goat-quantumcontrol 0.0.3.5

Python implementation of the quantum optimal control GOAT-algorithm

goat_quantumcontrol

goat_quantumcontrol is a Python library for the optimization of quantum gates using the GOAT algorithm.

The algorithm was developed in 2018 by Machnes et. al. and got published in following paper: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.150401

Installation

Use the package manager pip to install goat_quantumcontrol.

pip install goat_quantumcontrol

Usage

import goat_quantumcontrol as Qgoat

#-----System parameters------
# define the drift Hamiltonian
H0 = sigmax
# define the control Hamiltonian
Hdrive = sigmaz
# define the target gate
Utarget = X_gate

#-----Pulse parameters------
# define the number of time intervals
n_ts = 1000
# define the evolution time
evo_time = 3
# define the number of amps
num_of_amps = 2

#-----Optimization parameters--
# define the number of maximal iterations
max_iter = 200
# define the gtol parameter of the scipy 'BFGS' optimizer
gtol = 1e-10

# create an instance of the Pulse class to be used
fourier_pulse = Qgoat.FourierPulseWithEnvelope(n_ts=n_ts,
                                               evo_time=evo_time,
                                               num_of_amps=num_of_amps,
                                               window=None)

# create initial guess_amps with 
fourier_pulse.create_guess_amps()

# create an instance of the Optimizer class
optimizer = Qgoat.Optimizer(H0=H0, Hdrive=Hdrive,
                            target=Utarget,
                            pulse=fourier_pulse,
                            max_iter=max_iter, gtol=gtol,
                            printProgress=True)

# run the optimization
optimizer.run_optimization()

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT