Routing a quantum state in a bio-inspired network
- URL: http://arxiv.org/abs/2211.14176v2
- Date: Wed, 19 Jul 2023 09:28:16 GMT
- Title: Routing a quantum state in a bio-inspired network
- Authors: Elham Faraji, Alireza Nourmandipour, Stefano Mancini, Marco Pettini,
Roberto Franzosi
- Abstract summary: We consider a spin network resembling an $alpha$-helix structure and study quantum information transfer over this network.
We investigate analytically and numerically the perfect state transfer (PST) in such a network which provides an upper bound on the probability of quantum states transfer from one node to another.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We consider a spin network resembling an $\alpha$-helix structure and study
quantum information transfer over this bio-inspired network. The model we use
is the Davydov model in its elementary version without a phononic environment.
We investigate analytically and numerically the perfect state transfer (PST) in
such a network which provides an upper bound on the probability of quantum
states transfer from one node to another. We study PST for different boundary
conditions on the network and show it is reachable between certain nodes and
with suitable spin-spin couplings.
Related papers
- Simulation of Entanglement-Enabled Connectivity in QLANs using SeQUeNCe [7.486717790185952]
Quantum Local Area Networks (QLANs) are a promising building block for larger scale quantum networks.
In this paper, we discuss the implementation of the QLAN model in SeQUeNCe, a discrete-event simulator of quantum networks.
arXiv Detail & Related papers (2024-11-17T10:20:25Z) - From $SU(2)$ holonomies to holographic duality via tensor networks [0.0]
We construct a tensor network representation of the spin network states, which correspond to $SU(2)$ gauge-invariant discrete field theories.
The spin network states play a central role in the Loop Quantum Gravity (LQG) approach to the Planck scale physics.
arXiv Detail & Related papers (2024-10-24T14:59:35Z) - Deterministic multipartite entanglement via fractional state transfer across quantum networks [0.0]
We propose a fractional quantum state transfer, in which the excitation of an emitter is partially transmitted through the quantum communication channel.
We show that genuine multipartite entangled states can be faithfully prepared within current experimental platforms.
arXiv Detail & Related papers (2024-08-02T10:59:16Z) - Quantum State Transfer in Interacting, Multiple-Excitation Systems [41.94295877935867]
Quantum state transfer (QST) describes the coherent passage of quantum information from one node to another.
We describe Monte Carlo techniques which enable the discovery of a Hamiltonian that gives high-fidelity QST.
The resulting Jaynes-Cummings-Hubbard and periodic Anderson models can, in principle, be engineered in appropriate hardware to give efficient QST.
arXiv Detail & Related papers (2024-05-10T23:46:35Z) - Quantum-enhanced metrology with network states [8.515162179098382]
We prove a general bound that limits the performance of using quantum network states to estimate a global parameter.
Our work establishes both the limitation and the possibility of quantum metrology within quantum networks.
arXiv Detail & Related papers (2023-07-15T09:46:35Z) - Simulation of Entanglement Generation between Absorptive Quantum
Memories [56.24769206561207]
We use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories.
We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe.
We observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number.
arXiv Detail & Related papers (2022-12-17T05:51:17Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Optimized Quantum Networks [68.8204255655161]
Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests.
We utilize this to design entanglement-based quantum networks tailored to their desired functionality.
arXiv Detail & Related papers (2021-07-21T18:00:07Z) - Realization of a multi-node quantum network of remote solid-state qubits [0.45823749779393547]
We report on the experimental realization of a three-node entanglement-based quantum network.
We achieve real-time communication and feed-forward gate operations across the network.
We capitalize on the novel capabilities of this network to realize two canonical protocols without post-selection.
arXiv Detail & Related papers (2021-02-08T19:00:03Z) - Genuine Network Multipartite Entanglement [62.997667081978825]
We argue that a source capable of distributing bipartite entanglement can, by itself, generate genuine $k$-partite entangled states for any $k$.
We provide analytic and numerical witnesses of genuine network entanglement, and we reinterpret many past quantum experiments as demonstrations of this feature.
arXiv Detail & Related papers (2020-02-07T13:26:00Z) - Entanglement Classification via Neural Network Quantum States [58.720142291102135]
In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states.
We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
arXiv Detail & Related papers (2019-12-31T07:40:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.