• Automation framework: CK
  
• Workflow: quantum hackathon workflow
  
• Development repository: ck-quantum
  
• Source: GitHub
  
• How to get the stable version from this portal via the CK client:
  
  pip install cbench
  cb download module:hackathon.20181006 --version=1.0.0 --all
  ck help hackathon.20181006
  
• How to get the development version:
  
  pip install ck
  ck pull repo:ck-quantum
  ck help hackathon.20181006
  
• CK automation actions (CLI with the Python CK API and JSON IO):
  
  • ck get_raw_config hackathon.20181006 - get raw config for repo widget (API)
    
  • ck get_raw_data hackathon.20181006 - get raw data for repo-widget (API)
    

London Quantum Computing Hackathon, 6 October 2018

• Meeetup (fully booked 5 weeks in advance!)
• Slides (check often for updates)

Getting started

1. Please register at Quantum Experience
   1. Copy your API token from the "Advanced" tab (click on the "Regenerate" button first).
   2. For the day, please also change your "Institution" to "QuantumHackers" in the "Account" tab.
2. Follow CK-QISKit instructions.

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 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

• 1.0.0 (2019-10-16)

• .cm/desc.json (3 bytes)
• .cm/info.json (313 bytes)
• .cm/meta.json (6824 bytes)
• .cm/updates.json (326 bytes)
• README.md (5504 bytes)
• module.py (11925 bytes)