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: dventurelli@usra.edu

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.

8

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,eleanor.rieffel@nasa.gov

NASA Ames Research Center

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