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 initializes 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.setConfSetting

Tomography.setConfSetting(setting, val)

Sets a specific self.conf setting. This is no longer needed but will be kept in the class to support old code.

setting : string

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

The new value you want to the setting to be.

Tomography.importConf

Tomography.importConf(conftxt)

Import a text file containing the configuration settings.

conftxt : string

path to configuration file

Tomography.importData

Tomography.importData(datatxt)

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

datatxt : string

path to data file

rhog : ndarray with shape = (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.importEval

Tomography.importEval(evaltxt)

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

evaltxt : string

path to eval file

rhog : ndarray with shape = (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.StateTomography

Tomography.StateTomography(measurements, counts)

Main function that runs tomography. This function requires a set of measurements and a set of counts.

measurements : ndarray shape = ( Number of measurements , 2*NQubits )

Each row in the matrix is a set of independent measurements.
counts : ndarray shape = (Number of measurements, NDetectors**NQubits )

Each row in the matrix is a set of independent measurements.
crosstalk : ndarray shape = ( Number of measurements , 2**NQubits,2**NQubits ) (optional)

The crosstalk matrix to compensate for inefficentcies in your beam splitter.
efficiency : 1darray with length = NQubits*NDetectors (optional)

The relative efficienies between your detector pairs.
time : 1darray with length = Number of measurements (optional)

The total duration of each measurment.
singles : ndarray shape = ( Number of measurements , 2*NQubits ) (optional)

The singles counts on each detector.
window : 1darray with length = NQubits*NDetectors (optional)

The coincidence window duration for each detector pair.
error : int (optional)

The number of monte carlo states used to estimate the properties of the state.
intensities : 1darray with length = Number of measurements (optional)

The relative intensity of each measurement. Used for drift correction.
method : ['MLE','HMLE','BME','LINER'] (optional)

Which method to use to run tomography. Default is MLE

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

The predicted density matrix.
intensity : The predicted overall intensity used to normalize the state.

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.StateTomography_Matrix

Tomography.StateTomography_Matrix(tomo_input, intensities)

Main function that runs tomography.

tomo_input : ndarray

The input data for the current tomography. Example can be seen at top of page.
intensities : 1darray with length = number of measurements (optional)

Relative pump power (arb. units) during measurement; used for drift correction. Default will be an array of ones
method : ['MLE','HMLE','BME','LINER'] (optional)

Which method to use to run tomography. Default is MLE

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

The predicted density matrix.
intensity : The predicted overall intensity used to normalize the state.

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.tomography_MLE

Tomography.tomography_MLE(starting_matrix, coincidences, measurements, accidentals,overall_norms)

Runs tomography using maximum likelyhood estimation.

starting_matrix : ndarray with shape = (2^numQubits, 2^numQubits)

The starting predicted state.
coincidences : ndarray shape = (Number of measurements, NDetectors**NQubits)

The counts of the tomography.
measurements_densities : ndarray with shape = (NDetectors*number of measurements,2^numQubits, 2^numQubits)

The measurements of the tomography in density matrix form.
accidentals : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

The accidental values of the tomography. Used for accidental correction.
overall_norms : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

The relative weights of each measurment. Used for drift correction.

rhog : ndarray with shape = (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.tomography_HMLE

Tomography.tomography_HMLE(starting_matrix, coincidences, measurements, accidentals,overall_norms)

Runs tomography using hedged maximum likelyhood estimation.

starting_matrix : ndarray with shape = (2^numQubits, 2^numQubits)

The starting predicted state.
coincidences : ndarray shape = (Number of measurements, NDetectors**NQubits)

The counts of the tomography.
measurements_densities : ndarray with shape = (NDetectors*number of measurements,2^numQubits, 2^numQubits)

The measurements of the tomography in density matrix form.
accidentals : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

The accidental values of the tomography. Used for accidental correction.
overall_norms : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

The relative weights of each measurment. Used for drift correction.

rhog : ndarray with shape = (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.tomography_LINEAR

Tomography.tomography_LINEAR(coincidences, measurements,overall_norms)

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

coincidences : ndarray shape = (Number of measurements, NDetectors**NQubits)

The counts of the tomography.
measurements : ndarray with shape = (NDetectors*number of measurements,2^numQubits)

The measurements of the tomography in pure state form.
overall_norms : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

The relative weights of each measurment. Used for drift correction.

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

The starting predicted state.
intensity : float

The predicted overall intensity used to normalize the state.

