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Learning control of quantum systems

Nettet20. mai 2024 · In recent years, some experimental studies and simulations show that reinforcement learning (RL) is an effective learning control approach for solving certain quantum control problems. In this paper, Q-learning with different exploration strategies (e.g., ε-greedy and Softmax), probabilistic Q-learning (PQL) and quantum … NettetHis main research interests are in robust control theory, quantum control theory and stochastic control theory. Ian Petersen was elected IFAC Council Member for the 2014-2024 and 2024-2024 Trienniums. He was also elected to be a member of the IEEE Control Systems Society Board of Governors for the periods 2011-2013 and 2015-2024.

Open quantum system control based on reinforcement learning

Nettet11. apr. 2024 · Solving the ground state and the ground-state properties of quantum many-body systems is generically a hard task for classical algorithms. For a family of ... Download a PDF of the paper titled Exponentially Improved Efficient Machine Learning for Quantum Many-body States with Provable Guarantees, by Yanming Che and Clemens ... NettetLearning control of quantum systems using frequency-domain optimization algorithms Daoyi Dong, Chuan-Cun Shu, Jiangchao Chen, Xi Xing, Hailan Ma, Yu Guo, Herschel Rabitz Abstract—We investigate two classes of quantum control problems by using frequency-domain optimization algorithms in the context of ultrafast laser control of … bunnys info https://thecircuit-collective.com

Quantum robot--structure,algorithms and applications - arXiv

Nettet11. apr. 2024 · We compute the ground-state properties of fully polarized, trapped, one-dimensional fermionic systems interacting through a gaussian potential. We use an antisymmetric artificial neural network, or neural quantum state, as an ansatz for the wavefunction and use machine learning techniques to variationally minimize the … Nettet12. apr. 2024 · At the atomic and subatomic scales of matter, classical laws of nature lose control and quantum mechanics take over. Discoveries of new quantum phenomena and materials, such as quantum entanglement and topological systems, promise to deliver groundbreaking technologies. New extremely efficient quantum computers and … Nettet24. mai 2024 · Abstract. Learning the Hamiltonian that describes interactions in a quantum system is an important task in both condensed-matter physics and the verification of quantum technologies. Its classical ... bunny sings i love you

Control of Quantum Systems Wiley Online Books

Category:Model-Free Quantum Control with Reinforcement Learning

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Learning control of quantum systems

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Nettet1. aug. 2008 · Reinforcement Learning has quite recently been proposed for the control of quantum systems [30, 31,32,33,34,35,36,37], along with a strictly quantum Reinforcement Learning implementation [35,38]. NettetA Neural Network Approach to Sampling Based Learning Control for Quantum System with Uncertainty. Commun. Comput. Phys., 30 (2024), pp. 1453-1473. Robust control design for quantum systems with uncertainty is a key task for developing practical quantum technology. In this paper, we apply neural networks to learn the …

Learning control of quantum systems

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Nettet11. apr. 2024 · Download PDF Abstract: We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate environmental noise on qubits. We illustrate the approach with application to the protection of quantum gates performed on i) a central spin qubit … Nettet1. jan. 2024 · Evolutionary learning approaches including genetic algorithms and differential evolution algorithms have potential for control of open quantum systems and laboratory quantum control design. Reinforcement learning may provide an effective method for solving quantum control problems with feedback.

NettetThis chapter presents a brief introduction to learning control of quantum systems. Gradient-based learning methods, evolutionary computation algorithms, and reinforcement learning approaches are outlined for searching optimal or robust fields in quantum control problems. The state-of-the-art methods and future directions in … Nettet10. nov. 2003 · Abstract. A quantum system subject to external fields is said to be controllable if these fields can be adjusted to guide the state vector to a desired destination in the state space of the system ...

Nettet24. mar. 2024 · Få Learning and Robust Control in Quantum Technology af som e-bog på engelsk - 9783031202452 - Bøger rummer alle sider af livet. Læs Lyt Lev blandt millioner af bøger på Saxo.com. Nettet1. jul. 2024 · The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and sensing. This poses severe challenges in the efficient control, calibration and validation of quantum states and their dynamics. Although the full …

Nettet18. mar. 2024 · The coherent robust control problem for a class of linear quantum passive systems with model uncertainties is considered in this study, where both the plant and the controller are described by quantum stochastic differential equations (QSDEs). In the framework of language, the model uncertainties are translated into the …

Nettet13. des. 2024 · Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation.Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical … hallie rush show stablesNettetA probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the basic idea of probabilistic action selection. Then the FPQL algorithm is presented for learning control of quantum systems. Two examples (a spin-1/2 system and a Λ-type atomic system) are demonstrated to test the performance of the FPQL algorithm. The results … hallie scruggs facebookNettet7. aug. 2013 · For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. bunnys in sidney ohio