❇️ Add SMPP Solver and further Improvements for the Precompilation an

.pre-commit-config.yaml

@@ -92,3 +92,4 @@ repos:
           - python_tsp
           - networkx
           - mqt.ddsim
+          - pytest

README.md

@@ -29,9 +29,9 @@ In the current implementation, two case studies are conducted:
 1. A SAT Problem: Constraint Satisfaction Problem
 2. A Graph-based Optimization Problem: Travelling Salesman Problem
 
-# A SAT Problem: Constraint Satisfaction Problem
+## A SAT Problem: Constraint Satisfaction Problem
 
-This exemplary implementation can be found in the [CSP_example.ipynb](src/mqt/problemsolver/csp_example.ipynb) Jupyter notebook.
+This exemplary implementation can be found in the [CSP_example.ipynb](notebooks/csp_example.ipynb) Jupyter notebook.
 Here, the solution to a Kakuro riddle with a 2x2 grid can be solved for arbitrary sums `s0` to `s3`:
 
 <p align="center">
@@ -40,9 +40,9 @@ Here, the solution to a Kakuro riddle with a 2x2 grid can be solved for arbitrar
 
 MQT ProblemSolver will return valid values to `a`, `b`, `c`, and `d` if a solution exists.
 
-# A Graph-based Optimization Problem: Travelling Salesman Problem
+## A Graph-based Optimization Problem: Travelling Salesman Problem
 
-This exemplary implementation can be found in the [TSP_example.ipynb](src/mqt/problemsolver/tsp_example.ipynb) Jupyter notebook.
+This exemplary implementation can be found in the [TSP_example.ipynb](notebooks/tsp_example.ipynb) Jupyter notebook.
 Here, the solution to a Travelling Salesman Problem with 4 cities can be solved for arbitrary distances `dist_1_2` to `dist_3_4`between the cities.
 
 <p align="center">
@@ -51,6 +51,33 @@ Here, the solution to a Travelling Salesman Problem with 4 cities can be solved
 
 MQT ProblemSolver will return the shortest path visiting all cities as a list.
 
+## Satellite Mission Planning Problem
+
+Additional to the two case studies, we provide a more complex example for the satellite mission planning problem.
+The goal is to maximize the accumulated values of all images taken by the satellite while it is often not possible
+to take all images since the satellite must rotate and adjust its optics.
+
+In the following example, there are five to-be-captured locations which their assigned value.
+
+<p align="center">
+<img src="img/satellite_mission_planning_problem.png" height=200px>
+</p>
+
+# Pre-Compilation
+
+Every quantum computing application must be encoded into a quantum circuit and then compiled for a specific device.
+This lengthy compilation process is a key bottleneck and intensifies for recurring problems---each of which requires
+a new compilation run thus far.
+
+Pre-compilation is a promising approach to overcome this bottleneck.
+Beginning with a problem class and suitable quantum algorithm, a **predictive encoding** scheme is applied to encode a
+representative problem instance into a general-purpose quantum circuit for that problem class.
+Once the real problem instance is known, the previously constructed circuit only needs to be
+**adjusted**—with (nearly) no compilation necessary.
+
+Following this approach, we provide a pre-compilation module that can be used to precompile QAOA circuits
+for the MaxCut problem.
+
 # Usage
 
 MQT ProblemSolver is available via [PyPI](https://pypi.org/project/mqt.problemsolver/):
@@ -66,12 +93,18 @@ MQT ProblemSolver is available via [PyPI](https://pypi.org/project/mqt.problemso
 ├── src
 │ └── mqt
 │     └── problemsolver
-│        └── csp.py
+│     │  └── csp.py
+│     │  └── tsp.py
+│     └── satelitesolver
+│     │  └── ...
+│     └── precompilation
+│        └── ...
+└── notebooks
+│     └── problemsolver
 │        └── csp_example.ipynb
-│        └── tsp.py
 │        └── tsp_example.ipynb
-└── tests
-    └── ...
+│        └── satellitesolver
+│           └── satellitesolver_example.ipynb
 ```
 
 # References