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Praktikumsunterlagen (Slides, Übungen, etc.) für das Praktikum "Big Data and Machine Learning"
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Fraunhofer IAO QC / SEQUOIA EndtoEnd / Truck Fleet Route Planning in Supply Chain Management
Apache License 2.0Solving truck routing problem using Annealing on Dwave Advantage systems
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Finds smallest distance between any point in R3 to a point on a specific curve
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Fraunhofer IAO QC / SEQUOIA EndtoEnd / Solving LamA Problem via MILP Model
Apache License 2.0In this demonstration, we present a Quantum Alternating Algorithm designed to address Mixed Integer Linear Problems (MILP). The algorithm's efficacy is showcased through the resolution of an energy use case, employing CPU and GPU quantum simulators, as well as the IBM Quantum System at Ehningen.
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Fraunhofer IAO QC / SEQUOIA EndtoEnd / Sensitivity Analysis for Network Failure
Apache License 2.0Here we perform a hybrid, Grover based optimization to find the single network parameter change that leads to the largest reduction of the critical failure probability in a cascading network
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Fraunhofer IAO QC / SEQUOIA EndtoEnd / Scenariobased Route Planning to Safeguard Automotive Driving Functions
Apache License 2.0A demonstrator for the Sequoia EndtoEnd project which shows how scenariobased route planning to safeguard automotive driving functions can be implemented to run on a quantum computer
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Fraunhofer IAO QC / SEQUOIA EndtoEnd / Quantumbased Computational Fluid Dynamics with Quantum Circuit Learning
Apache License 2.0A powerful example of variational quantum algorithms is the socalled quantum circuit learning algorithm (QCL), which approximates functions and can solve nonlinear differential equations by using the parameter shift rule. This demonstrator aims to explain the basics of QCL and uses examples to show how different functions can be approximated and differential equations can be solved.
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Fraunhofer IAO QC / SEQUOIA EndtoEnd / QC Network Resilience Analysis
Apache License 2.0The demonstrator shows the time evolution of a small network with failures.
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A web page for publishing processspecific hydrogen potentials, using GitLab pages.
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Fraunhofer IAO QC / SEQUOIA EndtoEnd / PDEs Solutions with Quantum Convolutional Neural Networks
Apache License 2.0In this demostrator, we illustrate not only the general procedure of building a QNN via quantum circuit, but also showcase using QNN to predict 2D solution of Poisson equation. To accelerate the convergence, the physics informed NN is introduced. We also show the convergence comprison between QNN and PIQNN.
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