5 papers · 1 filter
Loss Behavior in Supervised Learning with Entangled States
Alexander Mandl, Johanna Barzen, Marvin Bechtold +2
Quantum Machine Learning (QML) aims to leverage the principles of quantum mechanics to speed up the process of solving machine learning problems or improve the quality of solutions…
Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects
Marvin Bechtold, Johanna Barzen, Frank Leymann +1
Scaling beyond individual quantum devices via distributed quantum computing relies critically on high-fidelity quantum state transfers between devices, yet the quantum interconnect…
Joint Wire Cutting with Non-Maximally Entangled States
Marvin Bechtold, Johanna Barzen, Frank Leymann +2
Distributed quantum computing leverages the collective power of multiple quantum devices to perform computations exceeding the capabilities of individual quantum devices. A current…
Cutting a Wire with Non-Maximally Entangled States
Marvin Bechtold, Johanna Barzen, Frank Leymann +1
Distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques e…
On Reducing the Amount of Samples Required for Training of QNNs: Constraints on the Linear Structure of the Training Data
Alexander Mandl, Johanna Barzen, Frank Leymann +1
Training classical neural networks generally requires a large number of training samples. Using entangled training samples, Quantum Neural Networks (QNNs) have the potential to sig…