Our research directions

The research focuses of the QAR-Lab are quantum optimization and quantum artificial intelligence. We are also currently working on a software platform whose core is the middleware UQO for uniform and easy access to quantum hardware.

Research at QAR-Lab:

Published Research

Test

The UQ Platform: A Unified Approach To Quantum Annealing

A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games

Approximating Archetypal Analysis Using Quantum Annealing

A Flexible Pipeline for the Optimization of Construction Trees

The Dynamic Time Warping Distance Measure as QUBO Formulation

Insights on Training Neural Networks for QUBO Tasks

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

Approximate Approximation on a Quantum Annealer

Optimizing Geometry Compression using Quantum Annealing

Torwards understanding Approximation Complexity on a Quantum Annealer (Extended Abstract)

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

Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA

A Hybrid Solution Method for the Capacitated Vehicle Routing Problem Using a Quantum Annealer

Quantum Technology and Optimization Problems: First International Workshop

Assessing Solution Quality of 3SAT on a Quantum Annealing Platform

Ongoing Research

Emerging Cooperation through Quantum Entanglement in Multi-Agent Systems

Distributed Quantum Machine Learning -Training and Evaluating a Machine Learning Model on a Distributed Quantum Computing Simulator

QUBO-Generation for (MAX-)3SAT via generative AI-Methods

Space-Efficient Quantum Optimization for the Traveling Salesman Problem via Binary Encoding of Feasible Solutions

Minimizing Teleportation and Enhancing Fidelity in Distributed Quantum Computing using a Multi-Objective Evolutionary Algorithm

Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits

Leveraging Preconditioning to Speed Up Quantum Simulation-Based Optimization

Warm Starting Variational Quantum Algorithms for Parameterized Combinatorial Optimization

Circuit Partitioning and Genetic Optimization for Efficient Qubit Distribution in Distributed Quantum Computing

Reinforcement learning-supported state preparation using parameterized quantum gates

Comparison of different hybrid quantum machine learning approaches for image classification on quantum computers

Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization

The Trainability of Quantum FederatedLearning

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits

State Preparation on Quantum HardwareUsing an Island Genetic Algorithm

Quantum Reinforcement Learning via Parameterized Quantum Walks

Explainable Time Series Forecasting using exogenous variables – How weather affects the stock market

CUAOA: A Novel CUDA-Accelerated Simulation Framework for the Quantum Approximate Optimization Algorithm

A Path Towards Quantum Advantage for theUnit Commitment Problem

Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games

Evaluating Metaheuristic Optimization Methods for Quantum Reinforcement Learning

Finding Arbitrage with different Quantum Algorithms

Optimization of Variational Quantum Circuits for Hybrid Quantum Proximal Policy Optimization Algorithms

Path-Connectedness of the Boundary between Features that Are Labeled Differently by a Single Layer Perceptron

Construction of quantum circuits with restricted gates

Efficient semi-supervised quantum anomaly detection using one-class support vector machines

Using Quantum Machine Learning to Predict Asset Prices in Financial Markets

A Reinforcement Learning Environment for directed Quantum Circuit Synthesis

Quantum-Enhanced Denoising DiffusionModels

Dimensionality Reduction with Autoencodersfor Efficient Classification with VariationalQuantum Circuits

Approximating Quadratic Unconstrained Binary Optimization Problems using Graph Convolutional Neural Networks

Application of graph partitioning algorithms and genetic algorithms to optimize teleportation costs in distributed quantum circuits.

NISQ-ready community detection on weighted graphs using separation-node identification

Influence of Embedding-Methods on the Generalization of Quantum Machine Learning

Quantum-Multi-Agent-Reinforcement-Learning mit Evolutionärer Optimierung (en)

Anomaly Detection using Quantum Circuit Born Machines (en)

Efficient Quantum Circuit Architecture for Coined Quantum Walks on many Bipartite Graphs (en)

Analyzing Reinforcement Learning strategies from a parameterized quantum walker (en)

QUBO-Generation for (MAX-)3SAT via generative AI-Methods

Space-Efficient Quantum Optimization for the Traveling Salesman Problem via Binary Encoding of Feasible Solutions

Minimizing Teleportation and Enhancing Fidelity in Distributed Quantum Computing using a Multi-Objective Evolutionary Algorithm

Leveraging Preconditioning to Speed Up Quantum Simulation-Based Optimization

Warm Starting Variational Quantum Algorithms for Parameterized Combinatorial Optimization

Circuit Partitioning and Genetic Optimization for Efficient Qubit Distribution in Distributed Quantum Computing

Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits

State Preparation on Quantum HardwareUsing an Island Genetic Algorithm

Explainable Time Series Forecasting using exogenous variables – How weather affects the stock market

CUAOA: A Novel CUDA-Accelerated Simulation Framework for the Quantum Approximate Optimization Algorithm

A Path Towards Quantum Advantage for theUnit Commitment Problem

Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games

Evaluating Metaheuristic Optimization Methods for Quantum Reinforcement Learning

Finding Arbitrage with different Quantum Algorithms

Optimization of Variational Quantum Circuits for Hybrid Quantum Proximal Policy Optimization Algorithms

Construction of quantum circuits with restricted gates

Efficient semi-supervised quantum anomaly detection using one-class support vector machines

Application of graph partitioning algorithms and genetic algorithms to optimize teleportation costs in distributed quantum circuits.

NISQ-ready community detection on weighted graphs using separation-node identification

Efficient Quantum Circuit Architecture for Coined Quantum Walks on many Bipartite Graphs (en)

Emerging Cooperation through Quantum Entanglement in Multi-Agent Systems

Distributed Quantum Machine Learning -Training and Evaluating a Machine Learning Model on a Distributed Quantum Computing Simulator

QUBO-Generation for (MAX-)3SAT via generative AI-Methods

Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits

Reinforcement learning-supported state preparation using parameterized quantum gates

Comparison of different hybrid quantum machine learning approaches for image classification on quantum computers

Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization

The Trainability of Quantum FederatedLearning

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits

Quantum Reinforcement Learning via Parameterized Quantum Walks

Optimization of Variational Quantum Circuits for Hybrid Quantum Proximal Policy Optimization Algorithms

Path-Connectedness of the Boundary between Features that Are Labeled Differently by a Single Layer Perceptron

Using Quantum Machine Learning to Predict Asset Prices in Financial Markets

Quantum-Enhanced Denoising DiffusionModels

Dimensionality Reduction with Autoencodersfor Efficient Classification with VariationalQuantum Circuits

Approximating Quadratic Unconstrained Binary Optimization Problems using Graph Convolutional Neural Networks

Influence of Embedding-Methods on the Generalization of Quantum Machine Learning

Quantum-Multi-Agent-Reinforcement-Learning mit Evolutionärer Optimierung (en)

Anomaly Detection using Quantum Circuit Born Machines (en)

Analyzing Reinforcement Learning strategies from a parameterized quantum walker (en)

CUAOA: A Novel CUDA-Accelerated Simulation Framework for the Quantum Approximate Optimization Algorithm

A Reinforcement Learning Environment for directed Quantum Circuit Synthesis

Anomaly Detection using Quantum Circuit Born Machines (en)

Efficient Quantum Circuit Architecture for Coined Quantum Walks on many Bipartite Graphs (en)

QAR-Lab – Quantum Applications and Research Laboratory
Ludwig-Maximilians-Universität München
Oettingenstraße 67
80538 Munich
Phone: +49 89 2180-9153
E-mail: qar-lab@mobile.ifi.lmu.de

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