Tomography.filter_data

Tomography.filter_data(tomo_input)

Filters the data into separate arrays.

tomo_input : ndarray

The input data for the current tomography. Example can be seen at top of page.

coincidences : ndarray shape = (Number of measurements, NDetectors**NQubits)

The counts of the tomography.
measurements_densities : ndarray with shape = (NDetectors*number of measurements,2^numQubits, 2^numQubits)

The measurements of the tomography in density matrix form.
measurements_pures : ndarray with shape = (NDetectors*number of measurements, 2^numQubits)

The measurements of the tomography in pure state form.
acc : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

The accidental values of the tomography. Used for accidental correction.
overall_norms : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

The relative weights of each measurment. Used for drift correction.

Tomography.buildTomoInput

Tomography.buildTomoInput(tomo_input, intensities)

Description not currently available

desc : This function build an input matrix based on a variety of inputs.
measurements : ndarray shape = ( Number of measurements , 2*NQubits )

Each row in the matrix is a set of independent measurements.
counts : ndarray shape = (Number of measurements, NDetectors**NQubits )

Each row in the matrix is a set of independent measurements.
crosstalk : ndarray shape = ( Number of measurements , 2**NQubits,2**NQubits ) (optional)

The crosstalk matrix to compensate for inefficentcies in your beam splitter.
efficiency : 1darray with length = NQubits*NDetectors (optional)

The relative efficienies between your detector pairs.
time : 1darray with length = Number of measurements (optional)

The total duration of each measurment.
singles : ndarray shape = ( Number of measurements , 2*NQubits ) (optional)

The singles counts on each detector.
window : 1darray with length = NQubits*NDetectors (optional)

The coincidence window duration for each detector pair.
error : int (optional)

The number of monte carlo states used to estimate the properties of the state.
intensities : 1darray with length = Number of measurements (optional)

The relative intensity of each measurement. Used for drift correction.
method : ['MLE','HMLE','BME','LINER'] (optional)

Which method to use to run tomography. Default is MLE

tomo_input : ndarray

The input data for the current tomography.
intensities : 1darray with length = number of measurements

Relative pump power (arb. units) during measurement; used for drift correction. Default will be an array of ones

Tomography.checkForInvalidSettings

Tomography.checkForInvalidSettings()

Description not currently available

Tomography.getCoincidences

Tomography.getCoincidences()

Returns an array of counts for all the measurements.

Tomography.getSingles

Tomography.getSingles()

Returns an array of singles for all the measurements.

Tomography.getTimes

Tomography.getTimes()

Returns an array of times for all the measurements.

Tomography.getMeasurements

Tomography.getMeasurements()

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

Tomography.getNumQubits

Tomography.getNumQubits()

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.getNumSingles

Tomography.getNumSingles()

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

Tomography.getNumDetPerQubit

Tomography.getNumDetPerQubit()

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

Tomography.getStandardBasis

Tomography.getStandardBasis(numBit,numDet = -1)

Returns an array of standard measurements in separated pure state form for the given number of qubits. It is the same format used in the tomo_input matrix.

numBits : int

number of qubits you want for each measurement. Default will use the number of qubits in the current configurations.
numDet : 1 or 2

Number of detectors for each measurement Default will use the number of qubits in the current configurations.

Tomography.getTomoInputTemplate

Tomography.getTomoInputTemplate()

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.
numDet : 1 or 2

Number of detectors for each measurement Default will use the number of qubits in the current configurations.

Tomoinput : ndarray

The input data for the current tomography.Example can be seen at top of page.

Tomography.getProperties

Tomography.getProperties(bounds)

Returns all the properties of the given density matrix.

bounds : int (optional)

The number of monte carlo runs you want to perform to get a better estimate of each property. Default will use whatever is set in the conf settings.

vals : ndarray with shape = (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.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 self.mont_carlo_states

n : int

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

rhop : ndarray with shape = (n, 2^numQubits, 2^numQubits)

The approximate density matrices.
intenp : 1darray with length = n

The intensity of each approximate density matrices.
fvalp : 1darray with length = n

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

Tomography.getBellSettings

Tomography.getBellSettings(bounds)

Returns the optimal measurment settings for the CHSH bell inequality. In Progress, have not checked.

Tomography.printLastOutput

Tomography.printLastOutput(bounds)

Prints the properties of the last tomography to the console. Properties are defined in tomography conf settings. The calculated properties are determined by self.err_functions.

bounds : int (optional)

The number of monte carlo runs you want to perform to get a better estimate of each property. Default will use whatever is set in the conf settings.

