# London Quantum Computing Hackathon, 6 October 2018

* [Meeetup](https://www.meetup.com/London-Quantum-Computing-Meetup/events/254156028/) (**fully booked 5 weeks in advance!**)
* [Slides](https://docs.google.com/presentation/d/13O5hqez88NVyR0Gn9q9pGdVlRtKOtEkwQO5kAUyVOLs/edit?usp=sharing) (check often for updates)

## Getting started

1. Please register at [Quantum Experience](https://quantumexperience.ng.bluemix.net/qx/signup)
    1. Copy your API token from the ["Advanced"](https://quantumexperience.ng.bluemix.net/qx/account/advanced) tab (click on the "Regenerate" button first).
    1. For the day, please also change your "Institution" to "QuantumHackers" in the ["Account"](https://quantumexperience.ng.bluemix.net/qx/account/profile) tab.
1. Follow [CK-QISKit instructions](https://github.com/ctuning/ck-qiskit).

## Run QISKit-VQE once

First, deploy a VQE ansatz and optimizer plugins that should just work:
```
$ ck deploy_optimizer vqe --value=optimizer.cobyla
$ ck deploy_ansatz vqe --value=ansatz.universal4
```

Then, launch VQE with the deployed optimizer and ansatz:
```
$ ck run vqe --device=local_qasm_simulator --repetitions=1
```

## Monitor the convergence process (an ASCII-graphics program run in a separate Terminal window)
```
ck run program:visualize-convergence
```


## Easy VQE exploration via optimizer parameters
```
$ ck run vqe --device=<device> --sample_size=<sample_size> --max_iterations=<max_iterations> --start_param_value=<start_param_value> --repetitions=<repetitions>
```
where:
- `device`: `local_qasm_simulator` (local simulator), `ibmq_qasm_simulator` (remote simulator), `ibmqx4` (remote hardware); by default, QCK will prompt to select one of these target quantum devices (0, 1, 2).
- `sample_size`: the number of times to evaluate the Hamiltonian function on the quantum device ("sampling resolution") per optimizer iteration; by default, 100.
- `max_iterations`: the maximum number of optimizer iterations ("iteration limit"); by default, 80.
- `start_param_value`: the starting value of each optimizer's parameter (can be a float number or the word 'random')
- `repetitions`: the number of times to repeat the experiment with the same parameters; by default, 3.

**NB:** The aim is to minimize the [Time-To-Solution](https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/d9iysrawnprjy2w/ck-quantum-hackathon-20180615.ipynb#Time-to-solution-metric) metric (TTS). As TTS is proportional to `sample_size`, exploring lower values of `sample_size` may be sensible.

At the same time, a low number of `repetitions` may make it hard to demonstrate solution convergence with a high probability. For experiments to be uploaded, we recommend using at least 10 repetitions on the simulators and 3-5 repetitions on the hardware.

## Advanced VQE exploration via plugins

### See which plugins are deployed (both `soft` and `env` entries)
```
$ ck search --tags=deployed
```

### Removing plugins

#### Removing an optimizer plugin
```
$ ck cleanup vqe --type=optimizer
```

#### Removing an ansatz plugin
```
$ ck cleanup vqe --type=ansatz
```

#### Removing both optimizer and ansatz plugins
```
$ ck cleanup vqe
```

### Working with optimizer plugins

#### Select an optimizer plugin to deploy
```
$ ck deploy_optimizer vqe
```

#### Locate and edit the deployed optimizer plugin (use your favourite text editor instead of `vi`)
```
$ ck plugin_path vqe --type=optimizer
$ vi `ck plugin_path vqe --type=optimizer`
```
**NB:** The optimizer plugin is written in Python.
It is expected to contain only one top-level function.
If you need more, please define them within the top-level one.

### Working with ansatz plugins

#### Select an ansatz plugin to deploy
```
$ ck deploy_ansatz vqe
```

#### Visualize the ansatz circuit (use your favourite image viewer instead of `display`)
```
$ ck run program:visualize-ansatz
$ display `ck find program:visualize-ansatz`/ansatz_circuit.png
```
**NB:** If unsure about the image viewer, try `eog` or `eom` on Linux, `open` on macOS.

#### Locate and edit the deployed ansatz plugin (use your favourite text editor instead of `vi`)
```
$ ck plugin_path vqe --type=ansatz
$ vi `ck plugin_path vqe --type=ansatz`
```
**NB:** The ansatz plugin is written in Python with QISKit.
It is expected to contain only one top-level function.
If you need more, please define them within the top-level one.

## Locate the experimental results

You can list all your experimental entries and locate them on disk as follows:
```
$ ck search local:experiment:* --tags=qck
$ ck find local:experiment:*
```

## View the TTS metric

Run the following and select an experiment entry to compute TTS for:
```
$ ck time_to_solution vqe --delta=0.015 --prob=0.95
```

To compute TTS for a particular experiment, supply its entry e.g.:
```
$ ck time_to_solution vqe --delta=0.015 --prob=0.8 local:experiment:anton-2018_10_05T12_18_19-local_qasm_simulator-ansatz.universal4-optimizer.cobyla-samples.100-repetitions.1
```

## Upload your experimental results to Quantum Collective Knowledge

When you have an experiment you would like to share, run:
```
$ ck upload vqe --team=schroedinger-cat-herders
```
and select the experiment from the list. We recommend uploading all experiments on the hardware and most successful experiments on the simulators.

Alternatively, upload one or more experiments by using their entries e.g.:
```
$ ck upload vqe --team=bell-state-ringers local:experiment:my_experiment_5 local:experiment:my_experiment_13
```