Decohering tensor network quantum machine learning models
Tensor network quantum machine learning (QML) models are promising applications on
near-term quantum hardware. While decoherence of qubits is expected to decrease the …
near-term quantum hardware. While decoherence of qubits is expected to decrease the …
Tensor Network Estimation of Distribution Algorithms
J Gardiner, J Lopez-Piqueres - arxiv preprint arxiv:2412.19780, 2024 - arxiv.org
Tensor networks are a tool first employed in the context of many-body quantum physics that
now have a wide range of uses across the computational sciences, from numerical methods …
now have a wide range of uses across the computational sciences, from numerical methods …
Patch-based medical image segmentation using matrix product state tensor networks
Tensor networks are efficient factorisations of high-dimensional tensors into a network of
lower-order tensors. They have been most commonly used to model entanglement in …
lower-order tensors. They have been most commonly used to model entanglement in …
Qubit Control and Applications to Quantum Computation and Open Quantum Systems
Z Yang - 2024 - search.proquest.com
Quantum computing has the potential to solve problems that are intractable for classical
computers. In practice, physical qubits are coupled to their environments and are open …
computers. In practice, physical qubits are coupled to their environments and are open …
[PDF][PDF] EUROPEAN AND BARRIER OPTIONS UNDER STOCHASTIC VOLATILITY MODELS
F BANGERTER - repository.tudelft.nl
This thesis presents a comprehensive exploration of the rough Heston model as a means to
enhance financial derivative pricing and calibration in the context of the complex behavior of …
enhance financial derivative pricing and calibration in the context of the complex behavior of …