Add installation and usage instructions

README.md

@@ -1,2 +1,54 @@
+
 # inspire-experiments
-Code and notebooks for running experiments on [QuTech Quantum Inspire](https://www.quantum-inspire.com/) 
+
+#### Code and notebooks for running, saving and analyzing experiments on [QuTech Quantum Inspire](https://www.quantum-inspire.com/).
+
+This package is mostly focused on using the [Starmon-5](https://www.quantum-inspire.com/backends/starmon-5/) hardware backend, 
+but is easily extendable to using other backends of _Quantum Inspire_.
+
+Here we use [_Qiskit_](https://github.com/Qiskit/qiskit) to generate the experiment circuits and send jobs just for convenience,
+but it is also possible to use _Quantum Inspire's_ native cQUASM language as shown in [this example](https://www.quantum-inspire.com/kbase/using-api-and-sdk/). 
+
+## Installation
+
+To use this package, just clone it locally to a directory of your choice
+```commandline
+❯ git clone git@github.com:codecrap/inspire-experiments.git
+```
+
+and install it as local package in your python environment (`-e` for editable)
+```commandline
+❯ python -m  pip install -e ./inspire-experiments
+```
+
+## Usage
+
+Once you have stored your API token locally (check the [Knowledge Base](https://www.quantum-inspire.com/kbase/using-api-and-sdk/) for how to do that)
+you can login using 
+```python
+from inspire_experiments import *
+api, backend = inspire_login()
+```
+and check for the backend's status using
+```python
+print(get_starmon_status())
+```
+
+After building the circuit you wish to run and sending the job using 
+```python
+job = qiskit.execute(circuit, shots=2**14, optimization_level=0, backend=backend)
+```
+Note that passing `optimization_level=0` is needed to disable the optimization part of the Qiskit circuit transpiler and interpret your circuit literally _as is_
+(except for the mapping to hardware topology, check the [Qiskit pass manager](https://github.com/Qiskit/qiskit-terra/blob/main/qiskit/transpiler/preset_passmanagers/level0.py) for details).
+
+
+You can save the results (by default saves as CSV and JSON files under `./data/`) with
+```python
+ExperimentData.save_job_result(job, exp_name="my_experiment_V1", header=get_file_header(circuit))
+```
+By passing the output of `get_file_header()` as the optional `header` argument,
+each file will contain a text representation of the circuit that was used in the job.
+This is useful to help remember which data belongs to which experiment.
+
+
+Check the `notebooks` directory for more detailed usage examples.