9 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…
On the Differential Topology of Expressivity of Parameterized Quantum Circuits
Johanna Barzen, Frank Leymann
Parameterized quantum circuits play a key role in quantum computing. Measuring the suitability of such a circuit for solving a class of problems is needed. One such promising measu…
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…
Harnessing Patterns to Support the Development of Hybrid Quantum Applications
Daniel Vietz, Martin Beisel, Johanna Barzen +3
Quantum computing provides computational advantages in various domains. To benefit from these advantages complex hybrid quantum applications must be built, which comprise both quan…
Qunicorn: A Middleware for the Unified Execution Across Heterogeneous Quantum Cloud Offerings
Benjamin Weder, Johanna Barzen, Martin Beisel +4
Quantum computers are available via a variety of different quantum cloud offerings. These offerings are heterogeneous and differ in features, such as pricing models or types of acc…
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…