Modern quantum optical systems such as photonic quantum computers and quantum
imaging devices require great precision in their designs and implementations in
the hope to realistically exploit entanglement and reach a real quantum
advantage. The theoretical and experimental explorations and validations of
these systems are greatly dependent on the precision of our classical
simulations. However, as Hilbert spaces increases, traditional computational
methods used to design and optimize these systems encounter hard limitations
due to the quantum curse of dimensionally. To address this challenge, we
propose an approach based on neural and tensor networks to approximate the
exact unitary evolution of closed entangled systems in a precise, efficient and
quantum consistent manner. By training the networks with a reasonably small
number of examples of quantum dynamics, we enable efficient parameter
estimation in larger Hilbert spaces, offering an interesting solution for a
great deal of quantum metrology problems.

利用神经和张量网络近似精确、高效和量子一致地演化封闭纠缠系统的方法，可以解决传统计算方法在哈密顿空间增加时遇到的硬性限制，为众多量子计量问题提供了有趣的解决方案。

量子一致神经 / 张量网络在强 / 弱纠缠态光子电路中的应用

Quantum consistent neural/tensor networks for photonic circuits with  strongly/weakly entangled states

Estimation of physical quantities is at the core of most scientific research
and the use of quantum devices promises to enhance its performances. In real
scenarios, it is fundamental to consider that the resources are limited and
Bayesian adaptive estimation represents a powerful approach to efficiently
allocate, during the estimation process, all the available resources. However,
this framework relies on the precise knowledge of the system model, retrieved
with a fine calibration that often results computationally and experimentally
demanding. Here, we introduce a model-free and deep learning-based approach to
efficiently implement realistic Bayesian quantum metrology tasks accomplishing
all the relevant challenges, without relying on any a-priori knowledge on the
system. To overcome this need, a neural network is trained directly on
experimental data to learn the multiparameter Bayesian update. Then, the system
is set at its optimal working point through feedbacks provided by a
reinforcement learning algorithm trained to reconstruct and enhance experiment
heuristics of the investigated quantum sensor. Notably, we prove experimentally
the achievement of higher estimation performances than standard methods,
demonstrating the strength of the combination of these two black-box algorithms
on an integrated photonic circuit. This work represents an important step
towards fully artificial intelligence-based quantum metrology.

本研究提出了一种基于深度学习和强化学习的模型无关方法，实现了实际的贝叶斯量子计量任务，并在一个集成光电电路上证明了比标准方法更高的测量性能，这是实现完全基于人工智能的量子计量的重要一步。

量子多参数估计的深度强化学习

Deep reinforcement learning for quantum multiparameter estimation

Quantum Metrology is one of the most promising application of quantum
technologies. The aim of this research field is the estimation of unknown
parameters exploiting quantum resources, whose application can lead to enhanced
performances with respect to classical strategies. Several physical quantum
systems can be employed to develop quantum sensors, and photonic systems
represent ideal probes for a large number of metrological tasks. Here we review
the basic concepts behind quantum metrology and then focus on the application
of photonic technology for this task, with particular attention to phase
estimation. We describe the current state of the art in the field in terms of
platforms and quantum resources. Furthermore, we present the research area of
multiparameter quantum metrology, where multiple parameters have to be
estimated at the same time. We conclude by discussing the current experimental
and theoretical challenges, and the open questions towards implementation of
photonic quantum sensors with quantum-enhanced performances in the presence of
noise.

简述：本文回顾了量子计量学的基本概念，特别关注光子技术在此任务中的应用，介绍了当今领域中平台和量子资源的最新进展，以及多参数量子计量学领域的研究方向，最后讨论光子量子传感器实施的实验和理论挑战，以及在噪声中具有量子增强性能的实现的未解决问题。

光子量子计量学

Photonic Quantum Metrology

Realistic quantum mechanical systems are always exposed to an external
environment. The presence of the environment often gives rise to a Markovian
process in which the system loses information to its surroundings. However,
many quantum systems exhibit a pronounced non-Markovian behavior in which there
is a flow of information from the environment back to the system, signifying
the presence of quantum memory effects [1-5]. The environment is usually
composed of a large number of degrees of freedom which are difficult to
control, but some sophisticated schemes for modifying the environment have been
developed [6]. The physical realization and control of dynamical processes in
open quantum systems plays a decisive role, for example, in recent proposals
for the generation of entangled states [7-9], for schemes of dissipative
quantum computation [10], for the design of quantum memories [11] and for the
enhancement of the efficiency in quantum metrology [12]. Here we report an
experiment which allows through selective preparation of the initial
environmental states to drive the open system from the Markovian to the
non-Markovian regime, to control the information flow between the system and
the environment, and to determine the degree of non-Markovianity by direct
measurements on the open system.

通过控制环境来驱动系统处于非马尔科夫状态，从而实现量子记忆等领域的应用。