Title
Abstract:
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Author:
Max Mustermann
Advisors:
Claudia Linnhoff-Popien
Student Thesis | Published {Month YYYY} | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors
June 2023
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Anomaly Detection using Quantum Circuit Born Machines
Abstract:
Anomaly detection is a critical component in various fields, including finance, medical diagnosis, and fraud detection. As datasets become increasingly complex and larger, traditional computers face limitations in processing power. In contrast, quantum computers oer promising solutions through the physical properties of their qubits, such as entanglement and superposition. The emergence of quantum machine learning, particularly the quantum circuit born machines (QCBMs), is introduced as a promising approach to tackle such complex problems. QCBMs are parameterized quantum circuits that can be trained to generate samples from a target distribution. The goal of this work is to leverage this ability for detecting anomalies that have a distribution dierent from that of normal data points. The effectiveness of QCBMs for anomaly detection is explored using a dataset generated by the make_blobs method from the Scikit-learn package in Python, where some outliers can be clearly distinguished from the clusters. And its performance is compared with an autoencoder model using the ROC-curve and the Matthews correlation coecient (MCC). These metrics are used to evaluate the models’ ability to detect anomalies and avoid false positives. The results show that QCBMs outperform the autoencoder when trained with a smaller dataset, indicating that QCBMs are more eective in dealing with data and can learn the underlying distribution more eciently than the autoencoder. However, both models can learn the distribution when trained with the full dataset.
Author:
Ahmad Almohamad Alissa
Advisors:
Jonas Stein, Danielle Schumann, Claudia Linnhoff-Popien
Student Thesis | Published April 2023 | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors
Efficient Quantum Circuit Architecture for Coined Quantum Walks on many Bipartite Graphs
Abstract:
Quantum walks, a quantum analog of classical random walks, have emerged as a powerful paradigm in quantum computation and simulation. While classical random walks rely on stochastic processes to explore systems, quantum walks leverage the unique properties of quantum mechanics to perform these tasks more efficiently. In particular, discrete-time quantum walks (DTQWs) have been studied extensively for their applications in graph theory, such as graph isomorphism, graph connectivity, and graph-based search problems. Despite their potential, implementing DTQWs on near-term quantum devices remains challenging. While previous works have focused on quantum circuit implementations for DTQWs with uniform coin operators, implementing non-homogeneous coin sets is a complex task that requires new approaches. This thesis presents an efficient quantum circuit architecture for implementing coined DTQWs with non-homogeneous, position-dependent coin sets on a large subset of bipartite graphs. A novel edge labeling scheme, Gray Code Directed Edges encoding, is introduced, taking advantage of Gray code for position encoding and the bipartite structure of the underlying graph to minimize the complexity of the quantum circuits representing coin and shift operators. This optimization leads to fewer gate operations, reducing the impact of noise and errors in near-term quantum devices. A labeling scheme is developed for various graph topologies, including cycle graphs, chained cylinder graphs, and square grid graphs, which are especially relevant for reinforcement learning applications. These findings offer a new perspective on the implementation of coined quantum walks and lay a foundation for future research on quantum walks with non-homogeneous coin sets.
Author:
Viktoryia Patapovich
Advisors:
Jonas Stein, Michael Kölle, Maximilian-Balthasar Mansky, Claudia Linnhoff-Popien
Student Thesis | Published July 2023 | Copyright © QAR-Lab
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Analyzing Reinforcement Learning strategies from a parameterized quantum walker
Abstract:
Reinforcement Learning has made significant progress in solving complex problems. Hence, it is not surprising that it can be found in various application domains. Quantum Computing as well is a prospering field, where big advancements could be seen over the last decades. Better quantum computers led to first experimentally proven quantum supremacy. Hence, the field of research grew which led to improvements in various application domains of quantum computing, one of them being quantum Reinforcement Learning where quantum computing is combined with classical reinforcement learning techniques. Among other approaches, quantum walks are used as quantum computational framework which is also the case in the present work. Here, the approach of using parameterized coin matrices to determine the behaviour of the walker adapted to grid graphs is used. Thereby, the parameters of the coin matrices should be learned, such that an optimized performance of the walker to perform a specific task is reached. In this thesis the feasibility of this approach applied to a grid world is investigated using grids of the size 2×2 and 4×4. Furthermore, a new concept for including additional constraints by introducing an extra environment qubit is presented and its influence on the optimization process of the parameters examined. The results can be seen as a proof of concept as for all experiments the approach used here shows better results than the random baseline. Moreover, no negative influence of the environment qubit can be detected. The results gained here are a basis for further research using this approach.
Author:
Lorena Wemmer
Advisors:
Jonas Stein, Michael Kölle, Claudia Linnhoff-Popien
Student Thesis | Published May 2023 | Copyright © QAR-Lab
Direct Inquiries to this work to the Advisors
Quantum summit 2023 in september
Jonas Stein, Research Associate of the QAR Lab at LMU Munich will provide valuable insights into the current QC research projects - especially on the highlights from the Quantum Computing Optimization Challenge.
(Jun. 13, 2023/Munich) Quantum computing isn’t just a future trend anymore – it’s here to stay and promising to solve both local and global problems in various fields that are beyond the reach of today’s computers. In September 20th-21st, the Quantum Summit will take place in Berlin, Germany. It will bring together a wide range of experts from the research community, as well as decision-makers from business and government, to share important insights in the field of quantum computing. Jonas Stein, Research Associate of the QAR Lab at LMU Munich will provide valuable insights into the current QC research projects – especially on the highlights from the Quantum Computing Optimization Challenge.
