Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems

• URL: http://arxiv.org/abs/2010.00682v1
• Date: Thu, 1 Oct 2020 21:00:12 GMT
• Title: Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems
• Authors: Minsung Kim, Davide Venturelli, Kyle Jamieson
• Abstract summary: This paper explores the boundary between the two types of computation---classical-quantum hybrid processing for optimization problems in wireless systems. We explore the feasibility of a hybrid system with a real hardware prototype using one of the most advanced experimentally available techniques.
• Score: 6.63697821097848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With unprecedented increases in traffic load in today's wireless networks, design challenges shift from the wireless network itself to the computational support behind the wireless network. In this vein, there is new interest in quantum-compute approaches because of their potential to substantially speed up processing, and so improve network throughput. However, quantum hardware that actually exists today is much more susceptible to computational errors than silicon-based hardware, due to the physical phenomena of decoherence and noise. This paper explores the boundary between the two types of computation---classical-quantum hybrid processing for optimization problems in wireless systems---envisioning how wireless can simultaneously leverage the benefit of both approaches. We explore the feasibility of a hybrid system with a real hardware prototype using one of the most advanced experimentally available techniques today, reverse quantum annealing. Preliminary results on a low-latency, large MIMO system envisioned in the 5G New Radio roadmap are encouraging, showing approximately 2--10X better performance in terms of processing time than prior published results.

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks. embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. split computing - where an SNN is partitioned across two devices - is a promising solution. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Dissipation-driven quantum generative adversarial networks [11.833077116494929]
  We introduce a novel dissipation-driven quantum generative adversarial network (DQGAN) architecture specifically tailored for generating classical data. The classical data is encoded into the input qubits of the input layer via strong tailored dissipation processes. We extract both the generated data and the classification results by measuring the observables of the steady state of the output qubits.
  arXiv  Detail & Related papers  (2024-08-28T07:41:58Z)
• Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies [0.0]
  This work demonstrates, for the first time, how the quantum generator architecture for the style-based quantum generative adversarial network (qGAN) can be implemented. The style-based qGAN, proposed in 2022, generalizes the state of the art for qGANs and allows for shallow-depth networks. The results obtained on both devices are of comparable quality, with the aria-1 device delivering somewhat more accurate results than the ibm_torino device.
  arXiv  Detail & Related papers  (2024-05-07T15:26:51Z)
• Quantum-assisted trustworthiness for the Quantum Internet [0.0]
  Device redundancy is a mechanism to increase the overall system fault tolerance and, consequently, trustworthiness. Quantum-assisted mechanisms have been envisaged as an appealing alternative to improve the performance in this kind of communication networks. This paper explores this approach by using super-additivity and superposed quantum trajectories in quantum Internet.
  arXiv  Detail & Related papers  (2024-04-25T09:47:30Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• Real-Time GPU-Accelerated Machine Learning Based Multiuser Detection for 5G and Beyond [70.81551587109833]
  nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity. One of the main challenges comes from the real-time implementation of these algorithms. This paper explores the acceleration of APSM-based algorithms through massive parallelization.
  arXiv  Detail & Related papers  (2022-01-13T15:20:45Z)
• Controller-based Energy-Aware Wireless Sensor Network Routing using Quantum Algorithms [15.607213703199209]
  We show proof-of-principle for the use of a quantum processor instead of a classical processor, to find optimal or near-optimal solutions very quickly. Preliminary results for small networks show that this approach using quantum computing has great promise and may open the door for other significant improvements in the efficacy of network algorithms.
  arXiv  Detail & Related papers  (2021-10-12T20:16:21Z)
• The Computational and Latency Advantage of Quantum Communication Networks [70.01340727637825]
  This article summarises the current status of classical communication networks. It identifies some critical open research challenges that can only be solved by leveraging quantum technologies.
  arXiv  Detail & Related papers  (2021-06-07T06:31:02Z)
• Entanglement Rate Optimization in Heterogeneous Quantum Communication Networks [79.8886946157912]
  Quantum communication networks are emerging as a promising technology that could constitute a key building block in future communication networks in the 6G era and beyond. Recent advances led to the deployment of small- and large-scale quantum communication networks with real quantum hardware. In quantum networks, entanglement is a key resource that allows for data transmission between different nodes.
  arXiv  Detail & Related papers  (2021-05-30T11:34:23Z)

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.