Class

Tomography

Tomography()

This is the main tomography object that the library is built around.

The goal is to only have one tomography object and edit the configuration settings as you go.
The constructor initalizes the conf settings and error functions with the default values below:


							self.conf = {'NQubits': 2,

							'NDetectors': 1,

							'Crosstalk': np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]),

							'Bellstate': 0,

							'DoDriftCorrection': 0,

							'DoAccidentalCorrection' : 1,

							'DoErrorEstimation': 0,

							'Window': 0,

							'Efficiency': 0,

							'RhoStart': [],

							'IntensityMap': [[1]],

							'Beta': 0}

							self.err_functions = ['concurrence','tangle','entropy','linear_entropy','negativity','purity']

Tomography.conf

The configuration settings of the tomography.

Tomography.err_functions

An array of properties that you want to be calculated when getProperties is called.

The array contains strings of the function names for the desired properties with no parentheses. Default will contain all the properties.

Possible Values:

concurrence
entropy
linear_entropy
negativity
purity
tangle

Tomography.filter_data

Tomography.filter_data(raw_counts, intensities)

Filters the data into separate arrays.

raw_counts : 2d numpy array

The input data for the current tomography. This is what tomo_input will be set to. Example can be seen at top of page.
See getTomoInputTemplate() to get a template for this input.
intensities : 1d numpy array with length = number of measurements

Relative pump power (arb. units) during measurement; used for drift correction.

data : 1d numpy array with length = number of measurements or size =(number of measurements,2^numQubits) for 2 det/qubit

The counts of the tomography.
m : 3d numpy array with size = (2^numQubits,2^numQubits,number of measurements)

The measurements of the tomography in density matrix form.
m2 : 2d numpy array with size = (number of measurements,2*numQubits)

The measurements of the tomography in pure state form.
acc : 1d numpy array with length = number of measurements or size =(number of measurements,2^numQubits) for 2 det/qubit

The singles values of the tomography. Used for accidental correction.

Tomography.getProperties

Tomography.getProperties(rho,bounds)

Returns the properties of the given density matrix.

Using bounds will not change the conf settings. The properties are determined by err_functions

rho : 2d numpy array with size = (2^numQubits,2^numQubits)

The density matrix you would like to know the properties of.
bounds : boolean

Set this to true if you want error bounds on your estimated property values. Default will use the conf settings.
These are determined with monte carlo simulation and the states are saved under Tomography.mont_carl_states.

vals : 2d numpy array with size = (length of self.err_functions,2)

The first col is the name of the property.
The second col is the value of the property.
The third col is the error bound on the property.

Tomography.getBasisMeas

Tomography.getBasisMeas(numBits)

Returns an array of standard measurments in pure state form for the given number of qubits.

numBits : int

number of qubits you want for each measurement. Default will use the number of qubits in the current configurations.

Tomography.getCoincidences

Tomography.getCoincidences()

Returns an array of counts for all the measurments.

Tomography.getMeasurements

Tomography.getMeasurements()

Returns an array of measurements in pure state form for all the measurments.

Tomography.getNumBits

Tomography.getNumBits()

Returns the number of qubits for the current configurations.

Tomography.getNumCoinc

Tomography.getNumCoinc()

Returns the number of coincidences per measurement for the current configurations.

Tomography.getNumDetPerQubit

Tomography.getNumDetPerQubit()

Returns the number of detectors per qubit for the current configurations.

Tomography.getNumOfDetectorsTotal

Tomography.getNumOfDetectorsTotal()

Returns the total number of detectors for the given data.

Tomography.getNumSingles

Tomography.getNumSingles()

Returns the number of singles per measurement for the current configurations.

Tomography.getSingles

Tomography.getSingles()

Returns an array of singles for all the measurments.

Tomography.getTimes

Tomography.getTimes()

Returns an array of times for all the measurments.

Tomography.getTomoInputTemplate

Tomography.getTomoInputTemplate(numBits)

Returns a standard template for tomo_input for the given number of qubits.

numBits : int

number of qubits you want for each measurement. Default will use the number of qubits in the current configurations.

Tomography.importConf

Tomography.importConf(conftxt)

Import a text file containing the configuration settings.

Details for the configuration file can be found in the reference for Conf File.

conftxt : string

path to configuration file

Tomography.importData

Tomography.importData(datatxt)

Import a text file containing the tomography data and run tomography.

