Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
D Fry, A Deshmukh, SYC Chen, V Rastunkov… - Scientific Reports, 2023 - nature.com
Quantum reservoir computing is strongly emerging for sequential and time series data
prediction in quantum machine learning. We make advancements to the quantum noise …
prediction in quantum machine learning. We make advancements to the quantum noise …
Quantum deep reinforcement learning for robot navigation tasks
H Hohenfeld, D Heimann, F Wiebe… - arXiv preprint arXiv …, 2022 - arxiv.org
We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a
simple, wheeled robot in simulated environments of increasing complexity. For this, we train …
simple, wheeled robot in simulated environments of increasing complexity. For this, we train …
Quantum normalizing flows for anomaly detection
B Rosenhahn, C Hirche - Physical Review A, 2024 - APS
A normalizing flow computes a bijective mapping from an arbitrary distribution to a
predefined (eg, normal) distribution. Such a flow can be used to address different tasks, eg …
predefined (eg, normal) distribution. Such a flow can be used to address different tasks, eg …
[HTML][HTML] AutoQML: Automatic generation and training of robust quantum-inspired classifiers by using evolutionary algorithms on grayscale images
A new hybrid system is proposed for automatically generating and training quantum-inspired
classifiers on grayscale images by using multiobjective genetic algorithms. It is defined a …
classifiers on grayscale images by using multiobjective genetic algorithms. It is defined a …
Optimum-preserving QUBO parameter compression
Quadratic unconstrained binary optimization (QUBO) problems are well-studied, not least
because they can be approached using contemporary quantum annealing or classical …
because they can be approached using contemporary quantum annealing or classical …
Efficient variational synthesis of quantum circuits with coherent multi-start optimization
We consider the problem of the variational quantum circuit synthesis into a gate set
consisting of the CNOT gate and arbitrary single-qubit (1q) gates with the primary target …
consisting of the CNOT gate and arbitrary single-qubit (1q) gates with the primary target …
Real-Part Quantum Support Vector Machines
N Piatkowski, S Mücke - Joint European Conference on Machine Learning …, 2024 - Springer
In recent years, quantum computing has been slowly transitioning from a purely theoretical
branch of computer science to a practical yet highly experimental discipline. Within quantum …
branch of computer science to a practical yet highly experimental discipline. Within quantum …
Simulating the operation of a quantum computer in a dissipative environment
S Zhang, Y Chen, Q Shi - The Journal of Chemical Physics, 2024 - pubs.aip.org
The operations of current quantum computers are still significantly affected by decoherence
caused by interaction with the environment. In this work, we employ the non-perturbative …
caused by interaction with the environment. In this work, we employ the non-perturbative …
Quantum Deep Reinforcement Learning for Robot Navigation Tasks
H Hohenfeld, D Heimann, F Wiebe, F Kirchner - IEEE Access, 2024 - ieeexplore.ieee.org
We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a
simple, wheeled robot in simulated environments of increasing complexity. For this, we train …
simple, wheeled robot in simulated environments of increasing complexity. For this, we train …
Predicting Machining Stability with a Quantum Regression Model
S Mücke, F Finkeldey, N Piatkowski, T Siebrecht… - arXiv preprint arXiv …, 2024 - arxiv.org
In this article, we propose a novel quantum regression model by extending the Real-Part
Quantum SVM. We apply our model to the problem of stability limit prediction in milling …
Quantum SVM. We apply our model to the problem of stability limit prediction in milling …