In various applications of optical communications, such as quantum key distribution (QKD) systems, the task of polarization control in optical fiber arises. A polarization controller (PC) on the receiver side is used to solve this problem. We investigate two machine learning approaches to polarization control: supervised learning (SL) and reinforcement learning (RL). We generalized analytical solution of the polarization control problem using SL approach. Supervised learning algorithm was trained on simulations and then was validated in experimental setup. We compared supervised approach with analytical solution. The advantage of RL approach is based on the capability of approximating nonlinear effects related to the imperfections of detection process. For the effective operation of polarization control algorithms, knowledge of the parameters of the PC is required. Reinforcement learning agent also eliminates control hardware calibration procedures, since the agent recognizes necessary system parameters based on the experience of interacting with the environment. RL agent was firstly pretrained on simulations and then was fine-tuned on the practical QKD system. We demonstrated that RL agent’s tuning provides the 10% quantum bit error rate on our setup and converges in 10 steps.
In various applications of optical communications, such as quantum key distribution (QKD) systems, the task of polarization control in optical fiber arises. A polarization controller (PC) on the receiver side is used to solve this problem. We investigate two machine learning approaches to polarization control: supervised learning (SL) and reinforcement learning (RL). We generalized analytical solution of the polarization control problem using SL approach. Supervised learning algorithm was trained on simulations and then was validated in experimental setup. We compared supervised approach with analytical solution. The advantage of RL approach is based on the capability of approximating nonlinear effects related to the imperfections of detection process. For the effective operation of polarization control algorithms, knowledge of the parameters of the PC is required. Reinforcement learning agent also eliminates control hardware calibration procedures, since the agent recognizes necessary system parameters based on the experience of interacting with the environment. RL agent was firstly pretrained on simulations and then was fine-tuned on the practical QKD system. We demonstrated that RL agent’s tuning provides the 10% quantum bit error rate on our setup and converges in 10 steps.
Abstract In various applications of optical communications, such as quantum key distribution (QKD) systems, the task of polarization control in optical fiber arises. A polarization controller (PC) on the receiver side is used to solve this problem. In our work, we investigate two machine learning approaches to polarization control: supervised learning (SL) and reinforcement learning (RL). We generalised analytical solution of the polarization control problem using SL-approach. Supervised learning algorithm was trained on simulations and then was validated in experimental setup. We compared supervised approach with analytical solution. The advantage of RL approach based on the capability of approximating nonlinear effects related to the imperfections of detection process. For the effective operation of polarization control algorithms, knowledge of the parameters of the PC is required. Reinforcement learning agent also eliminates control hardware calibration procedures, since the agent recognize necessary system parameters based on the experience of interacting with the environment. RL-agent was firstly pretrained on simulations and then was fine-tuned on the practical QKD system. We demonstrated that RL-agent’s tuning provides the 10% quantum bit error rate (QBER) on our setup, and converges in 10 steps.
The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.
Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.
Quantum Key Distribution (QKD) systems represent a cornerstone of secure quantum communication, relying on post-processing procedures to generate shared keys. Among these, error correction (EC) is a critical and computationally demanding step, where the estimation of the Quantum Bit Error Rate (QBER) plays a pivotal role in optimizing code rates for LDPC-based protocols. Traditional QBER estimation methods, grounded in physical models and statistics, often struggle with real-world variability and outliers, leading to suboptimal performance. In this study, we propose a novel machine learning framework for QBER forecasting, reframing the problem as a time-series prediction task. Leveraging real-world data from quantum transmitters, we evaluated a suite of models, including gradient boosting with autoregressive components, exponential smoothing hybrids, and deep neural networks, to predict QBER with greater precision. Through extensive experiments, our approach demonstrates significant improvements in error correction efficiency compared to traditional methods, particularly under challenging conditions like system calibration drifts and non-stationary signal behavior. The results highlight the potential of lightweight and interpretable machine learning models, such as gradient-boosted exponential smoothing, to outperform complex neural architectures in real-world scenarios. This research contributes to the advancement of quantum cryptography by enabling faster and more robust key generation, with reduced information disclosure during reconciliation. Our findings open new avenues for integrating machine learning into QKD systems for adaptive and scalable quantum communication.
Polar molecules are a promising platform for achieving scalable quantum information processing because of their long-range electric dipole-dipole interactions. Here, we take the coupled ultracold CaF molecules in an external electric field with gradient as qubits and concentrate on the creation of intermolecular entanglement with the method of deep reinforcement learning (RL). After sufficient training episodes, the educated RL agents can discover optimal time-dependent control fields that steer the molecular systems from separate states to two-qubit and three-qubit entangled states with high fidelities. We analyze the fidelities and the negativities (characterizing entanglement) of the generated states as a function of training episodes. Moreover, we present the population dynamics of the molecular systems under the influence of control fields discovered by the agents. Compared with the schemes for creating molecular entangled states based on optimal control theory, some conditions (e.g., molecular spacing and electric field gradient) adopted in this work are more feasible in the experiment. Our results demonstrate the potential of machine learning to effectively solve quantum control problems in polar molecular systems.
The recent advances in Machine Learning hold great promises for the field of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, which is typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system the agent is interacting with, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system and only some parameters of the evolution or of the initial state are unknown. We present here a general Machine Learning technique that can optimize the precision of quantum sensors, and in doing so it exploits the knowledge we have on the system. We have developed a Python package to automate a broad class of optimizations that can be found in the tasks of quantum parameter estimation, quantum metrology and quantum hypothesis testing. What the agent is learning here is an optimal adaptive strategy, that, on the basis of the previous outcomes, decides the next measurements to perform. It works both for Bayesian estimation and for frequentist estimation. The user is required to implement the physics of the system to be studied and state which parameters in the experiment are controllable and which are unknowns. The functions of the library allow then to easily train a neural network agent for optimizing the precision of the sensor, by simulating the experiment. We have explored some applications of this technique to magnetometry on NV centers (both DC and AC), to state discrimination in quantum optics and to phase estimation. So far, we were able to certify better results than the current state-of-the-art controls for many examples. The Machine Learning technique developed here can be applied in all those scenarios where the quantum system is well characterized and relatively simple and small. In these cases, we can squeeze every last bit of information from a quantum sensor by controlling it with a neural network appropriately trained.