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Site Report
Sebastian Feld, Thomas Gabor, Christoph Roch
LMU Munich
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QTOP
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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
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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
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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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Q3DCV
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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
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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
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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
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Q-Nash
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Paper at: https://arxiv.org/abs/1903.06454
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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]
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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]
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Q-Nash (Phase 2)
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Visualisation of last QUBO constraint:
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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
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Gate Assignment Problem
Given a schedule of incoming and outgoing flights.
How can the planes be best assigned to airport gates?
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Gate Assignment Problem
• 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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Gate Assignment Problem
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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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Gate Assignment Problem
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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
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Bayesian Inference Jonas Nüßlein
Bachelor‘s Thesis
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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
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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
avg QUBO size: 37 qubits, 77.4% weights used
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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
avg QUBO size: 37 qubits, 77.4% weights used
performance (avg achieved probability of explanation) normed for GLS++ = 1:
GLS+ = 0.913
qbsolv = 0.955
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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
avg QUBO size: 37 qubits, 77.4% weights used
performance (avg achieved probability of explanation) normed for GLS++ = 1:
GLS+ = 0.913
qbsolv = 0.955
• 30 variables, 2 given variables, 1.5 avg #parents, 5 avg #states
avg QUBO size: 294 qubits, 28.8% weights used
performance (avg achieved probability of explanation) normed for GLS++ = 1:
GLS+ = 0.323
qbsolv = 0.355
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UQ Sebastian Zielinski
Practical Work
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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
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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
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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
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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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Options for the Future
AI experiments become more
expensive
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Options for the Future
AI experiments become more
expensive
Progress in AI research slows
down
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Options for the Future
AI experiments become more
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…
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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
specialists community
Analysis Standardization
Implementation developers
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PlanQK
QAI concepts
QAI algorithms
QAI applications
specialists community
Analysis Standardization
Implementation developers
Search & Order users
packaged solution
01.04.2019 Qubits Europe 2019 65
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PlanQK
QAI concepts
QAI algorithms
QAI applications
specialists community
Analysis Standardization
Implementation developers
Search & Order users
packaged solution
01.04.2019 Qubits Europe 2019 66
![Page 67: Site Report Sebastian Feld, Thomas Gabor, Christoph Roch ... · Keynote – An Introduction to Quantum Computing and its Application Robert H. (Bo) Ewald (D-Wave Systems, Canada)](https://reader033.vdocuments.pub/reader033/viewer/2022060303/5f08e8e47e708231d424504f/html5/thumbnails/67.jpg)
The Plan for PlanQK
We are preparing a roadmap for making PlanQK reality.
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The Plan for PlanQK
We are preparing a roadmap for making PlanQK reality.
• StoneOne AG • HQS Quantum Solutions GmbH • University Stuttgart • LMU Munich
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The Plan for PlanQK
We are preparing a roadmap for making PlanQK reality.
• StoneOne AG • HQS Quantum Solutions GmbH • University Stuttgart • LMU Munich
funded by the German ministry for commerce (BMWi)
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The Plan for PlanQK
We are preparing a roadmap for making PlanQK reality.
• StoneOne AG • HQS Quantum Solutions GmbH • University Stuttgart • LMU Munich
funded by the German ministry for commerce (BMWi)
describing a larger follow-up project with many more partners (including you?)
01.04.2019 Qubits Europe 2019 70
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The Plan for PlanQK
We are preparing a roadmap for making PlanQK reality.
• StoneOne AG • HQS Quantum Solutions GmbH • University Stuttgart • LMU Munich
funded by the German ministry for commerce (BMWi)
describing a larger follow-up project with many more partners (including you?)
01.04.2019 Qubits Europe 2019 71
also funded by the BMWi?
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The Plan for PlanQK
We are preparing a roadmap for making PlanQK reality.
• StoneOne AG • HQS Quantum Solutions GmbH • University Stuttgart • LMU Munich
funded by the German ministry for commerce (BMWi)
describing a larger follow-up project with many more partners (including you?)
01.04.2019 Qubits Europe 2019 72
also funded by the BMWi?
![Page 73: Site Report Sebastian Feld, Thomas Gabor, Christoph Roch ... · Keynote – An Introduction to Quantum Computing and its Application Robert H. (Bo) Ewald (D-Wave Systems, Canada)](https://reader033.vdocuments.pub/reader033/viewer/2022060303/5f08e8e47e708231d424504f/html5/thumbnails/73.jpg)
The Plan for PlanQK
We are preparing a roadmap for making PlanQK reality.
• StoneOne AG • HQS Quantum Solutions GmbH • University Stuttgart • LMU Munich
funded by the German ministry for commerce (BMWi)
describing a larger follow-up project with many more partners (including you?)
01.04.2019 Qubits Europe 2019 73
also funded by the BMWi? near-term
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Stay tuned… and join us!
![Page 75: Site Report Sebastian Feld, Thomas Gabor, Christoph Roch ... · Keynote – An Introduction to Quantum Computing and its Application Robert H. (Bo) Ewald (D-Wave Systems, Canada)](https://reader033.vdocuments.pub/reader033/viewer/2022060303/5f08e8e47e708231d424504f/html5/thumbnails/75.jpg)
Stay tuned… and join us!