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Nonlocal boxes are conceptual tools that capture the essence of the phenomenon of quantum non-locality, central to modern quantum theory and quantum technologies. We introduce network nonlocal boxes tailored for quantum networks under the natural assumption that these networks connect independent sources and do not allow signaling. Hence, these boxes satisfy the No-Signaling and Independence (NSI) principle. For the case of boxes without inputs, connecting pairs of sources and producing binary outputs, we prove that there is an essentially unique network nonlocal box with local random outputs and maximal 2-box correlations: $E_2=sqrt{2}-1$.
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We quantify the amount of non-locality contained in n noi
It has been recently shown, that some of the tripartite boxes admitting bilocal decomposition, lead to non-locality under wiring operation applied to two of the subsystems [R. Gallego et al. Physical Review Letters 109, 070401 (2012)]. In the following, we study this phenomenon quantitatively. Basing on the known classes of boxes closed under wirings, we introduced multipartite monotones which are counterparts of bipartite ones - the non-locality cost and robustness of non-locality. We then provide analytical lower bounds on both the monotones in terms of the Maximal Non-locality which can be obtained by Wirings (MWN). We prove also upper bounds for the MWN of a given box, based on the weight of boxes signaling in a particular direction, that appear in its bilocal decomposition. We study different classes of partially local boxes and find MWN for each class, using Linear Programming. We identify also the wirings which lead to MWN and exhibit that some of them can serve as a witness of certain classes. We conclude with example of partially local boxes being analogue of quantum states that allow to distribute entanglement in separable manner.
We develop an open-system dynamical theory of the Casimir interaction between coherent atomic waves and a material surface. The system --- the external atomic waves --- disturbs the environment --- the electromagnetic field and the atomic dipole degrees of freedom --- in a non- local manner by leaving footprints on distinct paths of the atom interferometer. This induces a non-local dynamical phase depending simultaneously on two distinct paths, beyond usual atom-optics methods, and comparable to the local dynamical phase corrections. Non-local and local atomic phase coherences are thus equally important to capture the interplay between the external atomic motion and the Casimir interaction. Such dynamical phases are obtained for finite-width wavepackets by developing a diagrammatic expansion of the disturbed environment quantum state.
Quantum superpositions of distinct coherent states in a single-mode harmonic oscillator, known as cat states, have been an elegant demonstration of Schrodingers famous cat paradox. Here, we realize a two-mode cat state of electromagnetic fields in two microwave cavities bridged by a superconducting artificial atom, which can also be viewed as an entangled pair of single-cavity cat states. We present full quantum state tomography of this complex cat state over a Hilbert space exceeding 100 dimensions via quantum non-demolition measurements of the joint photon number parity. The ability to manipulate such multi-cavity quantum states paves the way for logical operations between redundantly encoded qubits for fault-tolerant quantum computation and communication.
In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.
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