Quantum Artificial Intelligence at NASA
NASA QuAIL team: Andre Petukhov, Bryan O’Gorman, Davide Venturelli, Eleanor Rieffel (Lead), John Realpe-Gómez, Kostyantyn Kechedzhi, Marcello
Benedetti, Max Wilson, Salvatore Mandrà, Zhang Jiang, Zhihui Wang
September 26, 2017 National Harbor, MD, USA
Alejandro Perdomo-OrtizSenior Research Scientist, Quantum AI Lab. at NASA Ames Research Center
and at the University Space Research Association (USRA)
Funding support:
Application focus areasPlanning and schedulingFault DiagnosisMachine Learning
Outcomes from application investigationsFuture QC architectural design elementsProgramming and parameter settingHybrid quantum-classical approaches
Application-specific and general classical solvers
Physical insights into and intuitions for QC
Quantum-enhanced applications
QC programmingSOA classical solvers
Physics InsightsAnalytical methodsSimulation tools
Biswas, et al. Parallel Computing (2016) – perspective article
NASA QuAIL team has published 40+ papers since 2012
NASA quantum computing efforts
Analytical and numerical tools and expertisePhysics insights into QA algorithms
Benchmarking QA resources and architectures
Kechedzhi, et al., PRX 6, 021028 (2016)
Knysh. Nat. Comm 7,12370 (2016)
Smelyanskiy, et al. PRL 118, 066802 (2017)
Jiang, et al. PRA 95, 012322 (2017)
Jiang, et al. arXiv:1708.07117 (2017)
Linear chains
Quantum diffusion
Tensor Networks
DMRG
Semi-classical approx.
Sherrington-Kirkpatrick model
Hopfield model
p-spin models
Instantons
MPO
ML for QCFeynman diagramatics
Master equations
QMC
Venturelli, et al. PRX 5, 031040 (2015)
Mandrà, et al. PRA 94, 022337 (2016)
Mandrà, et al. Quantum Sci. Tech. 2, 3 (2017)
Mandrà, et al. PRL, 118, 070502 (2017)
Perdomo-Ortiz, et al. arXiv:1708.09780 (2017)
Planning/scheduling
Hybrid approaches
QC programming expertise for real-world applications Planning/Scheduling Applications
Fault diagnosis Applications
- Tran et al. So-CS-16 (2016)
- Venturelli et al., IJCAI-COPLAS (2016)
- Tran et al. Workshops AAAI’16.
- Wang et al. ICAPS’17 (2017)
Mars-Lander activity sched.
Airport runwaysched.
PUBO (P) QUBO (Q)
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Quantum annealer
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Parameter setting
Fault diagnosis
Machinelearning
Device characterizationEmbedding
techniques
- Perdomo-Ortiz et al. arXiv:1503.01083 (2015)
- Perdomo-Ortiz et al. Eur. Phys. J. Spec. Topics. 224, 131-148 (2015).
- Perdomo-Ortiz et al. arXiv:1708.09780(2017)
Machine learning (Bayesian Nets & Quantum sampling)
Programming architectures beyond QAGoogle Martinis lab
Rigetti
IN: configs
.
OUT: params.
QA {J , h}
- O’Gorman, et al. Eur. Phys. J. Spec. Topics. 224, 163- 188 (2015)- Benedetti, et al., PRA 94 (2), 022308 (2016) - Benedetti, et al., arXiv:1609.02542v2. (2016)- Benedetti, et al., arXiv:1708.09784 (2017).- Perdomo-Ortiz, et al. arXiv:1708.09757. (2017)[perspective article]
- Jiang et al., PRA 95 (6), 062317 (2017).
- Wang et al., arXiv:1706.02998 (2017)
- Hadfield et al., arXiv:1709.03489 (2017)
- Venturelli, et al., arXiv:1705.08927 (2017)
- Neill, et al. arXiv:1709.06678 (2017)
Planning/scheduling
Hybrid approaches
Fault diagnosis
Machinelearning
Device characterizationEmbedding
techniques
QC programming expertise for real-world applications
D-Wave 2x at NASA: 20% of time available to public through light-weight proposal processCompetitive Selections
Cycle 1: 8 of 14 selected – 57% Cycle 2: 5 of 10 selected – 60%
Diversity of Organizations12 Universities – 67%6 Industrial Research Organizations – 33%
Diversity of Countries11 U.S. Organizations – 59%7 International Organizations – 41%
17 Research Papers Published or in Pre-Print to Date that used the Quantum AI Lab D-Wave machine (7 in 2015, 10 in 2016)
CYCLE 1 SELECTIONS
CYCLE 2 SELECTIONS – Part I
CYCLE 2 SELECTIONS – Part II
http://www.usra.edu/quantum/rfp/
Universities Space Research Association (USRA)
Quantum Artificial Intelligence (AI) LaboratoryUniversity and Industry Engagement Program
A program to enable a diversity of research in quantum computing, and develop the next generation workforce with expertise in quantum computing.
http://www.usra.edu/quantum/rfp/
Free Compute Time
Available for qualified research projects from universities and
industry. Projects are selected through an annual competitive
selection process.
Joint Proposals
University and industry scientists are invited to collaborate on
proposals to sponsored research programs.
Visiting Scientist Program
Universities and industry can sponsor a visiting scientist to work side-by-side with Quantum AI Lab
team members.
Workshops, Seminars & Training
University and industry participants are invited to participate in
workshops and other educational opportunities.
DEADLINE SEPT 30 – EXTENDED TODAY TO OCT 30info: [email protected]
Upgrade from Vesuvius to Washington to Whistler
D-Wave Two™ D-Wave 2X™ D-Wave 2000Q™512 (8x8x8) qubits “Vesuvius” 1152 (8x12x12) qubit
“Washington”2048 (8x16x16) qubit “Whistler”
509 qubits working – 95% yield 1097 qubits working – 95% yield 2038 qubits working – 97% yield
1472 J programmable couplers 3360 J programmable couplers 6016 J programmable couplers
20 mK max operating temperature (18 mK nominal)
15 mK Max operating temperature (13 mK nominal)
15 mK Max operating temperature (nominal to be measured)
5% and 3.5% precision level for h and J
3.5% and 2% precision level for hand J
To be measured.
Annealing time 20 µs Annealing time improved 4x (5µs) Annealing time improved 5x (1µs)Initial programming time improved 20% (9 ms). New anneal offset, pause and quench features.
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THANK YOU FOR YOUR ATTENTION
https://usra-openhire.silkroad.com/epostings/index.cfm?fuseaction=app.jobInfo&version=1&jobid=629
Opportunities at NASA Quantum AI Lab. (NASA QuAIL) at different levels: internships, postdoc, or Research Scientist.
For details, please contact:Eleanor Rieffel: NASA QuAIL Lead,[email protected]
NASA Ames Research Center
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