A Future with Quantum Machine Learning
Open accessAbstract
Could combining quantum computing and machine learning with Moore’s law produce a true “rebooted computer”? This article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.
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Massive MIMO systems and millimeter-wave spectrum are believed to have an important role in addressing the capacity demands of future communication networks.
“Massive Multiple-Input Multiple-Output (M-MIMO) systems and millimeter-Wave (mmWave) spectrum exploiting high spatial resolution and multi-gigahertz bandwidth, respectively, are believed to have an important role in addressing the capacity demands of future communication networks [12,22].”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [12]
The paper’s Abstract and “Moore’s law and superconducting electronics” section discuss quantum machine learning and hybrid computers, not wireless-network capacity. Searches for massive MIMO, millimeter-wave spectrum, spatial resolution, and bandwidth found no passage addressing the attributed claim. (LLM, conf=0.77)
Experimental — accuracy not yet measuredML can potentially assist big data analytics to realize self-sustaining and proactive wireless networks.
“ML can potentially assist big data analytics to realize self-sustaining and proactive wireless networks [12].”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [12]
The Abstract discusses potential uses of quantum machine learning, and Societal Implications discusses computerized agents and web traffic. Neither addresses big-data analytics for self-sustaining, proactive wireless networks; searches for those terms and their paraphrases found no supporting passage in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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Cites
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779 · see citing works
What is the Computational Value of Finite-Range Tunneling?
Denchev, V. S., Boixo, S., Isakov, S. V., Ding, N., Babbush, R., Smelyanskiy, V., Martinis, J., & Neven, H. (2016) · Physical Review X · doi:10.1103/physrevx.6.031015 · Cited by 399 · see citing works
Silicon CMOS architecture for a spin-based quantum computer
Veldhorst, M., Eenink, H. G. J., Yang, C. H., & Dzurak, A. S. (2017) · RePEc: Research Papers in Economics · Cited by 166 · see citing works
Computational Complexity and New Computing Approaches
DeBenedictis, E. P. (2016) · Computer · doi:10.1109/mc.2016.353 · Cited by 8 · see citing works
Self-citation
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Research works printed but not linked: 1
- [5]5. Vita: Erik P. DeBenedictis is a technical staff member at Sandia National Laboratories Center for Computing Research. He is a member of IEEE, ACM, and APS. Contact him at epdeben@sandia.gov.
Cited by
Sorted by citations
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658 · see citing works
Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models
Glover, F., Kochenberger, G., & Du, Y. (2019) · 4OR · doi:10.1007/s10288-019-00424-y · Cited by 298 · see citing works
- Authors
- DeBenedictis, E. P.
- Affiliations
- Sandia National Laboratories California (US)
- Corresponding author
- DeBenedictis, Erik P.
- Venue
- Computer
- Published
- 2018-02
- Volume
- 51
- Issue
- 2
- Pages
- 68-71
- ISSN
- 0018-9162 (ISSN-L)
- 1558-0814
- OpenAlex
- W2789080953
- Cited by
- 44
- References
- 4
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 8 · 2023: 4 · 2022: 8 · 2021: 7 · 2020: 5 · 2019: 4 · 2018: 3
- FWCI
- 2.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Licence
- Locations
- Computer (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DENA0003525, 0003525, NA0003525)
- National Nuclear Security Administration (NA0003525, DENA-0003525)
- Sandia National Laboratories (NA0003525, DE‐NA,0003525)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- quantum machine learning, quantum computing, hybrid computing, Moore's law, energy efficiency, computer architecture, future of computing
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