Awesome
LibMark2
A simple python package for sending and pulling VQE run results to/from WebMark2
Getting started
You can check the currently viable guide on how to get started with Libmark from the quickstart guide.
There is also an example notebook available which showcases the functionality.
Using the package
Make sure that the package is somewhere in your PYTHONPATH, then after using
from LibMark2.quantmark.tracker import get_tracker
qresult = get_tracker(optimizer, 'TOKEN')
where "optimizer" is simply a string (name of optimizer used). The token can be acquired from the Quantmark website. 'get_tracker' returns the correct Result object.
Data can be added to the object using
qresult.add_run(results, molecule, hamiltonian, ansatz)
Where 'results' is an object returned by tq.minimize.
Data can be sent to WebMark2, by calling the push
function of any Result object
qresult.push()
Want to save the data?
qresult.save()
will save a JSON file to the root.
Pushing
# Generic batch optimize for "any" VQE
def run_vqe(molecules, hamiltonian_function, ansatz_function, optimizer, silent=True, **ansatz_kwargs):
results = []
qresult = get_tracker(optimizer, 'TOKEN_HERE') # Start quantmark tracking
for i, molecule in enumerate(molecules):
print(str(i+1)+"/"+str(len(molecules)), end="\t")
print("Creating the Hamiltonian.", end="\t")
H = hamiltonian_function(molecule)
n_qubits = len(H.qubits)
print("Creating ansatz.", end="\t")
U = ansatz_function(molecule=molecule, n_qubits=n_qubits, **ansatz_kwargs)
print("Creating objective function")
E = tq.ExpectationValue(H=H, U=U)
variables = {k:0.0 for k in U.extract_variables()}
print("Optimizing.", end="\t")
result = tq.minimize(objective=E, method=optimizer, initial_values=variables, silent=silent)
print()
results.append(result)
qresult.add_run(result, molecule, H, U) # Adding the intermediate result
print("Done")
qresult.push() # Push results to the server
qresult.save() # Save the data to a JSON file
return results
Pulling
You can pull any public result (or your own result) from WebMark:
from LibMark2.quantmark.api import get_experiment, get_data
data = get_data(id, 'TOKEN_HERE') # Download all available information as a dict
experiment = get_experiment(id, 'TOKEN_HERE') # Download experiment
results = experiment.run_experiment() # Run the experiment
"results" will contain a list of tuples (distance, results)