Machine learning employs dynamical algorithms that mimic the human capacity to learn, where the reinforcement learning ones are among the most similar to humans in this respect. On the other hand, adaptability is an essential aspect to perform any task efficiently in a changing environment, and it is fundamental for many purposes, such as natural selection. Here, we propose an algorithm based on successive measurements to adapt one quantum state to a reference unknown state, in the sense of achieving maximum overlap. The protocol naturally provides many identical copies of the reference state, such that in each measurement iteration more information about it is obtained. In our protocol, we consider a system composed of three parts, the "environment" system, which provides the reference state copies; the register, which is an auxiliary subsystem that interacts with the environment to acquire information from it; and the agent, which corresponds to the quantum state that is adapted by digital feedback with input corresponding to the outcome of the measurements on the register. With this proposal we can achieve an average fidelity between the environment and the agent of more than 90% with less than 30 iterations of the protocol. In addition, we extend the formalism to d-dimensional states, reaching an average fidelity of around 80% in less than 400 iterations for d = 11, for a variety of genuinely quantum and semiclassical states. This work paves the way for the development of quantum reinforcement learning protocols using quantum data and for the future deployment of semi-autonomous quantum systems. * F. Albarrán-Arriagada francisco.albarran@usach.cl
An experiment is performed to reconstruct an unknown photonic quantum state with a limited amount of copies. A semiquantum reinforcement learning approach is employed to adapt one qubit state, an “agent,” to an unknown quantum state, an “environment,” by successive single‐shot measurements and feedback, in order to achieve maximum overlap. The experimental learning device herein, composed of a quantum photonics setup, can adjust the corresponding parameters to rotate the agent system based on the measurement outcomes “0” or “1” in the environment (i.e., reward/punishment signals). The results show that, when assisted by such a quantum machine learning technique, fidelities of the deterministic single‐photon agent states can achieve over 88% under a proper reward/punishment ratio within 50 iterations. This protocol offers a tool for reconstructing an unknown quantum state when only limited copies are provided, and can also be extended to higher dimensions, multipartite, and mixed quantum state scenarios.
We study a quantum Otto engine embedding a working substance composed by a
two-level system interacting with a harmonic mode. The physical properties of
the substance are described by a generalized quantum Rabi model arising in
superconducting circuits realizations. We show that light-matter quantum
correlations reduction during the hot bath stage and compression stage act as a
resource for enhanced work extraction and efficiency respectively. Also, we
demonstrate that the anharmonic spectrum of the working subtance has a direct
impact on the transition from heat engine into refrigerator as the light-matter
coupling is increased. These results shed light on the search for optimal
conditions in the performance of quantum heat engines.Comment: 9 pages, 12 figure
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