Details for the data file can be found in the reference for Data File.

datatxt : string

path to data file

Tomography.importEval

Tomography.importEval(evaltxt)

Import a eval file containing the tomography data and the configuration setting, then run tomography.

Details for the eval file can be found in the reference for Eval File.

evaltxt : string

path to eval file

Tomography.intensity

Relative pump power (arb. units) during measurement for the last tomography run.

Tomography.last_fval

The final value of the internal optimization function for the last tomography run.

Tomography.last_intensity

The predicted intensity of the state for the last tomography run.

Tomography.last_rho

The predicted density matrix of the last tomography run.

Tomography.linear_tomography

Tomography.linear_tomography(data, measurements)

Uses linear techniques to find a starting state for maximum likelihood estimation.

data : 1d numpy array with length = number of measurements or 1d numpy array with size =(number of measurements,2^numQubits) for 2 det/qubit

The counts of the tomography.
measurements : 2d numpy array with size = (number of measurements,2*numQubits)

The measurements of the tomography in density matrix form.

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

The starting predicted state.
intensity : float

The predicted overall intensity used to normalize the state.

Tomography.maximum_likelihood_tomography

Tomography.maximum_likelihood_tomography(rho0, data, m, acc)

Calculates the most likely state given the data.

This function uses a least squares method to find the final predicted state.

rho0 : 2d numpy array with size = (2^numQubits,2^numQubits)

The starting predicted state found with linear tomography.
data : 1d numpy array with length = number of measurements or size =(number of measurements,2^numQubits) for 2 det/qubit

The counts of the tomography.
m : 3d numpy array with size = (2^numQubits,2^numQubits,number of measurements)

The measurements of the tomography in density matrix form.
acc : 1d numpy array with length = number of measurements or 2d numpy array with size =(number of measurements,2^numQubits) for 2 det/qubit

The singles values of the tomography. Used for accidental correction.

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

The predicted density matrix.
intensity : float

The predicted overall intensity used to normalize the state.
fvalp : float

Final value of the internal optimization function. Values greater than the number of measurements indicate poor agreement with a quantum state.

Tomography.maxlike_fitness

Tomography.maxlike_fitness(t, data, accidentals, m, prediction)

Calculates the diffrence between the current predicted state data and the actual data.

t : 1d numpy array

T values of the current predicted state.
data : 1d numpy array with length = number of measurements or 2d numpy array with size =(number of measurements,2^numQubits) for 2 det/qubit

The counts of the tomography.
accidentals : 1d numpy array with length = number of measurements or 2d numpy array with size =(number of measurements,2^numQubits) for 2 det/qubit

The singles values of the tomography. Used for accidental correction.
m : 2d numpy array with size = (2^numQubits,2^numQubits,number of measurements)

The measurements of the tomography in density matrix form.
prediction : 1d numpy array

Predicted counts from the predicted state.

val : float

value of the optimization function.

Tomography.maxlike_fitness_hedged

Tomography.maxlike_fitness_hedged(t, data, accidentals, m, prediction,bet)

Calculates the diffrence between the current predicted state data and the actual data using hedged maximum likelihood.

t : 2d numpy array

T values of the current predicted state.
data : 1d numpy array with length = number of measurements or 2d numpy array with size =(number of measurements,2^numQubits) for 2 det/qubit

The counts of the tomography.
accidentals : 1d numpy array with length = number of measurements or 2d numpy array with size =(number of measurements,2^numQubits)for 2 det/qubit

The singles values of the tomography. Used for accidental correction.
m : 3d numpy array with size = (2^numQubits,2^numQubits,number of measurements)

The measurements of the tomography in density matrix form.
prediction : 1d numpy array

Predicted counts from the predicted state.
bet : float 0 to .5

The beta value used.

val : float

value of the optimization function.

Tomography.mont_carl_states

The generated monte carlo states.

Value is 0 if no states have been generated yet.

Tomography.setConfSetting

Tomography.setConfSetting(setting,val)

Sets a specific self.conf setting

setting : string

The setting you want to update.
Possible values are: ['NQubits', 'NDetectors', 'Crosstalk', 'Bellstate', 'DoDriftCorrection', 'DoAccidentalCorrection', 'DoErrorEstimation', 'Window', 'Efficiency', 'RhoStart', 'IntensityMap', 'Beta']
val: ndarray, int, or string

The new value you want to the setting to be.

Tomography.state_tomography

Tomography.state_tomography(raw_counts, intensities = -1)

Desc: Main function that runs tomography.

