💡 added mqt logo and adjusted all img urls to be absolute and not rel… (

#146)

* 💡 added mqt logo and adjusted all img urls to be absolute and not relative to work on pypi

* 💡 adjusted text accordingly to new 0.2.0 version

README.md

@@ -5,14 +5,21 @@
 [![codecov](https://img.shields.io/codecov/c/github/cda-tum/MQTBench?style=flat-square&logo=codecov)](https://codecov.io/gh/cda-tum/MQTBench)
 [![Server Deployment](https://github.com/cda-tum/MQTBench/actions/workflows/server_deploy.yml/badge.svg)](https://github.com/cda-tum/MQTBench/actions/workflows/server_deploy.yml)
 
+<p align="center">
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/mqt_light.png" width="60%">
+  <img src="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/mqt_dark.png" width="60%">
+</picture>
+</p>
+
 # MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing
 
 MQT Bench is a quantum circuit benchmark suite with cross-level support, i.e., providing the same benchmark algorithms for different abstraction levels throughout the quantum computing
 software stack.
 
 MQT Bench is part of the Munich Quantum Toolkit (MQT) developed by the [Chair for Design Automation](https://www.cda.cit.tum.de/) at the [Technical University of Munich](https://www.tum.de/) and is hosted at [https://www.cda.cit.tum.de/mqtbench/](https://www.cda.cit.tum.de/mqtbench/).
 
-[<img src="img/mqtbench.png" align="center" width="500" >](https://www.cda.cit.tum.de/mqtbench)
+[<img src="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/mqtbench.png" align="center" width="500" >](https://www.cda.cit.tum.de/mqtbench)
 
 This documentation explains how to use MQT Bench to create and filter benchmarks.
 
@@ -30,7 +37,7 @@ An example is given in the following:
 
 1. Algorithmic Level
 
-<img src="img/level_1.png"  align="center" width="250">
+<img src="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/level_1.png"  align="center" width="250">
 
 Variational Quantum Algorithms (VQAs) are an emerging class of quantum algorithms with a wide range of
 applications. A respective circuit is shown above, it represents an example of an ansatz function
@@ -39,7 +46,7 @@ level, the circuit is parameterized by the angles θ<sub>i</sub> of the six sing
 
 2. Target-independent Level
 
-<img src="img/level_2.png"  align="center" width="250">
+<img src="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/level_2.png"  align="center" width="250">
 
 VQAs are hybrid quantum-classical algorithms, where the parameters of the quantum ansatz are
 iteratively updated by a classical optimizer analogous to conventional gradient-based optimization.
@@ -49,7 +56,7 @@ shown above.
 
 3. Target-dependent Native Gates Level
 
-<img src="img/level_3.png"  align="center" width="250"/>
+<img src="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/level_3.png"  align="center" width="250"/>
 
 Different quantum computer realizations support
 different native gate-sets. In our example, we consider the
@@ -59,8 +66,8 @@ they are substituted by a sequence of X and Rz gates (denoted as • with a phas
 
 4. Target-dependent Mapped Level
 
-<img src="img/level_4.png"  align="center" width="300"/>
-<img src="img/arch.png"  align="right" width="100"/>
+<img src="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/level_4.png"  align="center" width="300"/>
+<img src="https://raw.githubusercontent.com/cda-tum/mqtbench/main/img/arch.png"  align="right" width="100"/>
 
 The architecture of the IBMQ Manila device is shown
 above on the right and it defines between which qubits a two-qubit operation may be performed.
@@ -189,10 +196,10 @@ MQT Bench is available via [PyPI](https://pypi.org/project/mqt.bench/)
 (venv) $ pip install mqt.bench
 ```
 
-To generate a benchmark circuit on the algorithmic level, please use the `get_one_benchmark` method:
+To generate a benchmark circuit on the algorithmic level, please use the `get_benchmark` method:
 
 ```python3
-def get_one_benchmark(
+def get_benchmark(
     benchmark_name: str,
     level: Union[str, int],
     circuit_size: int = None,
@@ -270,9 +277,9 @@ Hereby, the mappings between shortened `benchmark_name` and actual benchmarks ar
 For example, in order to obtain the _5_-qubit Deutsch-Josza benchmark on algorithm level, use the following:
 
 ```python
-from mqt.bench import get_one_benchmark
+from mqt.bench import get_benchmark
 
-qc = get_one_benchmark("dj", "alg", 5)
+qc = get_benchmark("dj", "alg", 5)
 ```
 
 ### Locally hosting the MQT Bench Viewer