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Solving truck routing problem using Annealing on Dwave Advantage systems

Sensor Software Setup Solution for Microcontrollers by Eric Kondratenko

Heat pump control testing framework

Viewpoint Optimization Benchmark

Here 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

In 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.

Demonstrator for Zero-Noise Extrapolation (ZNE) and Inverted-Circuit ZNE

We propose a quantum version of the deterministic annealing algorithm to verify the input-output relations of a neural network. We apply the algorithm to traffic sign recognition, an important task for self-driving vehicles.

The demonstration illustrates the generation of a quantum feature map for a simple regression problem. Reinforcement learning techniques are used, visualizing the decision process of the AI agent through a simple visualization of the quantum circuit creation. We show how to load, train and test the model. The results show a feature map design tailored to the problem.

A demonstrator for the Sequoia End-to-End project which shows how scenario-based route planning to safeguard automotive driving functions can be implemented to run on a quantum computer

The demonstrator shows the time evolution of a small network with failures.

End-to-end demonstrator for quantum optimization of charging schedules.

This notebook illustrates using the Quantum Approximate Optimization Algorithm (QAOA) to find optimized configurations for feature models with attributed costs on a quantum computer.