This function first parses the data, then finds an approximate starting state. It then uses maximum likelihood to find the final predicted state.

raw_counts : 2d numpy array

The input data for the current tomography. This is what tomo_input will be set to. Example can be seen at top of page.
See getTomoInputTemplate() to get a template for this input.
intensities : 1d numpy array with length = number of measurements

The predicted overall intensity used to normalize the state. Default will be an array of ones

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

The predicted density matrix.
intensity : float

The predicted overall intensity used to normalize the state.
fvalp : float

Final value of the internal optimization function. Values greater than the number of measurements indicate poor agreement with a quantum state.

Tomography.tomo_input

The raw input data of the last tomography run.

Tomography.getBellSettings

Tomography.getBellSettings(rho,bounds = -1)

Returns the optimal measurment settings for the CHSH bell inequality.

Using bounds will not change the conf settings.

rho : 2d numpy array with size = (2^numQubits,2^numQubits)

The density matrix you would like to know the optimal bell measurment settings of.
bounds : boolean

Set this to true if you want error bounds on your estimated measurment settings. Default will use the conf settings.
These are determined with monte carlo simulation and the states are saved under self.mont_carl_states

vals : 2d numpy array with size = (length of self.err_functions,2 or 3)

The first col is the associated side
The second col is the detector settings on the associated side.
The third col is the error bound on the detector settings.

Tomography.tomography_states_generator

Tomography.tomography_states_generator(n)

Uses monte carlo simulation to create random states similar to the estimated state.

The states are also saved under Tomography.mont_carl_states

n : int

Number of approximate states you want to create.
If no value is given it will use the conf settings.

rhop : 3d numpy array with shape = (n,2^numQubits,2^numQubits)

The approximate density matrices.
intenp : 1d numpy array with length = n

The intensity of each approximate density matrices.
fvalp : 1d numpy array with length = n

The fval of the regression associated with each approximate density matrices.

concurrence

concurrence(rhog)

Calculates the concurrence of the input state.

An entanglement monotone defined for a mixed state of two qubits. Where 1 corresponds to a completly entangled state.
Defined as max (0,λ₁-λ₂-λ₃-λ₄) where λ_i are the eigenvalues, in decreasing order.
Read more about concurrence: concurrence on wikipedia

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

Density Matrix of the desired state.

val : float

The calculated concurrence.

Other Properties:

entropy
linear_entropy
negativity
purity
tangle

density2t

density2t(rhog)

Converts a density matrix into a list of t values

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

Array to be converted.

t : 1d numpy array

List of t values defining the input matrix.

density2tm

density2tm(rhog)

Converts a density matrix into a lower t matrix.

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

Array to be converted.

tm : 2d numpy array with size = (2^numQubits,2^numQubits)

Lower t matrix defining the input matrix.

entropy

entropy(rhog)

Calculates the Von Neumann entropy of the input state.

Defined as -Σ λ_i*ln(λ_i) Where λ_i are the eigenvalues.
Read more about entropy: entropy on wikipedia

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

Density Matrix of the desired state.

val : float

The calculated entropy.

Other Properties:

concurrence
linear_entropy
negativity
purity
tangle

fidelity

fidelity(state1, state2)

Calculates the fidelity between the two input states.

Defined as trace(sqrtm(sqrtm(P) * Q * sqrtm(P)))^2. Where P & Q the density matrices of the two states and sqrtm is the matrix square root.
Read more about fidelity: fidelity on wikipedia

state1, state2 : 2d numpy array with equal sizes = (2^numQubits,2^numQubits)

Input arrays to calculate the fidelity between. Can be pure states or density matrices.

val : float

The calculated fidelity.

linear_entropy

linear_entropy(rhog)

Calculates the linear entropy of the input state.

Mixture measurement ranging from 0 to 1/(2^numQubits). Where 0 corresponds to a completly pure state and 1/(2^numQubits) corresponds to a completely mixed state.
Defined as 1-tr(rho^2). Read more about linear entropy: linear entropy on wikipedia

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

Density Matrix of the desired state.

val : float

The calculated linear entropy. ranging from 0 to 1/(2^numQubits).
A value of zero corresponds to a completly pure state.

Other Properties:

concurrence
entropy
negativity
purity
tangle

negativity

negativity(rhog)

Calculates the negativity of the input state.

