QAIO 2025 Abstracts

Area 1 - QAIO
Full Papers Short Papers

Area 1 - QAIO

Full Papers
Paper Nr: 10
Title:

Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
Authors:

Gerhard Stenzel, Sebastian Zielinski, Michael Kölle, Philipp Altmann, Jonas Nüßlein and Thomas Gabor
Abstract: To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format. Qandle is an open-source project hosted on GitHub and PyPI.
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Paper Nr: 11
Title:

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs
Authors:

Tobias Rohe, Florian Burger, Michael Kölle, Sebastian Wölckert, Maximilian Zorn and Claudia Linnhoff-Popien
Abstract: The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent probabilistic functionality can be utilised in this field of generative artificial intelligence. In this study, we use quantum-classical hybrid generative adversarial networks (QuGANs) to artificially generate graphs of shipping routes. We create a training dataset based on real shipping data and investigate to what extent QuGANs are able to learn and reproduce inherent distributions and geometric features of this data. We compare hybrid QuGANs with classical Generative Adversarial Networks (GANs), with a special focus on their parameter efficiency. Our results indicate that QuGANs are indeed able to quickly learn and represent underlying geometric properties and distributions, although they seem to have difficulties in introducing variance into the sampled data. Compared to classical GANs of greater size, measured in the number of parameters used, some QuGANs show similar result quality. Our reference to concrete use cases, such as the generation of shipping data, provides an illustrative example and demonstrate the potential and diversity in which QC can be used.
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Paper Nr: 13
Title:

Quantum Multi-Agent Reinforcement Learning for Aerial Ad-Hoc Networks
Authors:

Theodora-Augustina Drăgan, Akshat Tandon, Tom Haider, Carsten Strobel, Jasper Simon Krauser and Jeanette Miriam Lorenz
Abstract: Quantum machine learning (QML) as combination of quantum computing with machine learning (ML) is a promising direction to explore, in particular due to the advances in realizing quantum computers and the hoped-for quantum advantage. A field within QML that is only little approached is quantum multi-agent reinforcement learning (QMARL), despite having shown to be potentially attractive for addressing industrial applications such as factory management, cellular access and mobility cooperation. This paper presents an aerial communication use case and introduces a hybrid quantum-classical (HQC) ML algorithm to solve it. This use case intends to increase the connectivity of flying ad-hoc networks and is solved by an HQC multi-agent proximal policy optimization algorithm in which the core of the centralized critic is replaced with a data reuploading variational quantum circuit. Results show a slight increase in performance for the quantum-enhanced solution with respect to a comparable classical algorithm, earlier reaching convergence, as well as the scalability of such a solution: an increase in the size of the ansatz, and thus also in the number of trainable parameters, leading to better outcomes. These promising results show the potential of QMARL to industrially-relevant complex use cases.
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Paper Nr: 14
Title:

QMamba: Quantum Selective State Space Models for Text Generation
Authors:

Gerhard Stenzel, Michael Kölle, Tobias Rohe, Maximilian Balthasar Mansky, Jonas Nüßlein and Thomas Gabor
Abstract: Quantum machine learning offers novel paradigms to address limitations in traditional natural language processing models, such as fixed context lengths and computational inefficiencies. In this work, we propose QMamba, the first quantum adaptation of the Mamba architecture, integrating selective state space models with quantum computation for efficient and scalable text generation. QMamba leverages quantum principles like superposition and entanglement to enable unbounded context sizes and reduced computational complexity. Our contributions include the development of a quantum generative model optimized for hardware constraints, advancements in encoding, embedding, and measurement techniques, and the demonstration of its performance on pattern reproduction and context-challenging tasks like ”Needle in a Haystack.” Experimental results confirm QMamba’s potential to maintain high efficiency and performance across varying sequence lengths, laying the groundwork for future explorations in quantum-enhanced natural language processing.
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Paper Nr: 17
Title:

Grid Cost Allocation in Peer-to-Peer Electricity Markets: Benchmarking Classical and Quantum Optimization Approaches
Authors:

