6G technologies such as massive MIMO, dense cells, innovative air interface multiplexing techniques, and ultra-high frequencies stand to significantly benefit from increased amounts of computation at the base station. This position article surveys recent work authors have undertaken realize this vision on today's noisy intermediate scale quantum devices, illustrating possible system architectures leverage power devices for wireless networks. We sketch state art in processing offering...

In order to meet mobile cellular users' ever-increasing data demands, today's 4 G and 5 wireless networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling carbon footprint operational costs such remains a long-standing problem among network designers. This paper takes long view on problem, envisioning NextG scenario where leverages quantum annealing for baseband processing. We gather synthesize insights power...

We present the Hybrid Polar Decoder (HyPD), a hybrid classical-quantum decoder design for error correction codes, which are becoming widespread in today's 5G and tomorrow's 6G networks. HyPD employs CMOS processing decoder's binary tree traversal, Quantum Annealing (QA) (QPD)-a Maximum-Likelihood QA-based submodule. QPD's efficiently transforms into quadratic polynomial optimization form, then maps this on to physical QA hardware via QPD-MAP, customized problem mapping scheme tailored QPD....

With the continuous growth of Internet Things (IoT), trend increasing numbers IoT devices will continue. To increase network's capability to support a large number active accessing network concurrently, this work presents IoT-ResQ, warm-started quantum annealing-based multi-device detector via reverse annealing (RA). Unlike in typical forward (FA) protocol, IoT-ResQ's RA starts its search operation on controllable candidate classical state, instead superposition, and thus allows refined...

We present the Hybrid Polar Decoder (HyPD), a hybrid of classical CMOS and quantum annealing (QA) computational structures for decoding error correction codes, which are becoming widespread in today's 5G tomorrow's 6G networks. HyPD considers code's binary tree traversal, QA executing Quantum (QPD)-a novel QA-based maximum likelihood submodule. Our QPD design efficiently transforms decoder into quadratic polynomial optimization form amenable to QA's process. experimentally evaluate on...

In order to meet mobile cellular users' ever-increasing data demands, today's 4G and 5G networks are designed mainly with the goal of maximizing spectral efficiency. While they have made progress in this regard, controlling carbon footprint operational costs such remains a long-standing problem among network designers. This paper takes long view on problem, envisioning NextG scenario where leverages quantum annealing for baseband processing. We gather synthesize insights power consumption,...

Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in the context of NextG wireless networks. The opportunity to enable large systems and thus improve performance. aims leverage QA expedite computation required for theoretically optimal but computationally-demanding Maximum Likelihood overcome limitations currently deployed linear detectors. This paper presents X-ResQ, a QA-based detector system featuring fine-grained quantum task parallelism that uniquely...

Monte Carlo dropout may effectively capture model uncertainty in deep learning, where a measure of is obtained by using multiple instances at test time. However, applied across the whole network and use it significantly increases computational complexity, proportional to number instances. To reduce time we enable layers only near output neural reuse computation from prior while keeping, if any, other disabled. Additionally, leverage side information about ideal distributions for various...

Channel Coding is the technique that enables reliable delivery of digital data over unreliable communication channels. For most high performance channel coding techniques, existing classical algorithms are computationally expensive, making them impractical for throughput-demanding applications with large code sizes. Today's Noisy Intermediate-Scale Quantum (NISQ) computers, although limited due to a modest number qubits, short coherence time, and poor gate fidelity, useful tools exploring...

Monte Carlo dropout may effectively capture model uncertainty in deep learning, where a measure of is obtained by using multiple instances at test time. However, applied across the whole network and thus significantly increases computational complexity, proportional to number instances. To reduce time we enable layers only near output neural reuse computation from prior while keeping, if any, other disabled. Additionally, leverage side information about ideal distributions for various input...

The use of quantum computation for wireless network applications is emerging as a promising paradigm to bridge the performance gap between in-practice and optimal algorithms. While today's technology offers limited number qubits low fidelity gates, application-based solutions help us understand improve such even further. This paper introduces QGateD-Polar, novel Quantum Gate-based Maximum-Likelihood Decoder design Polar error correction codes, which are becoming widespread in 5G tomorrow's...