32 citations · 209 across the 24 of their papers we have counts for
27 papers
Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers
Jakob Schubert, Maximilian Kasper, Maximilian Linke +5
We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs). In mobile applications where…
Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization
Lucas Heublein, Mark Deutel, Axel Plinge +1
Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw in-phase and quadrature (IQ)…
PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units
Mark Deutel, Simon Geis, Axel Plinge
Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requires DNN architectures to be spe…
Pareto Optimal Benchmarking of AI Models on ARM Cortex Processors for Sustainable Embedded Systems
Pranay Jain, Maximilian Kasper, Göran Köber +3
This work presents a practical benchmarking framework for optimizing artificial intelligence (AI) models on ARM Cortex processors (M0+, M4, M7), focusing on energy efficiency, accu…
Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
Nina Brolich, Simon Geis, Maximilian Kasper +3
Biodiversity loss poses a significant threat to humanity, making wildlife monitoring essential for assessing ecosystem health. Avian species are ideal subjects for this due to thei…
Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
Alexander Mattick, Maniraman Periyasamy, Christian Ufrecht +4
Quantum computing is currently strongly limited by the impact of noise, in particular introduced by the application of two-qubit gates. For this reason, reducing the number of two-…
Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance
Marvin Richter, Abhishek Y. Dubey, Axel Plinge +3
Despite advances in the development of quantum computers, the practical application of quantum algorithms requiring deep circuit depths or high-fidelity transformations remains out…
Improving Quantum and Classical Decomposition Methods for Vehicle Routing
Laura S. Herzog, Friedrich Wagner, Christian Ufrecht +4
Quantum computing is a promising technology to address combinatorial optimization problems, for example via the quantum approximate optimization algorithm (QAOA). Its potential, ho…
SCIM MILQ: An HPC Quantum Scheduler
Philipp Seitz, Manuel Geiger, Christian Ufrecht +4
With the increasing sophistication and capability of quantum hardware, its integration, and employment in high performance computing (HPC) infrastructure becomes relevant. This ope…
Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
Maniraman Periyasamy, Axel Plinge, Christopher Mutschler +2
The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both…
Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
Sebastian Rietsch, Abhishek Y. Dubey, Christian Ufrecht +4
This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a…
Warm-Start Variational Quantum Policy Iteration
Nico Meyer, Jakob Murauer, Alexander Popov +4
Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iterati…
Comprehensive Library of Variational LSE Solvers
Nico Meyer, Martin Röhn, Jakob Murauer +3
Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variat…
Optimal joint cutting of two-qubit rotation gates
Christian Ufrecht, Laura S. Herzog, Daniel D. Scherer +4
Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here,…
C-MCTS: Safe Planning with Monte Carlo Tree Search
Dinesh Parthasarathy, Georgios Kontes, Axel Plinge +1
The Constrained Markov Decision Process (CMDP) formulation allows to solve safety-critical decision making tasks that are subject to constraints. While CMDPs have been extensively…
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading
Maniraman Periyasamy, Marc Hölle, Marco Wiedmann +3
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerba…
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients
Marco Wiedmann, Marc Hölle, Maniraman Periyasamy +5
VQA have attracted a lot of attention from the quantum computing community for the last few years. Their hybrid quantum-classical nature with relatively shallow quantum circuits ma…
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning
Nico Meyer, Daniel D. Scherer, Axel Plinge +2
Reinforcement learning is a growing field in AI with a lot of potential. Intelligent behavior is learned automatically through trial and error in interaction with the environment.…
Cutting multi-control quantum gates with ZX calculus
Christian Ufrecht, Maniraman Periyasamy, Sebastian Rietsch +3
Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-int…
Quantum Policy Gradient Algorithm with Optimized Action Decoding
Nico Meyer, Daniel D. Scherer, Axel Plinge +2
Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on ap…
A Survey on Quantum Reinforcement Learning
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +3
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on…
Efficient Beam Search for Initial Access Using Collaborative Filtering
George Yammine, Georgios Kontes, Norbert Franke +2
Beamforming-capable antenna arrays overcome the high free-space path loss at higher carrier frequencies. However, the beams must be properly aligned to ensure that the highest powe…
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
Sebastian Rietsch, Shih-Yuan Huang, Georgios Kontes +2
Reinforcement learning (RL) has shown to reach super human-level performance across a wide range of tasks. However, unlike supervised machine learning, learning strategies that gen…
Incremental Data-Uploading for Full-Quantum Classification
Maniraman Periyasamy, Nico Meyer, Christian Ufrecht +3
The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy i…