Entangled resources enable quantum sensing that achieves Heisenberg scaling, a quadratic improvement on the standard quantum limit, but preparing large scale entangled states is challenging in the presence of decoherence. We present a quantum control strategy using highly nonlinear geometric phase gates for preparing entangled states on spin ensembles which can be used for practical precision metrology. The method uses a dispersive coupling of $N$ spins to a common bosonic mode and does not require addressability, special detunings, or interactions between the spins. Using a control sequence that executes Grover's algorithm on a subspace of permutationally symmetric states, a target entangled resource state can be prepared using $O(N^{5/4})$ geometric phase gates. The geometrically closed path of the control operations ensures the gates are insensitive to the initial state of the mode and the sequence has built-in dynamical decoupling providing resilience to dephasing errors.
Metrology for Quantum Hardware Standardization -- Charting a Pathway: A Strategic Review
AIST, Global Research and Development Center for Business by Quantum-AI Technology (G-QuAT)（产业技术综合研究所，量子人工智能技术驱动业务全球研发中心）；National Metrology Institute of Japan (NMIJ)（日本国家计量院）
Ultra-high sensitivity detection of quantum-scale displacements in cavity optomechanics optimises the combined errors from measurement back-action and imprecisions from incoming quantum noises. This sets the well-known Standard Quantum Limit (SQL). Normal quantum cavity optomechanics allows cooling and detection of a single degree of freedom, along the cavity axis. However, a recent breakthrough that allows quantum ground-state cooling of levitated nanoparticles [Delic et al, arxiv:1911.04406], is uniquely 3D in character, with coupling along the $x$, $y$ and $z$ axes. We investigate current e…
Scaling Enhancement in Distributed Quantum Sensing via Bidirectional Causal Routing
QICI Quantum Information and Computation Initiative, School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院量子信息与计算倡议）；State Key Laboratory of Photonics and Communications, Institute for Quantum Sensing and Information Processing, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能传感学院光子与通信国家重点实验室量子传感与信息处理研究所）；Hefei National Laboratory（合肥国家实验室）；Shanghai Research Center for Quantum Sciences（上海量子科学研究中心）
Networking plays a ubiquitous role in quantum technology. It is an integral part of quantum communication and has significant potential for upscaling quantum computer technologies that are otherwise not scalable. Recently, it was realized that sensing of multiple spatially distributed parameters may also benefit from an entangled quantum network. Here we experimentally demonstrate how sensing of an averaged phase shift among four distributed nodes benefits from an entangled quantum network. Using a four-mode entangled continuous variable (CV) state, we demonstrate deterministic quantum phase s…
The nitrogen vacancy (NV) center in diamond has emerged as a leading solid-state quantum sensor for applications including magnetometry, electrometry, thermometry, and chemical sensing. However, an outstanding challenge for practical applications is that existing NV-based sensing techniques require bulky and discrete instruments for spin control and detection. Here, we address this challenge by integrating NV based quantum sensing with complementary metal-oxide-semiconductor (CMOS) technology. Through tailored CMOS-integrated microwave generation and photodetection, this work dramatically redu…
Shanxi University（山西大学）；Shanxi Normal University（山西师范大学）；Taiyuan University of Technology（太原理工大学）；Shanxi Medical University（山西医科大学）；Shanxi Vocational University of Engineering Science and Technology（山西工程科技职业大学）；Hefei National Laboratory（合肥国家实验室）
Army Research Laboratory（陆军研究实验室）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）
We report a scalable hybrid quantum-classical machine learning framework to build Bayesian networks (BN) that captures the conditional dependence and causal relationships of random variables. The generation of a BN consists of finding a directed acyclic graph (DAG) and the associated joint probability distribution of the nodes consistent with a given dataset. This is a combinatorial problem of structural learning of the underlying graph, starting from a single node and building one arc at a time, that fits a given ensemble using maximum likelihood estimators (MLE). It is cast as an optimizatio…
