantum processes, leveraging time series data derived from Choi states. The model exhibits exceptional accuracy, surpassing 95%, across diverse scenarios, including dephasing and Pauli channels in an arbitrary basis, generalized amplitude damping dynamics, and even in the presence of noise. Additionally, the developed model shows efficient forecasting capabilities for the analyzed time series data. These results suggest the potential of RNNs in discerning and predicting the Markovian nature of quantum processes.
Abstract Quantum noise is currently limiting efficient quantum information processing and computation, impacting on the fidelity and reliability of quantum states. In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical feed-forward neural networks. By framing reconstruction as a regression problem, we show how such an approach can be used to recover with fidelities exceeding 99% the noiseless density matrices of quantum states of up to three qubits undergoing noisy evolution, and we test its performance with both single-qubit (bit-flip, phase-flip, depolarizing, and amplitude damping) and two-qubit quantum channels (correlated amplitude damping). Furthermore, a critical aspect of our investigation involves also a comprehensive comparison between mean squared error and infidelity as loss functions. Our findings reveal that these two metrics yield comparable results in the context of state reconstruction. Moreover, we also consider the task of distinguishing between different quantum noisy channels, and show how a neural network-based classifier is able to solve such a classification problem with perfect accuracy.
This paper investigates the potential of quantum neural networks (QNNs) enhanced by non-Markovian dynamics. Traditional Markovian QNNs suffer from limitations in capturing long-range dependencies, a crucial aspect in complex data processing. We propose a novel QNN architecture that incorporates quantum memory and entanglement to facilitate non-Markovian information processing. This approach allows the network to retain and utilize the entire past history of the input, fundamentally altering the dynamics of information flow. Through theoretical analysis and mathematical formulations, we demonstrate how this architecture can significantly improve the network's ability to learn and represent complex patterns, particularly those exhibiting long-range correlations. Specifically, we introduce a modified update rule leveraging the concept of quantum memory and entanglement, expressed as: $ \Delta w_{ij} = \alpha \cdot E[\rho(t)|x_1, x_2, ..., x_K] \cdot \langle \psi_i | \rho(t) | \psi_j \rangle - (1 - \alpha) \cdot w_{ij}$ Where: * $ \Delta w_{ij}$ is the change in the synaptic weight between neurons *i* and *j*. * α\alphaα is the learning rate. * E[ρ(t)∣x1,x2,...,xK]E[\rho(t)|x_1, x_2, ..., x_K]E[ρ(t)∣x1​,x2​,...,xK​] represents the expected state of the quantum memory at time *t*, given the entire input sequence x1,x2,...,xKx_1, x_2, ..., x_Kx1​,x2​,...,xK​. * ρ(t)\rho(t)ρ(t) is the density matrix representing the quantum state at time *t*. * ⟨ψi∣ρ(t)∣ψj⟩\langle \psi_i | \rho(t) | \psi_j \rangle⟨ψi​∣ρ(t)∣ψj​⟩ is the inner product between the quantum states ψi\psi_iψi​ and ψj\psi_jψj​ within the density matrix ρ(t)\rho(t)ρ(t). * wijw_{ij}wij​ is the current synaptic weight between neurons *i* and *j*. The core of our innovation lies in the utilization of quantum memory, modeled as a quantum state ρ(t)\rho(t)ρ(t), which retains information about the entire past sequence. The entanglement between neurons further enables correlated updates, bypassing the limitations of Markovian models that only consider immediate dependencies. We explore the impact of varying the strength of entanglement and the capacity of the quantum memory on the network's performance using mathematical models. The results suggest a significant improvement in accuracy and convergence speed compared to standard Markovian QNNs when dealing with datasets exhibiting long-range dependencies. Further investigation into the scalability and practical implementation of this architecture is warranted.
Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications.
We explore the efficacy of the novel use of parametrised quantum circuits (PQCs) as quantum neural networks (QNNs) for forecasting time series signals with simulated quantum forward propagation. The temporal signals consist of several sinusoidal components (deterministic signal), blended together with trends and additive noise. The performance of the PQCs is compared against that of classical bidirectional long short-term memory (BiLSTM) neural networks. Our results show that for time series signals consisting of small amplitude noise variations (up to 40 per cent of the amplitude of the deterministic signal) PQCs, with only a few parameters, perform similar to classical BiLSTM networks, with thousands of parameters, and outperform them for signals with higher amplitude noise variations. Thus, QNNs can be used effectively to model time series having, at the same time, the significant advantage of being trained significantly faster than a classical machine learning model in a quantum computer.