David Bucher, Daniel Porawski, Benedikt Wimmer, Jonas Nüßlein, Corey O’Meara, Giorgio Cortiana and Claudia Linnhoff-Popien
Abstract: This paper presents a novel optimization approach for allocating grid operation costs in Peer-to-Peer (P2P) electricity markets using Quantum Computing (QC). We develop a Quadratic Unconstrained Binary Optimization (QUBO) model that matches logical power flows between producer-consumer pairs with the physical power flow to distribute grid usage costs fairly. The model is evaluated on IEEE test cases with up to 57 nodes, comparing Quantum Annealing (QA), hybrid quantum-classical algorithms, and classical optimization approaches. Our results show that while the model effectively allocates grid operation costs, QA performs poorly in comparison despite extensive hyperparameter optimization. The classical branch-and-cut method outperforms all solvers, including classical heuristics, and shows the most advantageous scaling behavior. The findings may suggest that binary least-squares optimization problems may not be suitable candidates for near-term quantum utility.
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Paper Nr: 18
Title:

Quantum-Efficient Kernel Target Alignment
Authors:

Rodrigo Coelho, Georg Kruse and Andreas Rosskopf
Abstract: In recent years, quantum computers have emerged as promising candidates for implementing kernels. Quantum Embedding Kernels embed data points into quantum states and calculate their inner product in a high-dimensional Hilbert Space by computing the overlap between the resulting quantum states. Variational Quantum Circuits (VQCs) are typically used for this end, with Kernel Target Alignment (KTA) as cost function. The optimized kernels can then be deployed in Support Vector Machines (SVMs) for classification tasks. However, both classical and quantum SVMs scale poorly with increasing dataset sizes. This issue is exacerbated in quantum kernel methods, as each inner product requires a quantum circuit execution. In this paper, we investigate KTA-trained quantum embedding kernels and employ a low-rank matrix approximation, the Nyström method, to reduce the quantum circuit executions needed to construct the Kernel Matrix. We empirically evaluate the performance of our approach across various datasets, focusing on the accuracy of the resulting SVM and the reduction in quantum circuit executions. Additionally, we examine and compare the robustness of our model under different noise types, particularly coherent and depolarizing noise.
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Paper Nr: 20
Title:

Benchmarking Quantum Reinforcement Learning
Authors:

Georg Kruse, Rodrigo Coelho, Andreas Rosskopf, Robert Wille and Jeanette-Miriam Lorenz
Abstract: Quantum Reinforcement Learning (QRL) has emerged as a promising research field, leveraging the principles of quantum mechanics to enhance the performance of reinforcement learning (RL) algorithms. However, despite its growing interest, QRL still faces significant challenges. It is still uncertain if QRL can show any advantage over classical RL beyond artificial problem formulations. Additionally, it is not yet clear which streams of QRL research show the greatest potential. The lack of a unified benchmark and the need to evaluate the reliance on quantum principles of QRL approaches are pressing questions. This work aims to address these challenges by providing a comprehensive comparison of three major QRL classes: Parameterized Quantum Circuit based QRL (PQC-QRL) (with one policy gradient (QPG) and one Q-Learning (QDQN) algorithm), Free Energy based QRL (FE-QRL), and Amplitude Amplification based QRL (AA-QRL). We introduce a set of metrics to evaluate the QRL algorithms on the widely applicable benchmark of gridworld games. Our results provide a detailed analysis of the strengths and weaknesses of the QRL classes, shedding light on the role of quantum principles in QRL and paving the way for future research in this field.
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Paper Nr: 23
Title:

Reducing QUBO Density by Factoring out Semi-Symmetries
Authors:

