Site Report Sebastian Feld, Thomas Gabor, Christoph Roch ... · Keynote – An Introduction to...

Site Report

Sebastian Feld, Thomas Gabor, Christoph Roch

LMU Munich

First International Workshop on Quantum Technology and Optimization Problems

Nick Chancellor Bo Ewald Markus Friedrich Thomas Gabor Markus Hoffmann Faisal Shah Khan Dieter Kranzlmüller Luke Mason Wolfgang Mauerer Catherine C. McGeoch Masayuki Ohzeki Jonathan Olson Dan O’Malley Tobias Stollenwerk

Durham University, UK D-Wave Systems, Canada LMU Munich, Germany LMU Munich, Germany Google, Germany Khalifa University, Abu Dhabi LRZ, Germany Science & Techn. Facilities Council, UK OTH Regensburg, Germany D-Wave Systems, Canada Tohoku University, Japan Zapata Quantum Computing, USA Los Alamos National Laboratory, USA DLR, Germany

Sebastian Feld Claudia Linnhoff-Popien

LMU Munich, Germany LMU Munich, Germany

Program Committee

Workshop Organizers

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https://www.springer.com/us/book/9783030140816

09:00 Conference Opening Sebastian Feld (LMU Munich, Germany), Claudia Linnhoff-Popien (LMU Munich, Germany)

09:10 Keynote – An Introduction to Quantum Computing and its Application Robert H. (Bo) Ewald (D-Wave Systems, Canada)

09:50 Session 1 – Analysis of Optimization Problems Chair: Michel Barbeau, Carleton University, Canada

10:45 Coffee Break

11:15 Session 2 – Quantum Gate Algorithms Chair: Sebastian Feld, LMU Munich, Germany

12:30 Lunch Break

13:30 Session 3 – Applications of Quantum Annealing Chair: Catherine C. McGeoch, D-Wave Systems, Canada

15:15 Coffee Break

15:45 Session 4 – Foundations and Quantum Technologies Chair: Wolfgang Mauerer, OTH Regensburg, Germany

17:15 Break

18:30 City Tour

20:00 Reception 5

S1 – Analysis of Optimization Problems

Embedding inequality constraints for quantum annealing optimization Tomás Vyskocil (Los Alamos National Laboratory, USA); Scott Pakin (Los Alamos National Laboratory, USA); Hristo N. Djidjev (Los Alamos National Laboratory, USA)

Assessing Solution Quality of 3SAT on a Quantum Annealing Platform Thomas Gabor (LMU Munich, Germany); Sebastian Zielinski (LMU Munich, Germany); Sebastian Feld (LMU Munich, Germany); Christoph Roch (LMU Munich, Germany); Christian Seidel (MVI Proplant, Germany); Florian Neukart (Volkswagen Group of America, USA); Isabella Galter (Volkswagen Data:Lab, Germany); Wolfgang Mauerer (OTH Regensburg; Siemens Corporate Research); Claudia Linnhoff-Popien (LMU Munich, Germany)

Principles and Guidelines for Quantum Performance Analysis Catherine C McGeoch (D-Wave Systems, Canada)

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S2 – Quantum Gate Algorithms

Nash embedding and equilibrium in pure quantum states Faisal Shah Khan (Khalifa University, Abu Dhabi); Travis S. Humble (Oak Ridge National Lab, USA)

A Quantum Algorithm for Minimising the Effective Graph Resistance upon Edge Addition Finn de Ridder (Radboud University, Netherlands); Niels Neumann (TNO, Netherlands); Thijs Veugen (TNO, Netherlands; CWI, Netherlands); Robert Kooij (Singapore University of Technology and Design, Singapore; Delft University of Technology, Netherlands)

Variational Quantum Factoring Eric Anschuetz (Zapata Computing, USA); Jonathan Olson (Zapata Computing, USA); Alán Aspuru-Guzik (Zapata Computing, USA); Yudong Cao (Zapata Computing, USA)

Function Maximization with Dynamic Quantum Search Charles Moussa (TOTAL American Services, USA; Oak Ridge National Laboratory, USA); Henri Calandra (TOTAL SA, France); Travis Humble (Oak Ridge National Laboratory, USA)

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S3 – Applications of Quantum Annealing Flight Gate Assignment with a Quantum Annealer Tobias Stollenwerk (DLR, Germany); Elisabeth Lobe (DLR, Germany); Martin Jung (DLR, Germany)

Solving Quantum Chemistry Problems with a D-Wave Quantum Annealer Michael Streif (Volkswagen Group, Germany); Florian Neukart (Leiden University, Netherlands; Volkswagen Group of America, USA); Martin Leib (Volkswagen Group, Germany)

Solving large Maximum Clique problems on a quantum annealer Elijah Pelofske (Los Alamos National Laboratory, USA); Georg Hahn (Lancaster University, U.K.); Hristo Djidjev (Los Alamos National Laboratory, USA)

