Towards Real‐World Quantum Networks: A Review

Abstract

Quantum networks play an extremely important role in quantum information science, with application to quantum communication, computation, metrology, and fundamental tests. One of the key challenges for implementing a quantum network is to distribute entangled flying qubits to spatially separated nodes, at which quantum interfaces or transducers map the entanglement onto stationary qubits. The stationary qubits at the separated nodes constitute quantum memories realized in matter while the flying qubits constitute quantum channels realized in photons. Dedicated efforts around the world for more than 20 years have resulted in both major theoretical and experimental progress toward entangling quantum nodes and ultimately building a global quantum network. Here, the development of quantum networks and the experimental progress over the past two decades leading to the current state of the art for generating entanglement of quantum nodes based on various physical systems such as single atoms, cold atomic ensembles, trapped ions, diamonds with nitrogen-vacancy centers, and solid-state host doped with rare-earth ions are reviewed. Along the way, the merits are discussed and the potential of each of these systems toward realizing a quantum network is compared.

Classification of four-qubit entangled states via Machine Learning

“…To form a data set needed to train our SVM model we sample complex numbers for each class {E i , i = 1, 5}. In addition, we sample random unitary matrices Û to increase the diversity of a particular data set in accordance with (5).…”

“…The sampling of H is done with another QuTip function, rand herm. Summarizing, for each SLOCC class of four-qubit states we create N data = 10000 samples of pure states, including factorized states, utilizing LOCC transformations for each state (5) with random unitary operators in accordance with Eq. ( 6).…”

“…In the case of entangled states, one considers three different cases. In these cases we operate with different data sets of entangled states where the diversity of each sample is varying by parameter ε in (6), in accordance with the SLOCC classification criteria (5). We tested the procedure for the following parameters: ε 1 = 0.5, ε 2 = 0.75, ε 3 = 1.…”

“…The ability to quantify, detect and analyze the structure of quantum entanglement [1] is essential for quantum computation [2][3][4], communication [5][6][7], quantum networks [8] and quantum metrology [9,10]. Moreover, an improper "amount" of entanglement, specific structure or pattern of multipartite entangled state, or action of quantum noise could severely affect the overall efficiency of quantum computation tasks [11] or performance of a given quantum protocol, such as that of entanglement purification [12].…”

Efficient, ever-ready quantum memory at room temperature for single photons

“…Harnessing the full alluring potential of quantum technologies will require linking quantum systems locally and globally [1][2][3] to share resources and enhance capabilities: similar to the multi-processor CPUs of local computers, or the vast computing power that comes from connecting data centers to form the internet. Quantum interconnects will be an essential element in linking the nodes of a quantum network consisting of sensors [4], distributed quantum computers [5,6], and quantum clock signals [7].…”