Communication in quantum networks of logical bus topology

Brougham, Thomas; Nikolopoulos, Georgios M.; Jex, Igor

doi:10.1103/physreva.80.052325

Cited by 17 publications

(11 citation statements)

References 27 publications

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“…Quantum state transfer [3] can be applied in quantum computation [4] and is essential for a broad range of experimental platforms including trapped ions [5][6][7][8][9][10], cold atoms [11,12], quantum dots [13][14][15][16][17], superconducting [18] and donor qubits [19], NMR [20][21][22], and photonic systems [23][24][25][26][27][28][29] etc. Among various physical systems and geometries [29][30][31][32][33][34][35][36][37][38][39][40][41][42], the transfer of a quantum state through a onedimensional spin chain [43,44] is among the most studied. While extensive researches have been dedicated to improve the fidelity of the transfer, including proposals accomplishing perfect transfer when certain conditions are met [44][45][46]…”

Section: Introductionmentioning

confidence: 99%

Automatic spin-chain learning to explore the quantum speed limit

et al. 2018

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One of the ambitious goals of artificial intelligence is to build a machine that outperforms human intelligence, even if limited knowledge and data are provided. Reinforcement Learning (RL) provides one such possibility to reach this goal. In this work, we consider a specific task from quantum physics, i.e. quantum state transfer in a one-dimensional spin chain. The mission for the machine is to find transfer schemes with fastest speeds while maintaining high transfer fidelities. The first scenario we consider is when the Hamiltonian is time-independent. We update the coupling strength by minimizing a loss function dependent on both the fidelity and the speed. Compared with a scheme proven to be at the quantum speed limit for the perfect state transfer, the scheme provided by RL is faster while maintaining the infidelity below 5 × 10 −4 . In the second scenario where a timedependent external field is introduced, we convert the state transfer process into a Markov decision process that can be understood by the machine. We solve it with the deep Q-learning algorithm. After training, the machine successfully finds transfer schemes with high fidelities and speeds, which are faster than previously known ones. These results show that Reinforcement Learning can be a powerful tool for quantum control problems.

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Section: Introductionmentioning

confidence: 99%

Automatic spin-chain learning to explore the quantum speed limit

et al. 2018

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“…The only perfect transfer protocols that do not directly couple every qubit in the network [7], and hence do not have trivial transfer distance, were designed to transfer a quantum state which is input on a given site, onto a specific, corresponding output site. These schemes are either highly non-local (hypercubes [8][9][10] and integral circulant graphs [11]) or local, but irregular.…”

Section: Introductionmentioning

confidence: 99%

Perfect Quantum Routing in Regular Spin Networks

Pemberton-Ross

Kay

2011

Phys. Rev. Lett.

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Motivated by the need for quantum computers to communicate between multiple, well separated qubits, we introduce the task of quantum routing for distributing quantum states, and generating entanglement, between these sites. We describe regular families of coupled quantum networks which perfectly route qubits between arbitrary pairs of nodes with a high transmission rate. The ability to route multiple states simultaneously and the regularity of the networks vastly improve the utility of this scheme in comparison to the task of state transfer, leading us to propose an implementation in optical lattices.

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“…Brougham et. al [9] have used a passive quantum networks with logical bus topology to transfer information safely. Also, Ciccarello et.…”

Section: Introductionmentioning

confidence: 99%

Communication via an entangled coherent quantum network

2011

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A quantum network is constructed via maximum entangled coherent states. The possibility of using this network to achieve communication between multi-participants is investigated. We showed that the probability of teleported unknown state successfully, depends on the size the used network. As the numbers of participants increases, the successful probability does not depend on the intensity of the field. The problem of implementing quantum teleportation protocol via a noise quantum network is discussed. We show one can send information perfectly with small values of the field intensity and larger values of the noise strength. The successful probability of this suggested protocol increases abruptly for larger values of the noise strength and gradually for small values. We show that for small size of the used quantum network, the fidelity of the teleported state decreases smoothly, while it decreases abruptly for larger size of network.

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Communication in quantum networks of logical bus topology

Cited by 17 publications

References 27 publications

Automatic spin-chain learning to explore the quantum speed limit

Automatic spin-chain learning to explore the quantum speed limit

Perfect Quantum Routing in Regular Spin Networks

Communication via an entangled coherent quantum network

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