Paper-QAI

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Insights on Training Neural Networks for QUBO Tasks

Insights on Training Neural Networks for QUBO Tasks

T. Gabor, S. Feld, H. Safi, T. Phan, and C. Linnhoff-Popien

Abstract

Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAOA) or quantum annealing (QA). Thus, we consider training neural networks in this context. We first discuss QUBO problems that originate from translated instances of the traveling salesman problem (TSP): Analyzing this representation via autoencoders shows that there is way more information included than necessary to solve the original TSP. Then we show that neural networks can be used to solve TSP instances from both QUBO input and autoencoders‘ hiddenstate representation. We finally generalize the approach and successfully train neural networks to solve arbitrary QUBO problems, sketching means to use neuromorphic hardware as a simulator or an additional co-processor for quantum computing.

1st International Workshop on Quantum Software Engineering (QSE at ICSE)

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The Holy Grail of Quantum Artificial Intelligence: Challenges in Accelerating the Machine Learning Pipeline

The Holy Grail of Quantum Artificial Intelligence: Challenges in Accelerating the Machine Learning Pipeline

T. Gabor, L. Suenkel, F. Ritz, L. Belzner, C. Roch, S. Feld, and C. Linnhoff-Popien

Abstract

We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them to a formal model for machine learning processes, we deduce four major challenges for the future of quantum artificial intelligence: (i) Replace iterative training with faster quantum algorithms, (ii) distill the experience of larger amounts of data into the training process, (iii) allow quantum and classical components to be easily combined and exchanged, and (iv) build tools to thoroughly analyze whether observed benefits really stem from quantum properties of the algorithm.

1st International Workshop on Quantum Software Engineering (QSE at ICSE)

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Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Thomas Hubregtsen, Christoph Segler, Josef Pichlmeier, Aritra Sarkar, Thomas Gabor, Koen Bertels
Abstract

Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attention of the current research is directed to algorithmic research on artificial data that is disconnected from live systems, such as optimization of systems or training of learning algorithms. In this paper we investigate the integration of quantum systems into industry-grade system architectures. In this work we propose a system architecture for the integration of quantum accelerators. In order to evaluate our proposed system architecture we implemented various algorithms including a classical system, a gate-based quantum accelerator and a quantum annealer. This algorithm automates user habits using data-driven functions trained on real-world data. This also includes an evaluation of the quantum enhanced kernel, that previously was only evaluated on artificial data. In our evaluation, we showed that the quantum-enhanced kernel performs at least equally well to a classical state-of-the-art kernel. We also showed a low reduction in accuracy and latency numbers within acceptable bounds when running on the gate-based IBM quantum accelerator. We, therefore, conclude it is feasible to integrate NISQ-era devices in industry-grade system architecture in preparation for future hardware improvements.

21st International Symposium on Quality Electronic Design (ISQED), pages 329-334

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Quantum Technology and Optimization Problems: First International Workshop

Quantum Technology and Optimization Problems:
First International Workshop

S. Feld and C. Linnhoff-Popien

Preface

Over the past decade, a wide variety of experimental quantum computing hardware has been invented and used for fundamental demonstrations in laboratories. Initial results confirm the feasibility of such hardware in real-world applications. Recently, one can observe upcoming research in areas like traffic flow optimization, mobile sensor placement, machine learning, and many more. The development of quantum computing hardware, be it in the quantum gate model or adiabatic quantum computation (quantum annealing), has made huge progress in the past few years. This started to transfer know-how from quantum technology-based research to algorithms and applications. This development is offering numerous opportunities to contribute within research, theory, applied technologies, and engineering. The First International Workshop on Quantum Technology and Optimization Problems (QTOP 2019) was held in conjunction with the International Conference on Networked Systems (NetSys 2019) in Munich, Germany, on March 18, 2019. The aim of this workshop was to connect people from academia and industry to discuss theory, technology, and applications and to exchange ideas in order to move efficiently forward in engineering and development in the exciting area of quantum technology and optimization problems. This book comprises a section containing a keynote and four sections with scientific papers. The sessions deal with the following topics that are crucial to the development of future improvements in quantum technology and optimization problems:

• Analysis of optimization problems
• Quantum gate algorithms
• Applications of quantum annealing
• Foundations of quantum technology

The international call for papers resulted in the selection of the papers in these proceedings. The selection of the papers followed a rigorous review process involving an international expert group. Each paper was reviewed by at least three reviewers. We express our gratitude to all members of the Program Committee for their valuable work. We also want to thank the members of the Mobile and Distributed Systems Group, and especially the Quantum Applications and Research Laboratory (QAR-Lab) who were responsible for the organization of the conference.

QTOP 2019, Munich, Germany, March 18, 2019, Proceedings, Springer, 2019, vol. 11413

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