Defined as Σ |λ_i| Where λ_i are the negative eigenvalues of the partial transpose of the matrix.
Read more about negativity: negativity on wikipedia

rhog : 2d numpy array with size = (2^numQubits,2^numQubits)

Density Matrix of the desired state.

val : float

The calculated negativity.

Other Properties:

concurrence
entropy
linear_entropy
purity
tangle

performOperation

performOperation(psi, g)

Performs the operations on the input State.

psi : ndarray

The input state to do the operation on. Can be a pure state or a density matrix.
g : 3d numpy array with shape = (num operations,2^numQubits,2^numQubits)

The operations you would like to be done. Can be one operation or an array of operations.

p : ndarray

The output state after the operations. Will retain the input form.

purity

purity(rhog)

Calculates the purity of the input state.

Mixture measurement ranging from 0 to 1/(2^numQubits). Where 1/(2^numQubits) corresponds to a completely mixed state and 1 corresponds to a completly pure state.
Defined as tr(rho^2)
Read more about purity: purity on wikipedia

rhog : 2d numpy array with shape = (2^numQubits,2^numQubits)

Density Matrix of the desired state.

val : float

The calculated purity ranging from 1/(2^numQubits) to 1.
A value of one corresponds to a completly pure state.

Other Properties:

concurrence
entropy
linear_entropy
negativity
tangle

t_matrix

t_matrix(t)

Converts a list of t values to an lower t matrix.

t : 1d numpy array

List of t values converted.

tm : 2d numpy array with shape = (2^numQubits,2^numQubits)

Lower t matrix.

t_to_density

t_to_density(t)

Converts a list of t values to a density matrix.

t : 1d numpy array

List of t values converted.

rhog : 2d numpy array with shape = (2^numQubits,2^numQubits)

Density Matrix.

tangle

tangle(rhog)

Calculates the tangle of the input state.

Entanglement Measure ranging from (0,1) where 1 corresponds to a completly entangled state.
Defined as the square of concurrence.

rhog : 2d numpy array with shape = (2^numQubits,2^numQubits)

Density Matrix of the desired state.

val : float

The calculated tangle.

Other Properties:

concurrence
entropy
linear_entropy
negativity
purity

toDensity

toDensity(psiMat)

Converts a pure state into a density matrix.

psiMat : 1d numpy array with length = (2^numQubits)

Pure state to be converted.

rhog: 2d numpy array with shape = (2^numQubits,2^numQubits)

Density Matrix of the input state.

printLastOutput

printLastOutput(tomo,bounds)

Prints the properties of the last tomography to the console.

Using bounds will not change the conf settings. The calculated properties are determined by self.err_functions.

tomo : Tomography() Object

This is the main tomography object. It will get the data from it's last tomography and print it.

bounds : boolean

Set this to true if you want error bounds on your estimated property values. Default is False. These are determined with monte carlo simulation and the states are saved under self.mont_carl_states.

makeRhoImages

makeRhoImages(p,plt_given,customColor)

Creates matlab plots of the density matrix.

p : 2d numpy array with shape = (n,2^numQubits,2^numQubits)

The density matrix you want to create plots of.
plt_given : matplotlib.pyplot

Input pyplot for which the figures will be saved on to.
customColor : boolean

Specify if you want our custom colorMap. Default is true.

saveRhoImages

saveRhoImages(p,pathToDirectory,customColor)

Creates and saves matlab plots of the density matrix.

p : 3d numpy array with shape = (n,2^numQubits,2^numQubits)

The density matrix you want to create plots of.
pathToDirectory : string

Path to where you ant your images to be saved.
customColor : boolean

Specify if you want our custom colorMap. Default is true.

matrixToHTML

matrixToHTML(M)

Creates an HTML table based on the given matrix.

M : 2d numpy array with shape = (2^numQubits,2^numQubits)

Matrix you would like to display on your html page.
printEigenVals : boolean

Specify if you want eigen values to be calculated and displayed at the bottom of the table.

res : string

HTML code of the created table.

propertiesToHTML

propertiesToHTML(vals)

Creates an HTML table based on the given property values.

vals : 2d numpy array with size = (length of self.err_functions,2)

The first col is the name of the property.
The second col is the value of the property.
The third col is the error bound on the property.

res : string

HTML code of the created table.

Conf File

This file states the configurations of the tomography.

The syntax of the txt file is python. You write the conf settings just like you would set a python dictionary.

