Building and Experimenting with Quantum Compute-Enabled NextG Wireless Networks
In recent years, user demand for increasing amounts of wireless capacity continues to outpace supply. This line of research aims to transform the current research landscape by leveraging quantum computation to overcome previous computational limitations, enabling new levels of wireless network performance, with the eventual outcome of incorporating quantum computation into tomorrow's Next Generation wireless cellular networking standards.
Context
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The Network Architect's Perspective
Performance-compute elasticity. In large NextG wireless networks there is elasticity in the relationship between spectral efficiency and expended compute cycles. There are several such examples of this, including:
- MIMO decoding: there is a potential to realize greater spectral efficiency by concurrently serving more uplink users (up to the number of base station antennas). In other words, the existence of a near-optimal MIMO detector for Large MIMO systems would enable the expansion of Massive MIMO systems (which serve significantly fewer users than antennas), to serve more users.
- Existing error control decoders (LDPC, Polar codes) make throughput compromises, in particular, reducing decoding precision, limiting the number of iterations the decoder expends, or using reduced-complexity algorithms.
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The Quantum/Optical Computer Architect's Perspective
Unique wireless demands. Unlike many other applications, to operate at "line rate," wireless baseband processing requires both (1) high computational throughput, and (2) low computational latency.
Explore Further
Use the links on this page to explore our work in gate model quantum computers, quantum annealer devices, and quantum-inspired analog computing devices.
Our forward-looking work quantum resource estimation aims to estimate the amount of quantum computational resources required for various types of processing tasks to support NextG wireless networks.