Release 2.0.5 #129
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Release 2.0.5 #129
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The auto-carrier loading problem is an industry-relevant optimization use case in which the goal is to find an optimal assignment of vehicles on platforms of a truck such that the number of vehicles is maximized while all weight and length constraints and regulations are fulfilled. In this implementation three scenarios have been implemented, ranging from a toy problem with only six cars and few constraints to a full-fetched problem instance close to a real-world example with ten cars and complex parameters and constraints. A more in-depth introduction to the application is provided in [http://dx.doi.org/10.2139/ssrn.4513003](https://dx.doi.org/10.2139/ssrn.4513003). ----- Co-authored-by: Christian Jaeck [[email protected]](mailto:[email protected]) Co-authored-by: Marvin Erdmann [[email protected]](mailto:[email protected]) Co-authored-by: Philipp Ross [[email protected]](mailto:[email protected]) Co-authored-by: Carlos Riofrio [[email protected]](mailto:[email protected])
The auto-carrier loading problem is an industry-relevant optimization use case in which the goal is to find an optimal assignment of vehicles on platforms of a truck such that the number of vehicles is maximized while all weight and length constraints and regulations are fulfilled. In this implementation three scenarios have been implemented, ranging from a toy problem with only six cars and few constraints to a full-fetched problem instance close to a real-world example with ten cars and complex parameters and constraints. A more in-depth introduction to the application is provided in http://dx.doi.org/10.2139/ssrn.4513003. Co-authored-by: Christian Jaeck [email protected] Co-authored-by: Marvin Erdmann [email protected] Co-authored-by: Philipp Ross [email protected] Co-authored-by: Carlos Riofrio [email protected]
Quantum Generative Adversarial Networks (QGANs) generate synthetic data sets by an interplay of a generator and a discriminator. The generator learns to generate data samples that resemble the original data set such that the discriminator cannot tell apart the synthetic and the original data. Now, this method is implemented as a part of the generative modeling module in QUARK. Additional content: * Add LibraryPennylane * Refinement of docstrings and typings in QML modules --------- Co-authored-by: Florian Kiwit <[email protected]> Co-authored-by: Marvin Erdmann <[email protected]> Co-authored-by: Maximilian Wolf <[email protected]>
philross
approved these changes
Jul 17, 2024
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Full changelog: v2.0.4...v2.0.5