These are the following configuration settings:

'NQubits'
- Values : >= 1
- Desc : The number of qubits the quantum state has. It will take exponentially more time for more qubits.
- Default : 2
'NDetectors'
- Values : 1 or 2
- Desc : The number of detectors per qubit used during the physical tomography of the quantum state.
- Default : 1
'ctalk'
- Values : matrix that is (2^NQubits) by (2^NQubits)
- Desc : Cross talk Matrix of the setup.
- Default : identity matrix with appropriate size
'Bellstate'
- Values : 'no' or 'yes'
- Desc : Give the optimal measurement settings for a CHSH bell inequality for the estimated density matrix. These settings are found through a numerical search over all possible measurement settings.
- Default : 'no'
'DoDriftCorrection'
- Values : 'no' or 'yes'
- Desc : Whether of not you want to perform drift correction on the state
- Default : 'no'
'DoAccidentalCorrection'
- Values : 'no' or 'yes'
- Desc : Whether of not you want to perform accidental corrections on the state.
- Default : 'no'
'Window'
- Values : 0 or array like, dimension = 1
- Desc : Coincidence window durations (in nanoseconds) to calculate the accidental rates. The four windows should be entered in the order of the detector pairs 1-2, 1-4, 3-2, 3-4, where A-B corresponds to a coincidence measurement between detector A and detector B.
- Default : '0'
'Efficiency'
- Values : 0 or array like, dimension = 1
- Desc : vector that lists the relative coincidence efficiencies of detector pairs when using 2 detectors per qubit. The order is detector 1-2, 1-4, 3-2, 3-4.
- Default : 0

Example:

conf['NQubits'] = 2
							
conf['NDetectors'] = 1
							
conf['Crosstalk'] = [[0.9842,0.0049,0.0049,0],[0.0079,0.9871,0,0.0050],[0.0079,0,0.9871,0.0050],[0.001,0.0079,0.0079,0.9901]]
							
conf['UseDerivative'] = 0
							
conf['Bellstate'] = 1
							
conf['DoErrorEstimation'] = 3
							
conf['DoDriftCorrection'] = 'no'
							
conf['Window'] = 0
							
conf['Efficiency'] = [0.9998,1.0146,0.9195,0.9265]

Data File

This file states the data of the measurements.Both tomo_input the intensity must be specified.

The syntax of the txt file is python. You write the data settings just like you would set a python matrix.

This is the following layout of the tomo_input matrix:

tomo_input
- Values : 2d numpy array
- Desc : Relative pump power (arb. units) during measurement; used for drift correction.
- tomo_input[ :, 0 ]: times
- tomo_input[ :, 1 : n_qubit + 1) ]: singles
- tomo_input[ :, n_qubit + 1 ]: coincidences
- tomo_input[ :, n_qubit + 2 : 3 * n_qubit + 2) ]: measurements
- tomo_input[ :, 0 ]: times
- tomo_input[ :, 1 : 2 * n_qubit + 1 ]: singles
- tomo_input[ :, 2 * n_qubit + 1 : 2 ** n_qubit + 2 * n_qubit + 1 ]: coincidences
- tomo_input[ :, 2 ** n_qubit + 2 * n_qubit + 1 : 2 ** n_qubit + 4 * n_qubit + 1 ]: measurements
intensity
- Values : 1d numpy array
- Desc : Relative pump power (arb. units) during measurement; used for drift correction.

Example:

This example is for 2 qubits using 1 detector.


							tomo_input = np.array(

																		[[1,0,0,3708,1,0,1,0],

																		[1,0,0,77,1,0,0,1],

																		[1,0,0,1791,1,0,0.7071,0.7071],

																		[1,0,0,2048,1,0,0.7071,0.7071j],

																		[1,0,0,51,0,1,1,0],

																		[1,0,0,3642,0,1,0,1],

																		[1,0,0,2096,0,1,0.7071,0.7071],

																		[1,0,0,1926,0,1,0.7071,0.7071j],

																		[1,0,0,1766,0.7071,0.7071,1,0],

																		[1,0,0,1914,0.7071,0.7071,0,1],

																		[1,0,0,1713,0.7071,0.7071,0.7071,0.7071],

																		[1,0,0,3729,0.7071,0.7071,0.7071,0.7071j],

																		[1,0,0,2017,0.7071,0.7071j,1,0],

																		[1,0,0,1709,0.7071,0.7071j,0,1],

																		[1,0,0,3686,0.7071,0.7071j,0.7071,0.7071],

																		[1,0,0,2404,0.7071,0.7071j,0.7071,0.7071j]])

						intensity = np.array([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1])

Eval File

This text file contains all the information for a tomography.

