Quantum Artificial Intelligence at NASA

Transcription

1 Quantum Artificial Intelligence at NASA Alejandro Perdomo-Ortiz Senior Research Scientist, Quantum AI Lab. at NASA Ames Research Center and at the University Space Research Association (USRA) 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 Funding support: September 26, 2017 National Harbor, MD, USA

2 NASA quantum computing efforts Biswas, et al. Parallel Computing (2016) perspective article Quantum-enhanced applications QC programming SOA classical solvers Simulation tools Physics Insights Analytical methods Application focus areas Planning and scheduling Fault Diagnosis Machine Learning Outcomes from application investigations Future QC architectural design elements Programming and parameter setting Hybrid quantum-classical approaches Application-specific and general classical solvers Physical insights into and intuitions for QC NASA QuAIL team has published 40+ papers since 2012

3 Analytical and numerical tools and expertise Quantum diffusion DMRG MPO Master equations Linear chains Tensor Networks Feynman diagramatics Semi-classical approx. ML for QC Hopfield model Instantons Sherrington-Kirkpatrick model QMC p-spin models Physics insights into QA algorithms Kechedzhi, et al., PRX 6, (2016) Knysh. Nat. Comm 7,12370 (2016) Smelyanskiy, et al. PRL 118, (2017) Jiang, et al. PRA 95, (2017) Jiang, et al. arxiv: (2017) Benchmarking QA resources and architectures Venturelli, et al. PRX 5, (2015) Mandrà, et al. PRA 94, (2016) Mandrà, et al. Quantum Sci. Tech. 2, 3 (2017) Mandrà, et al. PRL, 118, (2017) Perdomo-Ortiz, et al. arxiv: (2017)

4 QC programming expertise for real-world applications Planning/ scheduling Machine learning Planning/Scheduling Applications - Tran et al. So-CS-16 (2016) Fault diagnosis Mars-Lander activity sched. - Venturelli et al., IJCAI-COPLAS (2016) - Tran et al. Workshops AAAI 16. Hybrid approaches Embedding techniques Parameter setting Device characterization Airport runway sched. - Wang et al. ICAPS 17 (2017) Fault diagnosis Applications 0 i 1 AND OR c o 0 PUBO (P) QUBO (Q) Quantum annealer - Perdomo-Ortiz et al. arxiv: (2015) 0 i 2 XOR 1 AND - Perdomo-Ortiz et al. Eur. Phys. J. Spec. Topics. 224, (2015). 0 c i XOR Σ 1 s i s j s i s kj s k s i s j s ik ss j s l k s l s P (f, x) s Q (f, x, a) - Perdomo-Ortiz et al. arxiv: (2017)

5 QC programming expertise for real-world applications Programming architectures beyond QA Planning/ scheduling Machine learning Google Martinis lab - Jiang et al., PRA 95 (6), (2017). - Wang et al., arxiv: (2017) Hybrid approaches Embedding techniques Fault diagnosis Device characterization Rigetti - Hadfield et al., arxiv: (2017) - Venturelli, et al., arxiv: (2017) - Neill, et al. arxiv: (2017) Machine learning (Bayesian Nets & Quantum sampling) IN: configs. OUT: params. QA {J, h} - O Gorman, et al. Eur. Phys. J. Spec. Topics. 224, (2015) - Benedetti, et al., PRA 94 (2), (2016) - Benedetti, et al., arxiv: v2. (2016) - Benedetti, et al., arxiv: (2017). - Perdomo-Ortiz, et al. arxiv: (2017) [perspective article]

6 D-Wave 2x at NASA: 20% of time available to public through light-weight proposal process Competitive Selections Cycle 1: 8 of 14 selected 57% Cycle 2: 5 of 10 selected 60% Diversity of Organizations 12 Universities 67% 6 Industrial Research Organizations 33% Diversity of Countries 11 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

7 Universities Space Research Association (USRA) Quantum Artificial Intelligence (AI) Laboratory University 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. DEADLINE SEPT 30 EXTENDED TODAY TO OCT 30 info: dventurelli@usra.edu Free Compute Time Workshops, Seminars & Training Visiting Scientist Program Joint Proposals Available for qualified research projects from universities and industry. Projects are selected through an annual competitive selection process. University and industry participants are invited to participate in workshops and other educational opportunities. Universities and industry can sponsor a visiting scientist to work side-by-side with Quantum AI Lab team members. University and industry scientists are invited to collaborate on proposals to sponsored research programs.

8 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) 5% and 3.5% precision level for h and J 15 mk Max operating temperature (13 mk nominal) 3.5% and 2% precision level for h and J 15 mk Max operating temperature (nominal to be measured) 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

9 THANK YOU FOR YOUR ATTENTION NASA Ames Research Center 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,

10 Support slides