191 citations · 551 across the 134 of their papers we have counts for
26 papers · 1 filter
Permutation-invariant quantum circuits
Maximilian Balthasar Mansky, Santiago Londoño Castillo, Victor Ramos Puigvert +1
The implementation of physical symmetries into problem descriptions allows for the reduction of parameters and computational complexity. We show the integration of the permutation…
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Michael Kölle, Afrae Ahouzi, Pascal Debus +3
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class…
Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
Jonas Stein, Navid Roshani, Maximilian Zorn +3
A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs). Similar to deep learning, vanishing gradients pose immense prob…
Challenges for Reinforcement Learning in Quantum Circuit Design
Philipp Altmann, Jonas Stein, Michael Kölle +5
Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and adv…
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Daniëlle Schuman, Leo Sünkel, Philipp Altmann +4
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction cap…
Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures
Michael Kölle, Jonas Maurer, Philipp Altmann +3
Quantum computing offers the potential for superior computational capabilities, particularly for data-intensive tasks. However, the current state of quantum hardware puts heavy res…
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
Michael Kölle, Felix Topp, Thomy Phan +3
Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new ap…
Incentivising Demand Side Response through Discount Scheduling using Hybrid Quantum Optimization
David Bucher, Jonas Nüßlein, Corey O'Meara +5
Demand Side Response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand a…
Applying QNLP to sentiment analysis in finance
Jonas Stein, Ivo Christ, Nicolas Kraus +3
As an application domain where the slightest qualitative improvements can yield immense value, finance is a promising candidate for early quantum advantage. Focusing on the rapidly…
Improving Primate Sounds Classification using Binary Presorting for Deep Learning
Michael Kölle, Steffen Illium, Maximilian Zorn +3
In the field of wildlife observation and conservation, approaches involving machine learning on audio recordings are becoming increasingly popular. Unfortunately, available dataset…
Weight Re-Mapping for Variational Quantum Algorithms
Michael Kölle, Alessandro Giovagnoli, Jonas Stein +5
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum m…
Approximative lookup-tables and arbitrary function rotations for facilitating NISQ-implementations of the HHL and beyond
Petros Stougiannidis, Jonas Stein, David Bucher +3
Many promising applications of quantum computing with a provable speedup center around the HHL algorithm. Due to restrictions on the hardware and its significant demand on qubits a…
Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
Jonas Stein, Tobias Rohe, Francesco Nappi +5
Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximatio…
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
Jonas Stein, Michael Poppel, Philip Adamczyk +9
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning fr…
Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
Jonas Stein, Daniëlle Schuman, Magdalena Benkard +7
Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. With rapidly growing amounts of data and the omnipresenc…
Decomposition Algorithm of an Arbitrary Pauli Exponential through a Quantum Circuit
Maximilian Balthasar Mansky, Victor Ramos Puigvert, Santiago Londoño Castillo +1
We review the staircase algorithm to decompose the exponential of a generalized Pauli matrix and we propose two alternative recursive methods which offer more efficient quantum cir…
Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
Jonas Stein, Farbod Chamanian, Maximilian Zorn +4
Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimi…
Pattern QUBOs: Algorithmic construction of 3SAT-to-QUBO transformations
Sebastian Zielinski, Jonas Nüßlein, Jonas Stein +3
3SAT instances need to be transformed into instances of Quadratic Unconstrained Binary Optimization (QUBO) to be solved on a quantum annealer. Although it has been shown that the c…
Exponential Quantum Speedup for Simulation-Based Optimization Applications
Jonas Stein, Lukas Müller, Leonhard Hölscher +10
The simulation of many industrially relevant physical processes can be executed up to exponentially faster using quantum algorithms. However, this speedup can only be leveraged if…
Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study
Sebastian Zielinski, Jonas Nüßlein, Jonas Stein +3
To solve 3SAT instances on quantum annealers they need to be transformed to an instance of Quadratic Unconstrained Binary Optimization (QUBO). When there are multiple transformatio…
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Philipp Altmann, Fabian Ritz, Leonard Feuchtinger +3
The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a…
Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization
Jonas Nüßlein, Sebastian Zielinski, Thomas Gabor +2
We introduce a novel approach to translate arbitrary 3-SAT instances to Quadratic Unconstrained Binary Optimization (QUBO) as they are used by quantum annealing (QA) or the quantum…
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
Philipp Altmann, Thomy Phan, Fabian Ritz +2
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms. Building upon the concept of self-imitation learning (SIL), we intr…
Compression of GPS Trajectories using Autoencoders
Michael Kölle, Steffen Illium, Carsten Hahn +3
The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data. Several compression methods have been developed…