c3.experiment

Experiment class that models and simulates the whole experiment.

It combines the information about the model of the quantum device, the control stack and the operations that can be done on the device.

Given this information an experiment run is simulated, returning either processes, states or populations.

Module Contents

class c3.experiment.Experiment(pmap: c3.parametermap.ParameterMap = None, prop_method=None, sim_res=100000000000.0)[source]

It models all of the behaviour of the physical experiment, serving as a host for the individual parts making up the experiment.

Parameters

pmap (ParameterMap) –

including model: Model

The underlying physical device.

generator: Generator

The infrastructure for generating and sending control signals to the device.

gateset: GateSet

A gate level description of the operations implemented by control pulses.

set_prop_method(prop_method=None) → None[source]

Configure the selected propagation method by either linking the function handle or looking it up in the library.

enable_qasm() → None[source]

Switch the sequencing format to QASM. Will become the default.

set_created_by(config)[source]

Store the config file location used to created this experiment.

load_quick_setup(filepath: str) → None[source]

Load a quick setup file.

Parameters

filepath (str) – Location of the configuration file

quick_setup(cfg, base_dir: str = None) → None[source]

Load a quick setup cfg and create all necessary components.

Parameters

cfg (Dict) – Configuration options

read_config(filepath: str) → None[source]

Load a file and parse it to create a Model object.

Parameters

filepath (str) – Location of the configuration file

from_dict(cfg: Dict) → None[source]

Load experiment from dictionary

write_config(filepath: str) → None[source]

Write dictionary to a HJSON file.

asdict() → Dict[source]

Return a dictionary compatible with config files.

__str__() → str[source]

Return str(self).

evaluate_legacy(sequences, psi_init: tensorflow.Tensor = None)[source]

Compute the population values for a given sequence of operations.

Parameters

  • sequences (str list) – A list of control pulses/gates to perform on the device.

  • psi_init (tf.Tensor) – A tensor containing the initial statevector

Returns

A list of populations

Return type

list

evaluate_qasm(sequences, psi_init: tensorflow.Tensor = None)[source]

Compute the population values for a given sequence (in QASM format) of operations.

Parameters

  • sequences (dict list) – A list of control pulses/gates to perform on the device in QASM format.

  • psi_init (tf.Tensor) – A tensor containing the initial statevector

Returns

A list of populations

Return type

list

lookup_gate(name, qubits, params=None) → tensorflow.constant[source]

Returns a fixed operation or a parametric virtual Z gate. To be extended to general parametric gates.

get_VZ(target, params)[source]

Returns the appropriate Z-rotation.

process(populations, labels=None)[source]

Apply a readout procedure to a population vector. Very specialized at the moment.

Parameters

  • populations (list) – List of populations from evaluating.

  • labels (list) – List of state labels specifying a subspace.

Returns

A list of processed populations.

Return type

list

get_perfect_gates(gate_keys: list = None) → Dict[str, numpy.ndarray][source]

Return a perfect gateset for the gate_keys.

Parameters

gate_keys (list) – (Optional) List of gates to evaluate.

Returns

A dictionary of gate names and np.array representation of the corresponding unitary

Return type

Dict[str, np.array]

Raises

Exception – Raise general exception for undefined gate

compute_propagators()[source]

Compute the unitary representation of operations. If no operations are specified in self.opt_gates the complete gateset is computed.

Returns

A dictionary of gate names and their unitary representation.

Return type

dict

set_opt_gates(gates)[source]

Specify a selection of gates to be computed.

Parameters

gates (Identifiers of the gates of interest. Can contain duplicates.) –

set_opt_gates_seq(seqs)[source]

Specify a selection of gates to be computed.

Parameters

seqs (Identifiers of the sequences of interest. Can contain duplicates.) –

set_enable_store_unitaries(flag, logdir, exist_ok=False)[source]

Saving of unitary propagators.

Parameters

  • flag (boolean) – Enable or disable saving.

  • logdir (str) – File path location for the resulting unitaries.

store_Udict(goal)[source]

Save unitary as text and pickle.

goal: tf.float64

Value of the goal function, if used.

populations(state, lindbladian)[source]

Compute populations from a state or density vector.

Parameters

  • state (tf.Tensor) – State or densitiy vector.

  • lindbladian (boolean) – Specify if conversion to density matrix is needed.

Returns

Vector of populations.

Return type

tf.Tensor

compute_states(solver='rk4', step_function='schrodinger')[source]

Use a state solver to compute the trajectory of the system.

Returns

List of states of the system from simulation.

Return type

List[tf.tensor]

compute_final_state(solver='rk4', step_function='schrodinger')[source]

Solve the Lindblad master equation by integrating the differential equation of the density matrix

Returns

Final state after time evolution.

Return type

List