Add to ORKGWe develop an autonomous agent effectively interacting with noisy quantum computer to solve magnetism problems. By using the reinforcement learning the agent is trained to find the best-possible approximation of a spin Hamiltonian ground state from self-conducted ex-periments on quantum devices.This work was supported by the Russian Science Foundation, Grant No. 18-12-00185
Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum syste...
The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorit...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Quantum computing is expected to provide new promising approaches for solving the most challenging p...
Artificial neural networks are revolutionizing science. While the most prevalent technique involves ...
The paper proposes a new method of quantum computing using control and systems theory as well as mat...
Several tasks involving the determination of the time evolution of a system of solid state qubits re...
With quantum computers still under heavy development, already numerous quantum machine learning algo...
In this thesis, we investigate the speedup we can achieve with quantum enhanced reinforcement learni...
Engineering desired Hamiltonian in quantum many-body systems is essential for applications such as q...
Wereport a proof-of-principle experimental demonstration of the quantum speed-up for learning agents...
Strongly interacting quantum systems described by non-stoquastic Hamiltonians exhibit rich low-tempe...
The efficient validation of quantum devices is critical for emerging technological applications. In ...
We present the experimental demonstration of quantum Hamiltonian learning. Using an integrated silic...
Optimal control is highly desirable in many current quantum systems, especially to realize tasks in ...
Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum syste...
The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorit...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...
Quantum computing is expected to provide new promising approaches for solving the most challenging p...
Artificial neural networks are revolutionizing science. While the most prevalent technique involves ...
The paper proposes a new method of quantum computing using control and systems theory as well as mat...
Several tasks involving the determination of the time evolution of a system of solid state qubits re...
With quantum computers still under heavy development, already numerous quantum machine learning algo...
In this thesis, we investigate the speedup we can achieve with quantum enhanced reinforcement learni...
Engineering desired Hamiltonian in quantum many-body systems is essential for applications such as q...
Wereport a proof-of-principle experimental demonstration of the quantum speed-up for learning agents...
Strongly interacting quantum systems described by non-stoquastic Hamiltonians exhibit rich low-tempe...
The efficient validation of quantum devices is critical for emerging technological applications. In ...
We present the experimental demonstration of quantum Hamiltonian learning. Using an integrated silic...
Optimal control is highly desirable in many current quantum systems, especially to realize tasks in ...
Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum syste...
The quantum alternating operator ansatz (QAOA) is a prominent example of variational quantum algorit...
Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained gre...