The Quantum Computing Optimization Challenge is a practical course initiated by Prof. Dr. Claudia Linnhoff-Popien, head of the Chair of Mobile and Distributed Systems at LMU Munich. It took place for the first time in 2020 and has been an integral part of the curriculum ever since. This practical course teaches the ability to model optimization problems for quantum computers, as well as an introduction to practical work with existing quantum computers. Four computers are currently available for this purpose in the QAR Lab: IBM Q System One, Rigetti Aspen-11, Fujitsu DAU, D-Wave Advantage. In cooperation with well-known partners from industry, tasks with strong relevance for practical applications are assigned each semester. The students have the opportunity to execute and compare one task on two computers. In the summer semester 2023, it takes place for the 5th timeIn the QC Optimization Challenge, use cases in areas of optimization, machine learning and simulation are on the agenda.
You can look forward to an exciting presentation in September at the Quantum Summit.
For more information about the Quatum Summit, click here.
Kick-off project launch QCHALLenge
LMU Munich's QAR-Lab is leading the QCHALLenge quantum computing project funded by Bundesministerium für Wirtschaft und Klimaschutz - the companies AQARIOS, BASF SE, BMW AG, SAP SE and Siemens AG will be partners.
(Dec. 01, 2022/Munich, Germany) On December 1st, all project partners met for the first time to advance the common vision to achieve a quantum advantage in the field of production and logistics. With the project QCHALLenge optimization problems especially in these areas should be solved using existing quantum computing (QC) hardware. For this purpose, algorithms, concepts and tools are being developed that will enable the industry to use QC in a multi-sector and low-threshold implementation. The focus will be on the automated integration of QC into existing solutions, the development of generic quantum SDKs and the expansion of know-how in the application and development of QC solutions.
Project partners and their roles:
The project partners are represented by technology experts, software manufacturers and the user industry, in order to optimally combine their know-how from as many different perspectives as possible. In this context, LMU Munich as consortium leader takes the lead of QCHALLenge and contributes its many years of experience in the field of QC software through the Quantum Applications and Research Laboratory (QAR-Lab). Since 2016, the QAR-Lab has already been doing research in the field of quantum computing and working on numerous QC industry and funding projects. Among other things, this has resulted in the middleware UQO for the hardware agnostic use of QC. AQARIOS, founded in 2021 as a spin-off of LMU Munich, especially focuses as a software and technology partner on the development and implementation of QC solutions. The companies BASF, BMW, SAP and Siemens represent the user perspective in the consortium. They are advancing QC in their business areas and have already been able to build up their know-how through numerous projects in the field of QC. Since QCHALLenge is specifically focused on the domains of production and logistics, this results, besides other topics, in use cases for the optimization of supply chains and warehouses as well as the application of QC in automation.
Goals of QCHALLenge:
QCHALLenge focuses on the integration of QC into existing software workflows. In particular, the project targets the optimization of methods in machine learning and simulation. In doing so, the consortium aims to achieve the following goals at the end of QCHALLenge:
- the development of generic programming tools and environments,
- the creation of user-friendly, engineering applications such as optimizations for specific use cases,
- the development of software solutions for the integration of conventional computers and QC systems (hybrid quantum software),
- the configuration of strategies and methods for the structured analysis of user-side problems regarding the target-oriented application and development of QC solutions.
Realization and outlook:
To bring QCHALLenge to success, the focus is on four main strategic cornerstones: In the first step, the goal is to identify suitable use cases and to work out a requirements analysis. The focus here is on which use cases are relevant in practice and also bring a possible quantum advantage. Particularly, the comparison to classical baselines will be considered and a prediction about the probability of a quantum advantage will be made. In the second step, general architectures are developed and finalized for integrating various software tools into existing software solutions. The focus here is on the interface definition for existing software solutions. In the third step, the first prototype software tools and hybrid use-case algorithms will be used. In the end, these prototypes can be further developed into a technically mature software tool. The software tools and algorithms are to be developed in such a way that they can be used operationally after the project period and made accessible to medium-sized companies primarily.
Quantum computing is the next technology that promises the potential for disruptive innovation. It offers breakthrough possibilities for solving problems that are unsolvable in practice on classical computers. It is hard to predict the opportunities that quantum computing, and quantum technology as a whole, will provide for humanity in the future. There are numerous fields of application in which it could be used. QCHALLenge starts an exciting project in the field of quantum computing, which all project partners are highly looking forward to. We can’t wait to see where this journey will take us.
5th QC Practical Course Kick-off with BASF, BMW and Siemens
Kick-off event with BASF, BMW and Siemens: 16 LMU students implement use cases on four quantum computers: IBM Q System Two, Rigetti Aspen-M-2, Fujitsu DAU and the D-Wave Advantage in the QC Optimization Challenge Practical Course.
The practical course will provide the ability to model optimization problems for quantum computers, as well as an introduction to practical work with existing quantum computers. Four computers are currently available in the QAR Lab for this purpose: IBM Q System Two, Rigetti Aspen-M-2, Fujitsu DAU, D-Wave Advantage.
In cooperation with our industry partners BASF, BMW Group and Siemens projects with high relevance for concrete applications are calculated by our students on quantum computers in this semester. With the three use cases (1) Financial Forecasting (BASF), (2) Drive Train Optimization (BMW) and (3) Train Routing (Siemens), the students will explore the main applications of quantum computing: machine learning, simulation and optimization.
We are pleased to make this possible for the 5th time and are looking forward to the results at the end of this semester.