Quantum Annealing based Optimization of Robotic Movement in Manufacturing Arpit Mehta (BMW AG, Germany); Murad Muradi (BMW AG, Germany); Selam Woldetsadick (BMW AG, Germany)

Quantum Annealing of Vehicle Routing Problem with Time, State and Capacity Hirotaka Irie (DENSO Corporation, Japan); Goragot Wongpaisarnsin (Toyota Tsusho Nexty Electronics, Thailand); Masayoshi Terabe (DENSO Corporation, Japan); Akira Miki (DENSO Corporation, Japan); Shinichirou Taguchi (DENSO Corporation, Japan)

Boosting quantum annealing performance using evolution strategies for annealing offsets tuning Sheir Yarkoni (D-Wave Systems, Canada; Leiden University, Netherlands); Hao Wang (Leiden University, Netherlands); Aske Plaat (Leiden University, Netherlands); Thomas Bäck (Leiden University, Netherlands)

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S4 – Foundations and Quantum Technologies

Quantum Photonic TRNG with Dual Extractor Mitchell A. Thornton (Southern Methodist University, USA); Duncan L. MacFarlane (Southern Methodist University, USA)

Secure Quantum Data Communications Using Classical Keying Material Michel Barbeau (Carleton University, Canada)

Continuous-variable Quantum Network Coding Against Pollution Attacks Tao Shang (Beihang University, China); Ke Li, Ranyiliu Chen (Beihang University, China); Jianwei Liu (Beihang University, China)

On the Influence of Initial Qubit Placement During NISQ Circuit Compilation Alexandru Paler (Johannes Kepler University, Austria)

Towards a Pattern Language for Quantum Algorithms Frank Leymann (University of Stuttgart, Germany)

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Second International Workshop in Quantum Technology and Optimization Problems

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IEEE Global Communications Conference (IEEE Globecom) 9-13 December 2018, Abi Dhabi, UAE

4th Workshop on Quantum Communications and Information Technology

(QCIT'18)

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https://ieeexplore.ieee.org/abstract/document/8644358

Motivation

If – and to what extent – can quantum annealing be advantageously

applied in selected problems of 3D geometry compressing

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CSG Representation for Point Clouds

Point clouds represent real-world objects scanned by 3D sensors

Constructive Solid Geometry (CSG) representations approximate objects using

geometric primitives and boolean operators

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CSG Extraction as Combinatorial Optimization

Try all possible combinations of tree topologies and node assignments

Use objective function that minimizes geometric error and penalizes large trees

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CSG Tree Extraction Pipeline

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Point Cloud Processing & Primitive Detection

Figure: arXiv:1811.08988v1

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Topology Constraints

Set 𝑃 of geometric primitives

Surface 𝑆 to represent

Intersection graph 𝐺

Set 𝑈 of fundamental products

A B

c

D

E

F

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Problem Partitioning

A B

c

D

E

F

Topological problem partitioning: each maximal clique of intersection graph 𝐺 is a partition Better scaling with number of primitives 𝑃

Clique decision problem

Maximal clique problem

Maximal clique enumeration

Enumerate Expression Combinations

Observation: Some parts of the surface can be represented with less operations If all possible expression combinations are considered, the optimal tree can be found For each clique compute set of all possible subsets of fundamental products

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Derive Minimal Expression

Given set 𝑉 as the union of fundamental product subsets and set 𝑈′ of fundamental products that represent surface 𝑆 Find minimum exact cover 𝑉∗ which is a subset of 𝑉 such that each element of 𝑈′ is covered by exactly one subset in 𝑉∗ Ising formulation of minimum exact cover exists

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Optimizing Geometry Compression using QA

Some promising links for incorporating QA into geometry

compression

Probably a quantum-classical hybrid approach

Elaborate maximal clique enumeration

Heuristic that doesn’t use all possible subsets of fundamental

products

Future Work

Conclusion

Q-Nash

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Paper at: https://arxiv.org/abs/1903.06454

https://arxiv.org/abs/1903.06454

Game Theory

Graphical Game (n-player): • Each player has a set of strategies/actions

• Graph with nodes as players and edges as dependencies A player is only in a game with his neighbors

Player`s payoff is only defined by the joint action with his neighbors

A joint action (strategy profile) is called global, if all players of a game play one of their actions

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(Pure) Nash Equilibrium & Best Response

• A global strategy profile is a Nash equilibrium if no player can do better by unilaterally changing his strategy

• the best response is the strategy (or strategies) which produces the most favorable outcome for a player, taking other players' strategies as given

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[1] https://www.thoughtco.com/the-prisoners-dilemma-definition-1147466

[1]

Q-Nash (Phase 1)

Determining best response strategy sets of every player

• Using classical computation

• Example:

best response strategies of player A:

{A0,B0,C0},{A1,B0,C1},{A0,B1,C0},{A1,B1,C0},{A1,B1,C1},{A1,B2,C0},{A0,B2,C1}

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Q-Nash (Phase 2)

• Finding PNE in best response strategy sets (based on Set Cover Problem )

• Using quantum annealing, the corresponding QUBO is:

Additional last constraint ensures that exactly n best response strategy (sets) are chosen

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[2] https://www.frontiersin.org/articles/10.3389/fphy.2014.00005/full

[2]

Q-Nash (Phase 2)

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Visualisation of last QUBO constraint:

Solution Quality

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Computational Results

• Find embedding: constant

• Determining best responses: polynomial

• QBSolv classic: „quite unpredictable“

• QA time: relatively constant

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Gate Assignment Problem

in cooperation with


Given a schedule of incoming and outgoing flights.

