Scalable Quantum Spin Networks from Unitary Construction
- URL: http://arxiv.org/abs/2307.12978v2
- Date: Thu, 28 Dec 2023 20:28:00 GMT
- Title: Scalable Quantum Spin Networks from Unitary Construction
- Authors: Abdulsalam H. Alsulami, Irene D'Amico, Marta P. Estarellas, and
Timothy P. Spiller
- Abstract summary: We present larger spin network systems that can be used for longer-range quantum information tasks.
We show that even such larger spin network systems are robust against realistic levels of disorder.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Spin network systems can be used to achieve quantum state transfer with high
fidelity and to generate entanglement. A new approach to design
spin-chain-based spin network systems, for shortrange quantum information
processing and phase-sensing, has been proposed recently in [1]. In this paper,
we investigate the scalability of such systems, by designing larger spin
network systems that can be used for longer-range quantum information tasks,
such as connecting together quantum processors. Furthermore, we present more
complex spin network designs, which can produce different types of entangled
states. Simulations of disorder effects show that even such larger spin network
systems are robust against realistic levels of disorder.
Related papers
- Entanglement-Assisted Quantum Networks: Mechanics, Enabling
Technologies, Challenges, and Research Directions [66.27337498864556]
This paper presents a comprehensive survey of entanglement-assisted quantum networks.
It provides a detailed overview of the network structure, working principles, and development stages.
It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
arXiv Detail & Related papers (2023-07-24T02:48:22Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Complex quantum network models from spin clusters [0.0]
We present a theoretical model for complex quantum communication networks on a lattice of spins.
We show that the resulting quantum networks can have complexity comparable to that of the classical internet.
arXiv Detail & Related papers (2022-10-28T02:19:42Z) - Unitary Design of Quantum Spin Networks for Robust Routing, Entanglement
Generation, and Phase Sensing [0.0]
This paper studies a more complex spin system, a 2D spin network (SN) engineered by applying suitable unitaries to two uncoupled spin chains.
Considering only the single-excitation subspace of the SN, it is demonstrated that the system can be operated as a router, directing information through the SN.
It is also shown that it can serve to generate maximally entangled states between two sites.
arXiv Detail & Related papers (2022-02-05T20:12:54Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - A proposal for practical multidimensional quantum networks [0.0]
High-dimensional quantum systems (qudits) present higher photon information efficiency and robustness to noise.
Their use in quantum networks present experimental challenges due to the impractical resources required in high-dimensional quantum repeaters.
Our work significantly simplifies the implementation of high-dimensional quantum networks, fostering their development with current technology.
arXiv Detail & Related papers (2021-03-16T17:15:58Z) - Entanglement transfer, accumulation and retrieval via quantum-walk-based
qubit-qudit dynamics [50.591267188664666]
Generation and control of quantum correlations in high-dimensional systems is a major challenge in the present landscape of quantum technologies.
We propose a protocol that is able to attain entangled states of $d$-dimensional systems through a quantum-walk-based it transfer & accumulate mechanism.
In particular, we illustrate a possible photonic implementation where the information is encoded in the orbital angular momentum and polarization degrees of freedom of single photons.
arXiv Detail & Related papers (2020-10-14T14:33:34Z) - Experimental Quantum Generative Adversarial Networks for Image
Generation [93.06926114985761]
We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
arXiv Detail & Related papers (2020-10-13T06:57:17Z) - Probing Criticality in Quantum Spin Chains with Neural Networks [0.0]
We show that even neural networks with no hidden layers can be effectively trained to distinguish between magnetically ordered and disordered phases.
Our results extend to a wide class of interacting quantum many-body systems and illustrate the wide applicability of neural networks to many-body quantum physics.
arXiv Detail & Related papers (2020-05-05T12:34:50Z) - 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.