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of 11 073
pro vyhledávání: '"A. Mutschler"'
Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garner
Externí odkaz:
http://arxiv.org/abs/2410.21117
Autor:
Gaikwad, Nishant S., Heublein, Lucas, Raichur, Nisha L., Feigl, Tobias, Mutschler, Christopher, Ott, Felix
Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server and shares only the model updates (i.e., gradient weights) during the a
Externí odkaz:
http://arxiv.org/abs/2410.15681
Artificial Intelligence (AI)-based radio fingerprinting (FP) outperforms classic localization methods in propagation environments with strong multipath effects. However, the model and data orchestration of FP are time-consuming and costly, as it requ
Externí odkaz:
http://arxiv.org/abs/2410.00617
Autor:
Heublein, Lucas, Feigl, Tobias, Nowak, Thorsten, Rügamer, Alexander, Mutschler, Christopher, Ott, Felix
Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract
Externí odkaz:
http://arxiv.org/abs/2409.15114
Autor:
Richter, Marvin, Dubey, Abhishek Y., Plinge, Axel, Mutschler, Christopher, Scherer, Daniel D., Hartmann, Michael J.
Despite advances in the development of quantum computers, the practical application of quantum algorithms remains outside the current range of so-called noisy intermediate-scale quantum devices. Now and beyond, quantum circuit compilation (QCC) is a
Externí odkaz:
http://arxiv.org/abs/2409.05849
This work-in-progress paper presents results on the feasibility of single-shot object detection on microcontrollers using YOLO. Single-shot object detectors like YOLO are widely used, however due to their complexity mainly on larger GPU-based platfor
Externí odkaz:
http://arxiv.org/abs/2408.15865
On-device training of DNNs allows models to adapt and fine-tune to newly collected data or changing domains while deployed on microcontroller units (MCUs). However, DNN training is a resource-intensive task, making the implementation and execution of
Externí odkaz:
http://arxiv.org/abs/2407.10734
Autor:
Raichur, Nisha L., Heublein, Lucas, Feigl, Tobias, Rügamer, Alexander, Mutschler, Christopher, Ott, Felix
The primary objective of methods in continual learning is to learn tasks in a sequential manner over time from a stream of data, while mitigating the detrimental phenomenon of catastrophic forgetting. In this paper, we focus on learning an optimal re
Externí odkaz:
http://arxiv.org/abs/2405.11067
Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule
Autor:
Periyasamy, Maniraman, Plinge, Axel, Mutschler, Christopher, Scherer, Daniel D., Mauerer, Wolfgang
The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both classical and quantum components, are well-suited for noisy intermedia
Externí odkaz:
http://arxiv.org/abs/2404.15751
Autor:
Rietsch, Sebastian, Dubey, Abhishek Y., Ufrecht, Christian, Periyasamy, Maniraman, Plinge, Axel, Mutschler, Christopher, Scherer, Daniel D.
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 given unitary while minimizing circuit depth, total gate count, a spec
Externí odkaz:
http://arxiv.org/abs/2404.14865