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Zeitschrift für Naturforschung A

Machine learning studies for the effects of probes and cavity on quantum synchronization

Qing-Yu Meng, Yong Hu, Qing Hui Yang, Qinsheng Zhu, Xiaoyu Li

Published Feb 26, 2021• 0 citations
Publisher

Abstract

Abstract As an important technology of the quantum detection, the quantum synchronization detection is always used in the detection or measurement of some quantum systems. A probing model is established to describe the probing of a qubit system in the cavity field and to reveal the effect of the environment (cavity) on the quantum synchronization occurrence, as well as the interactions among environment, a qubit system, and probing equipment. By adjusting the frequency of the probe, the in-phase, anti-phase, and out-of-phase synchronization can be achieved. Simultaneously, the effect of γ 3 ${\gamma }_{3}$ which describes the interaction strength between the probe and environments for quantum synchronization is discussed under different Ohmic dissipation index s . Finally, the machine learning method is applied to present an optimization for classification and regression of synchronization transition dependent on s and γ 3 ${\gamma }_{3}$ .

DOI
10.1515/zna-2020-0303

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Abstract As an important technology of the quantum detection, the quantum synchronization detection is always used in the detection or measurement of some quantum systems. A probing model is established to describe the probing of a qubit system in the cavity field and to reveal the effect of the environment (cavity) on the quantum synchronization occurrence, as well as the interactions among environment, a qubit system, and probing equipment. By adjusting the frequency of the probe, the in-phase, anti-phase, and out-of-phase synchronization can be achieved. Simultaneously, the effect of γ 3 γ3{\gamma }_{3}γ3​ which describes the interaction strength between the probe and environments for quantum synchronization is discussed under different Ohmic dissipation index s . Finally, the machine learning method is applied to present an optimization for classification and regression of synchronization transition dependent on s and γ 3 γ3{\gamma }_{3}γ3​ .
Abstract A probing scheme is considered with an accessible and controllable qubit, used to probe an out‐of equilibrium system consisting of a second qubit interacting with an environment. Quantum spontaneous synchronization between the probe and the system emerges in this model and, by tuning the probe frequency, can occur both in‐phase and in anti‐phase. The capability of machine learning in this probing scheme is analyzed based on quantum synchronization. An artificial neural network is used to infer, from a probe observable, main dissipation features, such as the environment Ohmicity index. The efficiency of the algorithm in the presence of some noise in the dataset is also considered. It is shown that the performance in either classification and regression is significantly improved due to the in/anti‐phase synchronization transition. This opens the way to the characterization of environments with arbitrary spectral densities.
The interaction of a quantum system, which is not accessible by direct measurement, with an external probe can be exploited to infer specific features of the system itself. We introduce a probing scheme based on the emergence of spontaneous quantum synchronization between an out-of-equilibrium qubit, in contact with an external environment, and a probe qubit. Tuning the frequency of the probe leads to a transition between synchronization in phase and antiphase. The sharp transition between these two regimes is locally accessible by monitoring the probe dynamics alone and allows one to reconstruct the shape of the spectral density of the environment.
Abstract The quantum synchronization between a pair of two‐level systems inside two coupled cavities is studied. By using a digital–analog decomposition of the master equation that rules the system dynamics, it is shown that this approach leads to quantum synchronization between both two‐level systems. Moreover, in this digital–analog block decomposition, the fundamental elements of a quantum machine learning protocol can be identified, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If the algorithm can be additionally equipped with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state, and local conditional operations on the agent and environment subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experiences different loss/decoherence mechanisms, and gives the flexibility to choose the synchronization state. Finally, an implementation is proposed, based on current technologies in superconducting circuits.
Abstract Quantum probing is the art of exploiting simple quantum systems interacting with a complex environment to extract precise information about some environmental parameters, e.g. the temperature of the environment or its spectral density. Here we analyze the performance of a single-qubit probe in characterizing Ohmic bosonic environments at thermal equilibrium. In particular, we analyze the effects of tuning the interaction Hamiltonian between the probe and the environment, going beyond the traditional paradigm of pure dephasing. In the weak-coupling and short-time regime, we address the dynamics of the probe analytically, whereas numerical simulations are employed in the strong coupling and long-time regime. We then evaluate the quantum Fisher information for the estimation of the cutoff frequency and the temperature of the environment. Our results provide clear evidence that pure dephasing is not optimal, unless we focus attention to short times. In particular, we found several working regimes where the presence of a transverse interaction improves the maximum attainable precision, i.e. it increases the quantum Fisher information. We also explore the role of the initial state of the probe and of the probe characteristic frequency in determining the estimation precision, thus providing quantitative guidelines to design optimized detection to characterize bosonic environments at the quantum level.
The cover figure represents a system comprising two atoms, which are called Agent and Environment, coupled to a central atom, called Register, in a dissipative reservoir. Additionally, a laser shines the Agent atom to counterbalance the losses. The machine learning protocol consists of measuring the Register and, according to the measurement outcome, act on the adjacent systems via classical communication. This protocol speeds up the quantum synchronization between Agent and Environment. For further information see article number 1800076 by Mikel Sanz and co-workers.