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Heat pump control testing framework

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

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

A powerful example of variational quantum algorithms is the so-called quantum circuit learning algorithm (QCL), which approximates functions and can solve non-linear 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.

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

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

Solving truck routing problem using Annealing on Dwave Advantage systems

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.

In 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.

An alternative problem encoding for QAOA is used to accelerate cost function computation

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

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.

Solving a combinatorial optimization problem with a hybrid quantum-classical algorithm.