Site Report: USRA Quantum Computing AI Lab · 2019-04-10 · Site Report: USRA Quantum Computing AI Lab (NASA-Google-USRA collaboration) ... Leveraging Quantum Annealing for Large

Davide Venturelli – QUBITS EUROPE – March 26th 2019

Site Report: USRA Quantum Computing AI Lab(NASA-Google-USRA collaboration)

Davide Venturelli,Quantum Artificial Intelligence Laboratory,USRA:RIACS Science Operations Manager

and Quantum Computing Task LeadResearch Scientist @ NASA Ames Research Center [email protected] ([email protected])

Fund

ing by

NASAEleanor RieffelJeremy FrankChris TeubertRupak Biswas

USRADavide VenturelliZhihui WangStuart HadfieldFilip WudarskiEugeniu PlamadealaJeffrey MarshallNorman TubmanVanesa Gomez-GonzalezDavid Bell

Fellowship/VisitingBryan O'Gorman(Tad Hogg)(Boris Altshuler)

https://ti.arc.nasa.gov/tech/dash/groups/physics/quail/

SGTSalvatore MandràGianni MossiWalter VinciAdam Max Wilson

External Collaborators (2018)Kyle Jamieson, Minsung Kim (Princeton), Alexei Kondryatev (Standard Chartered Bank), Stefano Casalegno (FFSS), Bibek Pokharel (USC), Hemant Shukla (Siemens)

Recent Reference papers: The power of pausing: advancing understanding of thermalization in experimental quantum annealers (J.Marshall, D.Venturelli, I.Hen, E.G.Rieffel) – arXiv:1810.05881 (2019) Reverse quantum annealing approach to portfolio optimization problems (D.Venturelli, A.Kondratyev) – Quantum Machine Intelligence Journal, arXiv:1810.08584: (2019) Leveraging Quantum Annealing for Large MIMO Processing in Cloud-Based Radio Access Networks (M.Kim, D.Venturelli, K.Jamieson) – to appear Performance of Quantum Annealers on Hard Scheduling Problems (B. Pokharel, D. Venturelli, E.G. Rieffel) – to appear

mailto:[email protected]


■ ■ Outline

Brief history of the use of the D-WAVE 2000Q at NASA Ames Phenomenological model of annealing dynamics Experiments with the Pause Performance Distribution

Embedded Spin Glasses Forward annealing versus Reverse Annealing Performance examples on embedded problems Wireless Networks Maximum Likelihood Decoding Financial Portfolio Optimization

Conclusions and Future Directions

1


■ ■ Brief History of the D-Wave Quantum Annealers

ILIAC IV, NASA Ames Research CenterFirst massively parallel computer 64, 64-bit FPUs and a single CPU 50 MFLOP peak, fastest computer at

the time

Finding good problems and algorithms was challenging.«Would computers ever be able to compete with wind tunnels?»

NASA, Google and USRA formed a three-way collaboration focused on Artificial Intelligence and Quantum Computing (2012-present)

Universities,Industry,Startups, NSF


■ ■ Update on Usage of the NASA Ames D-Wave Machine

Quantum RFP

Competitive SelectionsCycle 1 (512 qubit processor): 8 of 14 selected – 57%

Cycle 2 (1152 qubit processor): 10 of 15 selected – 67%Cycle 3 (2048 qubit processor): 15 of 19 selected – 79%

Diversity of Selected OrganizationsApprox 60% Universities + 40% Industrial Research Organizations Approx 60% U.S. Organizations + 40% International OrganizationsComputer Science, Physics, Mathematics, Electrical Engineering, Operations Research, Chemistry, Aerospace Engineering, Finance

Diversity of ResearchQuantum Physics -> Algorithms -> Applications

Machine Learning for Image Analysis, Communications, Materials Science, Biology, Finance

RFP CYCLE 1 & 2 SELECTIONS

RFP CYCLE 3 (extract)

https://tinyurl.com/USRA-RFP2019


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 mKnominal)

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 h and J To be measured.

Annealing time 20 µs Annealing time improved 4x (5µs)Readout time improved (120µs)

Annealing time improved 5x (1µs)Initial programming time improved 20% (9 ms). Extented J, anneal offset, pause and quench features. (+h field schedules [2019])

■ ■ The D-Wave machine at NASA Ames 5m


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 mKnominal)

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 h and J To be measured.

Annealing time 20 µs Annealing time improved 4x (5µs)Readout time improved (120µs)

Annealing time improved 5x (1µs)Initial programming time improved 20% (9 ms). Extented J, anneal offset, pause and quench features. (+h field schedules [2019])

■ ■ The D-Wave machine at NASA Ames 5m

B.Bokharel, D.Venturelli, E.Rieffel (2019)


■ ■ Recap of D-Wave 2000Q parameters, schedules

Pause

Coherent Evolution does not describes the dynamics:

+ coupling to a bath(s) + dynamics of the bath(s)


J.Marshall, DV, I.Hen, E.Rieffel (2019)

Quasi-static evolution

■ ■ Phenomenological model of annealing dynamics

Initial evolution locked in the ground state

At Equilibrium with the environment

Out of equilibrium with the environment(trying to cool down)

Frozen, transitions suppressed. State is some NEQ distribution

Equilibrium Excess Energy <E>-E0

10m


■ ■ Experiments with the pause: pause location

Out of equilibrium with the environment(trying to cool down)

Theory advances on open system quantum annealing: Weak Coupling Limit

o Albash, Boixo, Lidar, Zanardi, NJP 2012 Solvable Toy Models (nonperturbative)

o Smelyanskiy , Venturelli et al., PRL 2017o Kechedzhi , Smelyanskiy, PRX 2016

Realistic Simulationso Boixo , Smelyanskiy et al, Nature 2016o Smirnov, Amin , NJP 2018


■ ■ Experiments with the Pause: pause duration

783 qubits10k num reads1ms anneal time

12 qubits10k num reads1ms anneal time

50 instances, planted problems10k num reads1ms anneal time, 100ms pause

The pause always improves results(order of magnitudes TTS improvement!)

15m

Logarithmic dependence


■ ■ Experiments with the Pause: Distribution

Box plot of the difference between the pause point for which the maximal R2

value is found (i.e., the closest fit to a Boltzmann distribution based on G=0–estimated by entropic sampling technique), and the optimal pause point, with problem size.

55 instances, planted 10k num reads1ms anneal time100ms pause time

Classical Boltzmann Distribution

Best fit to a quantum Boltzmann distribution

12 qubits10k num reads1ms anneal time1000ms pause time

20m

Conjecture→at optimal pause point, the distribution is always well approximated by a classical Boltzmann for large N.


■ ■ Take home messages from pausing anneal in native sparse Ising problems

1. We experimentally observed orders of magnitude improvement in performance with respect to unpausedannealing for certain problems of the planted-solution type by the use of a pause at the optimal location.

2. The optimal pause location is found to occur after the location of minimum gap, as expected; we conjecture pausing after the minimum gap allows for the ground state to repopulate after dissipative transitions which occur during the region of the minimum gap.

3. we provide evidence suggesting thermalization to a classical Boltzmann distribution is occurring in problems containing up to 500 qubits.The temperature of this classical distribution however is different from the physical temperature of the device.


■ ■ Pause and Reverse Annealing Protocol

One more «knob» to try in empirical research heuristics.o D-Wave Whitepaper 14-1018A-A (2018)o Ottaviani et al. arXiv:1808.08721v1 (2018)

Some theoretical research on RQA:o Ohkuwa et al. PRA (2018)o Passarelli et al. arXiv:1902.06788 (2019)

• 10-100x improvement• Circumvent some bottlenecks if start

is good enough

Which initialization? Where to stop? For how long?


