Machine Learning Applied to Quantum Synchronization‐Assisted Probing

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.

Spectral density classification for environment spectroscopy

“…For instance, in [34] ML techniques were used to discern between Markovian and non-Markovian noise. More pertinent to the matter at hand, aspects of the problem of distinguishing between Ohmic, sub-Ohmic and super-Ohmic SDs have already been studied: in [35], a scenario where a probe qubit is used to access a second inaccessible one is proposed to infer the Ohmicity class by using NNs and leveraging the special features of quantum synchronization. In [36], a different use of NNs was put forward as tomographic data at just two instants of time were used, rather than a time-series approach.…”

Transient synchronization in open quantum systems

“…This has been shown to allow, for instance, for cryptography and communications based on chaotic signals [7]. Furthermore SS can arise also in the transient evolution of dynamical systems, during relaxation towards equilibrium [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In particular SS is recognized as a universal phenomenon in non-linear sciences [1] but it can also occur in linear systems [9,11,13].…”

“…A first example of an application based on transient SS has been proposed in the context of quantum probing: the transition between synchronization in phase and in antiphase can allow one to probe the environment of a dissipating qubit with an external probe [14]. This has also been exploited to improve the performance of probing assisted by machine learning [16]. When increasing the complexity of the system, as in random or small-world networks, transient SS is also found to be a persistent phenomenon [17] that can be triggered by local parameter tuning [13].…”

Quantum Machine Learning and Bioinspired Quantum Technologies

“…In the quantum biomimetic field, we have ‐Contributions related to quantum synchronization via quantum machine learning, in the parallel works by Francisco A. Cárdenas-López et al . and Gabriel Garau Estarellas et al ‐An article motivating the debate on possible quantum effects in conscious minds, by Göran Wendin …”

“…[2] -Experimentally carrying out a quantum autoencoder based on quantum adders with the Rigetti cloud quantum computer, by Yongcheng Ding et al [3] -A quantum experiment to reconstruct an unknown photonic quantum state with a limited amount of copies, in the context of reinforcement learning, by Shang Yu et al [4] -A prediction of the band gap which represents one of the basic properties of a crystalline material via machine learning calculations, by Alexander V. Balatsky and co-workers. [5] -A Review Article on the progress in using artificial neural networks to build quantum many-body states, by Zhih-Ahn Jia et al [6] In the quantum biomimetic field, we have -Contributions related to quantum synchronization via quantum machine learning, in the parallel works by Francisco A. Cárdenas-López et al [7] and Gabriel Garau Estarellas et al [8] -An article motivating the debate on possible quantum effects in conscious minds, by Göran Wendin. [9] We believe that this Special Issue will shed light on unexplored areas up to now, acting as the precursor for further exciting research in the field of bioinspired quantum technologies.…”