Jonas Nüßlein, Leo Sünkel, Jonas Stein, Tobias Rohe, Daniëlle Schuman, Sebastian Feld, Corey O’Meara, Giorgio Cortiana and Claudia Linnhoff-Popien
Abstract: Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing are prominent approaches for solving combinatorial optimization problems, such as those formulated as Quadratic Unconstrained Binary Optimization (QUBO). These algorithms aim to minimize the objective function $x^T Q x$, where $Q$ is a QUBO matrix. However, the number of two-qubit CNOT gates in QAOA circuits and the complexity of problem embeddings in Quantum Annealing scale linearly with the number of non-zero couplings in $Q$, contributing to significant computational and error-related challenges. To address this, we introduce the concept of \textit{semi-symmetries} in QUBO matrices and propose an algorithm for identifying and factoring these symmetries into ancilla qubits. \textit{Semi-symmetries} frequently arise in optimization problems such as \textit{Maximum Clique}, \textit{Hamilton Cycles}, \textit{Graph Coloring}, and \textit{Graph Isomorphism}. We theoretically demonstrate that the modified QUBO matrix $Q_{\text{mod}}$ retains the same energy spectrum as the original $Q$. Experimental evaluations on the aforementioned problems show that our algorithm reduces the number of couplings and QAOA circuit depth by up to $45\%$. For Quantum Annealing, these reductions also lead to sparser problem embeddings, shorter qubit chains and better performance. This work highlights the utility of exploiting QUBO matrix structure to optimize quantum algorithms, advancing their scalability and practical applicability to real-world combinatorial problems.
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Paper Nr: 31
Title:

Embedding of Tree Tensor Networks into Shallow Quantum Circuits
Authors:

Shota Sugawara, Kazuki Inomata, Tsuyoshi Okubo and Synge Todo
Abstract: Variational Quantum Algorithms (VQAs) are being highlighted as key quantum algorithms for demonstrating quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) devices, which are limited to executing shallow quantum circuits because of noise. However, the barren plateau problem, where the gradient of the loss function becomes exponentially small with system size, hinders this goal. Recent studies suggest that embedding tensor networks into quantum circuits and initializing the parameters can avoid the barren plateau. Yet, embedding tensor networks into quantum circuits is generally difficult, and methods have been limited to the simplest structure, Matrix Product States (MPSs). This study proposes a method to embed Tree Tensor Networks (TTNs), characterized by their hierarchical structure, into shallow quantum circuits. TTNs are suitable for representing two-dimensional systems and systems with long-range correlations, which MPSs are inadequate for representing. Our numerical results show that embedding TTNs provides better initial quantum circuits than MPS. Additionally, our method has a practical computational complexity, making it applicable to a wide range of TTNs. This study is expected to extend the application of VQAs to two-dimensional systems and those with long-range correlations, which have been challenging to utilize.
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Short Papers
Paper Nr: 12
Title:

Continuous Quantum Reinforcement Learning for Robot Navigation
Authors:

Theodora-Augustina Drăgan, Alexander Künzner, Robert Wille and Jeanette Miriam Lorenz
Abstract: One of the multiple facets of quantum reinforcement learning (QRL) is enhancing reinforcement learning (RL) algorithms with quantum submodules, namely with variational quantum circuits (VQC) as function approx-imators. QRL solutions are empirically proven to require fewer training iterations or adjustable parameters than their classical counterparts, but are usually restricted to applications that have a discrete action space and thus limited industrial relevance. We propose a hybrid quantum-classical (HQC) deep deterministic policy gradient (DDPG) approach for a robot to navigate through a maze using continuous states, continuous actions and using local observations from the robot’s LiDAR sensors. We show that this HQC method can lead to models of comparable test results to the neural network (NN)-based DDPG algorithm, that need around 200 times fewer weights. We also study the scalability of our solution with respect to the number of VQC layers and qubits, and find that in general results improve as the layer and qubit counts increase. The best rewards among all similarly sized HQC and classical DDPG methods correspond to a VQC of 8 qubits and 5 layers with no other NN. This work is another step towards continuous QRL, where literature has been sparse.
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Paper Nr: 16
Title:

Quantum Approaches to the 0/1 Multi-Knapsack Problem: QUBO Formulation, Penalty Parameter Characterization and Analysis
Authors:

Evren Guney, Joachim Ehrenthal and Thomas Hanne
Abstract: The 0/1 Multi-Knapsack Problem (MKP) is a combinatorial optimization problem with applications in logistics, finance, and resource management. Advances in quantum computing have enabled the exploration of problems like the 0/1 MKP through Quadratic Unconstrained Binary Optimization (QUBO) formulations. This work develops QUBO formulations for the 0/1 MKP, with a focus on optimizing penalty parameters for encoding constraints. Using simulation experiments across quantum platforms, we evaluate the feasibility of solving small-scale instances of the 0/1 MKP. The results provide insights into the challenges and opportunities associated with applying quantum optimization methods for constrained resource allocation problems.
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Paper Nr: 22
Title:

QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
Authors:

Hari Hara Suthan Chittoor, Paul Robert Griffin, Ariel Neufeld, Jayne Thompson and Mile Gu
Abstract: Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of domains including weather forecasting, stock market analysis and disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep learning models like transformer models. Despite the complex architecture of transformer based LTSF models ‘Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2023)’ showed that simple linear models can outperform the state-of-the-art transformer based LTSF models. Recently, quantum machine learning (QML) is evolving as a domain to enhance the capabilities of classical machine learning models. In this paper we initiate the application of QML to LTSF problems by proposing QuLTSF, a simple hybrid QML model for multivariate LTSF. Through extensive experiments on a widely used weather dataset we show the advantages of QuLTSF over the state-of-the-art classical linear models, in terms of reduced mean squared error and mean absolute error.
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Paper Nr: 24
Title:

Intrusion Detection System Based on Quantum Generative Adversarial Network
Authors:

Franco Cirillo and Christian Esposito
Abstract: Intrusion Detection Systems (IDS) are crucial for ensuring network security in increasingly complex digital environments. Among IDS techniques, anomaly detection is effective in identifying unknown threats. However, classical machine learning methods face significant limitations, such as struggles with high-dimensional data and performance constraints in handling imbalanced datasets. Generative Adversarial Networks (GANs) offer a promising alternative by enhancing data generation and feature extraction, but their classical implementations are computationally intensive and limited in exploring complex data distributions. Quantum GANs (QGANs) overcome these challenges by leveraging quantum computing’s advantages. By utilizing a hybrid QGAN architecture with a quantum generator and a classical discriminator, the model effectively learns the distribution of real data, enabling it to generate samples that closely resemble genuine data patterns. This capability enhances its performance in anomaly detection. The proposed QGAN use a variational quantum circuit (VQC) for the generator and a neural network for the discriminator. Evaluated on NSL-KDD dataset, the QGAN attains an accuracy of 0.937 and an F1-score of 0.9384, providing a robust, scalable solution for next-generation IDS.
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Paper Nr: 26
Title:

Unified Framework for Implementing Inaccurate Knowledge in Quantum Symbolic Artificial Intelligence Models
Authors:

Eduardo Mosqueira-Rey, Samuel Magaz-Romero and Vicente Moret-Bonillo
Abstract: Symbolic models of Artificial Intelligence are based on defining declarative knowledge that is connected through procedural knowledge forming symbolic graphs through which reasoning flows. Both declarative and procedural knowledge can be inaccurate, which has led to the definition of different models to represent this inaccuracy. Since the functioning of quantum computers is inherently probabilistic, it has been proposed to take advantage of this nature to implement inaccurate knowledge more effectively. In this paper, we present different models for implementing inaccurate knowledge in quantum computers and propose a unified framework to represent and implement the common features of all of them.
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Paper Nr: 28
Title:

Applying Quantum Tensor Networks in Machine Learning: A Systematic Literature Review
Authors:

Erico Souza Teixeira, Yara Rodrigues Inácio and Pamela T. L. Bezerra
Abstract: Integrating quantum computing (QC) into machine learning (ML) holds the promise of revolutionizing computational efficiency and accuracy across diverse applications. Quantum Tensor Networks (QTNs), an advanced framework combining the principles of tensor networks with quantum computation, offer substantial advantages in representing and processing high-dimensional quantum states. This systematic literature review explores the role and impact of QTNs in ML, focusing on their potential to accelerate computations, enhance generalization capabilities, and manage complex datasets. By analyzing 23 studies from 2013 to 2024, we summarize key advancements, challenges, and practical applications of QTNs in quantum machine learning (QML). Results indicate that QTNs can significantly reduce computational resource demands by compressing high-dimensional data, enhance robustness against noise, and optimize quantum circuits, achieving up to a 10-million-fold speedup in specific scenarios. Additionally, QTNs demonstrate strong generalization capabilities, achieving high classification accuracy (up to 0.95) with fewer parameters and training data. These findings position QTNs as a transformative tool in QML, bridging critical limitations in current quantum hardware and enabling real-world applications.
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