It is essentially a conf file and a data file combined into one file.

Example:

This example is for 2 qubits using 1 detector.


							conf['NQubits'] = 2
conf['NDetectors'] = 1
								
conf['Crosstalk'] = [[0.9842,0.0049,0.0049,0],[0.0079,0.9871,0,0.0050],[0.0079,0,0.9871,0.0050],[0.001,0.0079,0.0079,0.9901]]
								
conf['UseDerivative'] = 0
								
conf['Bellstate'] = 1
								
conf['DoErrorEstimation'] = 3
								
conf['DoDriftCorrection'] = 'no'
								
conf['Window'] = 0
								
conf['Efficiency'] = [0.9998,1.0146,0.9195,0.9265]

							tomo_input = np.array(

																		[[1,0,0,3708,1,0,1,0],

																		[1,0,0,77,1,0,0,1],

																		[1,0,0,1791,1,0,0.7071,0.7071],

																		[1,0,0,2048,1,0,0.7071,0.7071j],

																		[1,0,0,51,0,1,1,0],

																		[1,0,0,3642,0,1,0,1],

																		[1,0,0,2096,0,1,0.7071,0.7071],

																		[1,0,0,1926,0,1,0.7071,0.7071j],

																		[1,0,0,1766,0.7071,0.7071,1,0],

																		[1,0,0,1914,0.7071,0.7071,0,1],

																		[1,0,0,1713,0.7071,0.7071,0.7071,0.7071],

																		[1,0,0,3729,0.7071,0.7071,0.7071,0.7071j],

																		[1,0,0,2017,0.7071,0.7071j,1,0],

																		[1,0,0,1709,0.7071,0.7071j,0,1],

																		[1,0,0,3686,0.7071,0.7071j,0.7071,0.7071],

																		[1,0,0,2404,0.7071,0.7071j,0.7071,0.7071j]])

						intensity = np.array([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1])

Table of Contents

TomoClass

TomoFunctions

TomoDisplay

Other Files

Class

Tomography

Tomography()

See Also:

Tomography.conf

Tomography.conf

See Also:

Tomography.err_functions

Tomography.err_functions

Possible Values:

See Also:

Tomography.filter_data

Tomography.filter_data(raw_counts, intensities)

Paramaters:

raw_counts : 2d numpy array

intensities : 1d numpy array with length = number of measurements

Returns:

data : 1d numpy array with length = number of measurements or size =(number of measurements,2^numQubits) for 2 det/qubit

m : 3d numpy array with size = (2^numQubits,2^numQubits,number of measurements)

m2 : 2d numpy array with size = (number of measurements,2*numQubits)

acc : 1d numpy array with length = number of measurements or size =(number of measurements,2^numQubits) for 2 det/qubit

Tomography.getProperties

Tomography.getProperties(rho,bounds)

Paramaters:

rho : 2d numpy array with size = (2^numQubits,2^numQubits)

bounds : boolean

Returns:

vals : 2d numpy array with size = (length of self.err_functions,2)

See Also:

Tomography.getBasisMeas

Tomography.getBasisMeas(numBits)

Paramaters:

numBits : int

Tomography.getCoincidences

Tomography.getCoincidences()

See Also:

Tomography.getMeasurements

Tomography.getMeasurements()

Tomography.getNumBits

Tomography.getNumBits()

Tomography.getNumCoinc

Tomography.getNumCoinc()

See Also:

Tomography.getNumDetPerQubit

Tomography.getNumDetPerQubit()

See Also:

Tomography.getNumOfDetectorsTotal

Tomography.getNumOfDetectorsTotal()

See Also:

Tomography.getNumSingles

Tomography.getNumSingles()

See Also:

Tomography.getSingles

Tomography.getSingles()

See Also:

Tomography.getTimes

Tomography.getTimes()

Tomography.getTomoInputTemplate

Tomography.getTomoInputTemplate(numBits)

Paramaters:

numBits : int

Tomography.importConf

Tomography.importConf(conftxt)

Paramaters:

conftxt : string

See Also:

Tomography.importData

Tomography.importData(datatxt)

Paramaters:

datatxt : string

See Also:

Tomography.importEval

Tomography.importEval(evaltxt)

Paramaters:

evaltxt : string