■ ■ Benchmarks on Applications Embedded on Fully-Connected Graphs

MIMO Wireless Maximum-Likelihood Decoding

Fund-of-Funds Portfolio Optimization

Decoded signal all possible signals: strings of symbols

Received signal

Wireless channel estimated via preambles – changes at ms scale, noisy

Asset preference (sharpe ratio bucket) Asset correlation matrix

Boothby et al.(2015)

Up to ≈40 users embeddable for Quadrature-phase shift keying (QPSK) [4 symbols alphabet]

+adding noise

Up to ≈60 assets embeddable for a fully-correlated problem.

Median Time to solution at 99% confidence

TTS = log(0.01)/log(1-P)

30m


■ ■ Wireless Decoding Problem: Embedding Parameter Setting, Anneal Time Optimization, Pause Location

In the paper we report BPSK, QPSK, 16QAM and with AWGN –here only QPSK.

Tests done on 10 random-H, random-y instances, 50k anneals minimum.

First time applied problem use the «extended J» feature

Best anneal time 1ms

Majority voting to decode chains representing logical variables

We look for the best pause for all parameter settings – one order of magnitude in TTS can be gained. (Only small pauses are relevant for the C-RAN MIMO Wireless problem)

( 18 users )

The expected best BER after Na anneals:

TTB10-6

MIMO Wireless Maximum-Likelihood Decoding

M.Kim, D.Venturelli, K.Jamieson(2019)


■ ■ Portfolio Optimization Problem: Greedy Pre-processing

Tested 30 instances – random parameters from NAV data statistics

Run only the instances that don’t solve


Results consistent with Venturelli et al. PRX 2015, Hamerly et a. 2019

Reverse Anneal doesn’t change the setting.

■ ■ Portfolio Optimization: Embedding Parameter Setting, Reverse Pause Optimization

JF and pause location can be set independently Shorter pause is more performant (but non-zero)


■ ■ ResultsFund-of-Funds Portfolio Optimization

Venturelli, Kondryatev Quantum Machine Intelligence Journal 2019 (to appear)


■ ■ Other NASA-Sponsored Project : Convergent Aeronautics Solution

Feasibility study: Using quantum-classical hybrids to build a secure jam-free network to ensure the availability of a UAS Traffic Management (UTM) network against communication disruptions

Kopardekar, P., Rios, J., et. al., Unmanned Aircraft System Traffic Management (UTM) Concept of Operations, 2016

Future • Higher vehicle density• Heterogeneous air vehicles• Mixed equipage• Greater autonomy• More vulnerability to communications disruptions

Explore quantum approaches to• Robust network design• Track and locate of a moving jammer • Secure communication of codes supporting anti-jamming protocols

Harness the power of quantum computing and communication to address the cybersecurity challenge of availabilityUSRA quantum physicists are key team members

• Joint with NASA Glenn, who are working on Quantum Key Distribution (QKD) for spread spectrum codes.


■ ■ Conclusions

Open Positions: Senior Scientist (10+ yrs experience) Scientist (3+ years postdoc experience) Associate Scientist (postdoc level) Grad Student Intern (3-8 months)

Open Call to use the D-Wave 2000Q (free)https://tinyurl.com/USRA-RFP2019

Submit to Quantum Machine Intelligence Journal (Springer)https://www.springer.com/engineering/computational+intelligence+and+complexity/journal/42484

[email protected]([email protected])

(2017 – 3 spots)Stuart Hadfield (Columbia)David Roberts (MIT)Bibek Pokharel (USC)

(2018 – 6 spots)Benjamin Villalonga-Correa (UIUC)Aniruddha Sabat (U Maryland)Sasha Nanda (Caltech)Jeffrey Marshall (USC)Riccardo Mengoni (Univ. Verona)Zhenyi Qi (U Michigan)

(2019 – 8 spots)Andrea di Gioacchino (Univ. Milano)David Bernal (CMU)Minsung Kim (Princeton). . .

Interest: use and modeling of advanced features of the D-Wave machine.

mailto:[email protected]

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Site Report: USRA Quantum Computing AI Lab · 2019-04-10 · Site Report: USRA Quantum Computing AI Lab (NASA-Google-USRA collaboration) ... Leveraging Quantum Annealing for Large