How can the planes be best assigned to airport gates?

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• flight-to-flight costs • (+) amount of transferring passengers

• gate-to-gate costs • (+) travelling distance between gates

• (-) exposure to local retailers

• flight-to-gate match • (+) airplanes don‘t fit gate

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• Run in hybrid mode with qbsolv • Fixed number of gates (68) • Scaling flights from 3 to 208

With the problem size growing larger

also the problem difficulty increases with the scaling

Pre-/Postprocessing increases with size of the problem, but the time spent in the optimization part increases more drastically


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• Run classically with qbsolv • Scaling problem size • Kept difficulty roughly the same

(ratio of flights/gates ≈ 2.5)

Overall computation time increases (at least) quadratically with the problem size

Next steps: Compare solution quality to classical algorithms

Bayesian Inference Jonas Nüßlein

Bachelor‘s Thesis

A throwback to last week…

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Bayesian Networks

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Bayesian Networks

Most Probable Explanation

Given: The network above and the observation GRASS WET

Question: What is the most probable explanation?

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Computing Inference

(1) partition Bayesian network into families of dependent variables

(2) compute most probable explanation with respect to families

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Computing Inference

(1) partition Bayesian network into families of dependent variables

(2) compute most probable explanation with respect to families

QUBO

QUBO

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Results

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Generate 100 random Bayes networks for varying parameters

• 11 variables, 2 given variables, 3 avg #parents, 3 avg #states

Results

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avg QUBO size: 37 qubits, 77.4% weights used

Results

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performance (avg achieved probability of explanation) normed for GLS++ = 1:

GLS+ = 0.913

qbsolv = 0.955

Results

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GLS+ = 0.913

qbsolv = 0.955

• 30 variables, 2 given variables, 1.5 avg #parents, 5 avg #states



GLS+ = 0.323

qbsolv = 0.355

UQ Sebastian Zielinski

Practical Work

Questions

1) How would you incorporate quantum computers into a larger business application?

2) How can multiple quantum machines be integrated dynamically?

UQ Approach

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Questions

1) How would you incorporate quantum computers into a larger business application?

2) How can multiple quantum machines be integrated dynamically?

UQ Approach

Answer

Build a common interface

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UQ Architecture

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PlanQK Plattform und Ökosystem für Quantenunterstützte Künstliche Intelligenz

platform and ecosystem for quantum-supported artificial intelligence

1) “AI researchers have often tried to build knowledge into their agents,

2) this always helps in the short term, and is personally satisfying to the researcher, but

3) in the long run it plateaus and even inhibits further progress, and

4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning.”

AI and Computation

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1) “AI researchers have often tried to build knowledge into their agents,

2) this always helps in the short term, and is personally satisfying to the researcher, but

3) in the long run it plateaus and even inhibits further progress, and

4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning.”

AI and Computation

“The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.”

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Computation Power used in AI

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Computation Power used in AI

“Since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month doubling time (by comparison, Moore’s Law had an 18 month doubling period).”

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Options for the Future

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AI experiments become more

expensive

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expensive

Progress in AI research slows

down

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expensive

Progress in AI research slows

down

We find a way to increase available computing power

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If only there was a machine…

An Awful Lot of Expertise

Quantum Platform

Domain Analysis

AI Algorithms

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PlanQK

QAI concepts

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PlanQK

QAI concepts

QAI algorithms

specialists community

Analysis Standardization

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PlanQK

QAI concepts

QAI algorithms

QAI applications



Implementation developers

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PlanQK

QAI concepts

QAI algorithms

QAI applications




Search & Order users

packaged solution

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PlanQK

QAI concepts

QAI algorithms

QAI applications




Search & Order users

packaged solution

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The Plan for PlanQK

We are preparing a roadmap for making PlanQK reality.

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The Plan for PlanQK


• StoneOne AG • HQS Quantum Solutions GmbH • University Stuttgart • LMU Munich

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The Plan for PlanQK



funded by the German ministry for commerce (BMWi)

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The Plan for PlanQK




describing a larger follow-up project with many more partners (including you?)

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The Plan for PlanQK





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also funded by the BMWi?

The Plan for PlanQK





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also funded by the BMWi?

The Plan for PlanQK





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also funded by the BMWi? near-term

Stay tuned… and join us!

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