paraqeet.model.differentiable_hamiltonian.DifferentiableHamiltonian

class paraqeet.model.differentiable_hamiltonian.DifferentiableHamiltonian(drives=None)

Bases: Differentiable, Hamiltonian

A Hamiltonian that also satisfies the differentiable interface. It requires a gradient function that works on a per timestep basis and provides a vectorized mapping to evaluate multiple time values.

Parameters:

drives (list[Drive] | None)

__init__(drives=None)

Parameters:

drives (list[Drive] | None)

Methods

__init__([drives])
dimension()	Return the dimension of the Hilbert space of this Hamiltonian.
get_collapseops()	Return a list tuples of decay rates and collapse operators for each subsystem.
get_gradient_at_timestep(time)	Compute the gradient of this Hamiltonian wrt to parameters for a single timestep.
get_parameters()	Return all parameters of this class that can be optimized.
get_value(times)	Return the matrix representation of the Hamiltonian.
get_value_and_gradient(times)	Compute value and gradient for given timesteps.
get_value_at_timestep(timestep)	Return the matrix representation of the Hamiltonian.
set_all_optimizable_parameters(all_params)	Set all optimizable parameters in the optimization.
set_optimizable_parameters(params)	Set which parameters associated with the object shall be considered during optimization.

Attributes

all_optimizable_parameters	Get the optimizable parameters
drives	Return the list of Drives of the system.
name	Get the name of the parameter.
optimizable_parameters	Get the optimizable parameters

property all_optimizable_parameters: list[Quantity]

Get the optimizable parameters

Returns:

The list of all the optimizable parameters considered in the optimization

Return type:

list[Quantity]

abstract dimension()

Return the dimension of the Hilbert space of this Hamiltonian.

Returns:

Returns the dimension of the Hilbert space of this Hamiltonian.

Return type:

int

property drives: list[Drive]

Return the list of Drives of the system.

Returns:

Returns a list of time-dependent drives of the system.

Return type:

list[Drive]

abstract get_collapseops()

Return a list tuples of decay rates and collapse operators for each subsystem.

Returns:

List of collapse operators

Return type:

list[Tuple[Array, Array]]

abstract get_gradient_at_timestep(time)

Compute the gradient of this Hamiltonian wrt to parameters for a single timestep.

Parameters:

time (float)

Return type:

Array

abstract get_parameters()

Return all parameters of this class that can be optimized.

Raises:

NotImplementedError – Subclasses derived from this class must implement this method.

Return type:

list[Quantity]

get_value(times)

Return the matrix representation of the Hamiltonian.

The default implementation calls get_value_at_timestep for each time step. Subclasses can override this function for a more efficient implementation.

Parameters:

times (Array) – Vector of time samples.

Returns:

Hamiltonian of shape [t, n, n] with ‘t’ as time and ‘n’ as the Hilbert space dimension.

Return type:

Array

get_value_and_gradient(times)

Compute value and gradient for given timesteps. The gradient call uses vmap over at_timestep methods.

Parameters:

times (Array)

Return type:

tuple[Array, Array]

abstract get_value_at_timestep(timestep)

Return the matrix representation of the Hamiltonian.

Parameters:

timestep (float) – One time point.

Returns:

Hamiltonian of shape [n, n] with n as the Hilbert space dimension.

Return type:

Array

property name: str | None

Get the name of the parameter.

Returns:

Name of the parameter.

Return type:

str | None

property optimizable_parameters: list[Quantity]

Get the optimizable parameters

Returns:

The list of optimizable parameters associated with the object.

Return type:

list[Quantity]

set_all_optimizable_parameters(all_params)

Set all optimizable parameters in the optimization.

Parameters:

• params (List[Quantity]) – List of optimizable parameters to be set.

• all_params (list[Quantity])

Return type:

None

set_optimizable_parameters(params)

Set which parameters associated with the object shall be considered during optimization.

All quantities that are not in the response of get_parameters will be filtered out. This function is called by the optimizer before gradient based optimization to tell the layers which gradients to compute.

Parameters:

params (list[Quantity]) – List of optimizable parameters to be set.

Return type:

None