Award Abstract # 1824470
SpecEES: Collaborative Research: Advancing the Wireless Spectral Frontier with Quantum-Enabled Computational Techniques (QENeTs)

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UNIVERSITIES SPACE RESEARCH ASSOCIATION
Initial Amendment Date: September 6, 2018
Latest Amendment Date: September 6, 2018
Award Number: 1824470
Award Instrument: Standard Grant
Program Manager: Murat Torlak
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2018
End Date: February 28, 2022 (Estimated)
Total Intended Award Amount: $277,206.00
Total Awarded Amount to Date: $277,206.00
Funds Obligated to Date: FY 2018 = $277,206.00
History of Investigator:
  • Davide Venturelli (Principal Investigator)
    DVenturelli@usra.edu
Recipient Sponsored Research Office: Universities Space Research Association
425 3RD ST SW STE 950
WASHINGTON
DC  US  20024-3230
(410)730-2656
Sponsor Congressional District: 00
Primary Place of Performance: USRA NASA Quantum AI Laboratory
NASA Ames Research Center MS-269
Moffett Field
CA  US  94035-0001
Primary Place of Performance
Congressional District:
18
Unique Entity Identifier (UEI): VPWMMPGGPJ74
Parent UEI: VPWMMPGGPJ74
NSF Program(s): SpecEES Spectrum Efficiency, E
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 059Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

In recent years, user demand for increasing amounts of wireless capacity continues to outpace supply. To meet this demand, significant progress has been made in designing new wireless technologies, but even higher-performance systems remain impractical largely because their techniques are extremely computationally demanding. For the best performance, these techniques generally require an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user. The base station's computational capacity is becoming the limiting factor on wireless capacity. Quantum-Enabled Computational Techniques (QENeTs) 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 wireless cellular networking standards. In the context of advanced cellular technologies, QENeTs will contribute techniques to both improve network performance and enable co-existence of wireless local area networking technologies such as Wi-Fi in dense deployments.

The project will design a multitude of new communications receiver decoding algorithms that are amenable to execution on today's and tomorrow's quantum annealers, as well as the early prototypes of forthcoming quantum gate model computers. The QENeTs maximum-likelihood decoder problems are the first application of quantum computing to wireless networks, and the first time that such real-world problems have been attempted in quantum computers in their full complexity. These methods will be tested on real hardware and benchmarked against the best known classical approaches. In addition to spectral efficiency, the project will also consider how quantum-enabled techniques can improve the energy efficiency of massive multiple-input/multiple-output (MIMO) algorithms, both on the mobile handset where battery life is key, as well as on the infrastructure side, where the ability to power-down base stations reaps significant cost savings benefits for the network operator. The QENeTs project will contribute to the governmental and industrial research environments, as the Universities Space Research Association (USRA), one of the collaborating institutions, has entered into a joint Space Act Agreement with the National Aeronautics and Space Administration (NASA) and Google to conduct collaborative research on the benefits of quantum computing for a range of challenging applications. QENeTs will further support graduate student education through summer internship experiences at USRA and the NASA Ames Research Center.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Kasi, Srikar and Singh, Abhishek Kumar and Venturelli, Davide and Jamieson, Kyle "Quantum Annealing for Large MIMO Downlink Vector Perturbation Precoding" IEEE International Conference on Communications , 2021 https://doi.org/10.1109/ICC42927.2021.9500557 Citation Details
Kim, M. and Venturelli, D. and Jamieson, K. "Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems" Nineteenth ACM Workshop on Hot Topics in Networks , 2020 https://doi.org/10.1145/3422604.3425924 Citation Details
Kim, Minsung and Kasi, Srikar and Lott, P. Aaron and Venturelli, Davide and Kaewell, John and Jamieson, Kyle "Heuristic Quantum Optimization for 6G Wireless Communications" IEEE Network , v.35 , 2021 https://doi.org/10.1109/MNET.012.2000770 Citation Details
Kim, Minsung and Mandrà, Salvatore and Venturelli, Davide and Jamieson, Kyle "Physics-inspired heuristics for soft MIMO detection in 5G new radio and beyond" Proceedings of the 27th Annual International Conference on Mobile Computing and Networking , 2021 https://doi.org/10.1145/3447993.3448619 Citation Details
Kim, Minsung and Venturelli, Davide and Jamieson, Kyle "Leveraging quantum annealing for large MIMO processing in centralized radio access networks" The 31st ACM Special Interest Group on Data Communication (SIGCOMM) , 2019 10.1145/3341302.3342072 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The Quantum-Enabled Computational Techniques (QENeTs) project leveraged the development of noisy-intermediate quantum (NISQ) analog devices, to investigate if those emerging hardware platforms (or the associated physics-based technical innovations that can be run in digital hardware) could overcome computational limitations that currently are a bottleneck in wireless network performance. 


Starting from a novel formulation of the Maximum-Likelihood Decoding problem for Multi-user Multi-Input Multi-Output (MU-MIMO) problem as an energy minimization of an Ising spin Hamiltonian, we developed efficient compilations, decompositions, hybridizations, parametrizations, refined mappings and evaluation metrics to maximize the performance of the tested solvers. In particular, we tested three generations of superconducting quantum annealing processors (D-Wave 2000Q, 2000Q-LN and Advantage) using two quantum operational modes (forward and reverse annealing); parallel tempering algorithms in GPUs; and simulations of Ising Machines consisting of dynamical systems based on Kuramoto oscillators or Degenerate Optical Parametric Oscillators (DOPOs).


For each study presented in the papers, we observed that a computationally-heavy operational regime (fully specified by number of users, number of base station antennas, frequency bandwidth, and modulation/coding scheme) could be identified for which the projected performance of the decoders that leverage these quantum and quantum-inspired methods is substantially beating the state of art (in terms of throughput) represented by Minimum Mean-Square-Error (MMSE) Decoders or variations of Sphere Decoders. For the case of tests on currently accessible quantum annealers, the time overheads to program the chips and read the results before and after the pure computing time are still too high for the technology to make an impact today, and further engineering advances and system integration efforts are required to possibly observe an advantage. We identify these limitations and offer design principles for future generation quantum annealing devices and algorithms that target wireless applications. 


Notwithstanding the timeline of development of sufficiently fast quantum computers, our research indicates that physical dynamics of other quantum-inspired Ising solvers programmable efficiently on current GPUs, FPGAs or soon-to-be available photonic chips is likely leverageable in the very near term to develop ASICs that would be the enabler of large and massive MIMO networks working with good throughput and low energy consumption.


Last Modified: 09/06/2022
Modified by: Davide Venturelli

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