Tomography.exportToEval

Tomography.exportToEval(filePath)

Exports the input data to the specified file path. You can rerun tomography on this file using importEval() command.

filePath : str (optional)

The file name you want the data saved to. See Also ------ importEval

Tomography.exportToConf

Tomography.exportToConf(filePath)

Exports the conf data to the specified file path. You can rerun tomography on this file using importConf() and importData() command.

filePath : str (optional)

The file name you want the data saved to. See Also ------ exportToData;importConf;importData

Tomography.exportToData

Tomography.exportToData(filePath)

Exports the tomo_input matrix data to the specified file path. You can rerun tomography on this file using importConf() and importData() command.

filePath : str (optional)

The file name you want the data saved to. See Also ------ exportToConf;importConf;importData

log_likelyhood

log_likelyhood(t, coincidences, accidentals, m, prediction)

This is the log likelyhood function. It used in bayesian tomography.

t : ndarray

T values of the current predicted state.
coincidences : ndarray with length = number of measurements or shape = (number of measurements, 2^numQubits) for 2 det/qubit

The counts of the tomography.
accidentals : ndarray with length = number of measurements or shape = (number of measurements, 2^numQubits) for 2 det/qubit

The singles values of the tomography. Used for accidental correction.
m : ndarray with shape = (2^numQubits, 2^numQubits, number of measurements)

The measurements of the tomography in density matrix form.
prediction : ndarray

Predicted counts from the predicted state.
overall_norms : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit. (optional)

The relative weights of each measurment. Used for drift correction.

val : float

value of the optimization function.

density2tm

density2tm(rhog)

Converts a density matrix into a lower t matrix.

rhog : ndarray

Array to be converted.

tm : ndarray

Lower t matrix defining the input matrix.

density2t

density2t(rhog)

Converts a density matrix into a list of t values

rhog : ndarray

Array to be converted.

t : ndarray

List of t values defining the input matrix.

toDensity

toDensity(psiMat)

Converts a pure state into a density matrix.

psiMat : ndarray

Pure state to be converted.

psiMat : ndarray

rhog: ndarray Density Matrix of the input state.

t_matrix

t_matrix(t)

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

t : ndarray

List of t values converted.

tm : ndarray

Lower t matrix.

t_to_density

t_to_density(t)

Converts a list of t values to a density matrix.

t : ndarray

List of t values converted.
normalize : bool

Set to true if you want the density matrix to be normalized by its trace. Default is True.

rhog : ndarray

Density Matrix.

fidelity

fidelity(state1, state2)

Calculates the fidelity between the two input states.

state1, state2 : ndarray

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

val : float

The calculated fidelity.

concurrence

concurrence(rhog)

Calculates the concurrence of the input state.

rhog : ndarray

Density Matrix of the desired state.
val : float

The calculated concurrence. Other Properties entropy;linear_entropy;negativity;purity;tangle See Also ------ err_functions;getProperties

tangle

tangle(rhog)

Calculates the tangle of the input state.

rhog : ndarray

Density Matrix of the desired state. Tangle is calculated by squaring the concurrence.
val : float

The calculated tangle. Other Properties entropy;linear_entropy;negativity;purity;concurrence See Also ------ err_functions;getProperties

entropy

entropy(rhog)

Calculates the Von Neumann Entropy of the input state.

rhog : ndarray

Density Matrix of the desired state.
val : float

The calculated entropy. Other Properties concurrence;linear_entropy;negativity;purity;tangle See Also ------ err_functions;getProperties

linear_entropy

linear_entropy(rhog)

Calculates the linear entropy of the input state.

rhog : ndarray

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 entropy;concurrence;negativity;purity;tangle See Also ------ err_functions;getProperties

negativity

negativity(rhog)

Calculates the negativity of the input state.

rhog : ndarray

Density Matrix of the desired state.
val : float

The calculated negativity. Other Properties entropy;linear_entropy;concurrence;purity;tangle See Also ------ err_functions;getProperties

purity

purity(rhog)

Calculates the purity of the input state.

rhog : ndarray

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 entropy;linear_entropy;negativity;concurrence;tangle See Also ------ err_functions;getProperties

partial_transpose

partial_transpose(rhog)

Returns the partial transpose of the input density matrix. DISCLAIMER : Tests in progress

Desc: Returns the partial transpose of the input density matrix. DISCLAIMER : Tests in progress
rhog : ndarray

Input arrays find the partial transpose of.

rv : ndarray

Partial transpose of rhog.

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 : ndarray 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.

random_pure_state

random_pure_state(N)

Returns a random quantum state from a uniform distribution across the space.

N : int

The dimension of the quantum state

pure_state : ndarray with length = 2^N

The random quantum state.

random_density_state

random_density_state(N)

Returns a random quantum density from an approximate uniform distribution across the space.

density : ndarray with shape = (2^N, 2^N)

The random quantum state.

random_bell_state

random_bell_state(N)

Randomly returns one of the 4 bell state. For 1 qubits on of the standard basis states is returned. For states with dimension greater then 2 the Greenberger-Horne-Zeilingerstate is returned with a random phase.

pure_state : ndarray with shape = (2^N, 2^N)

The random quantum state.

random_ginibre

random_ginibre(D)

Returns a random matrix from the Ginibre ensemble of size DxD. This is a complex matrix whos elements are a+ib | a,b iid. Norm(0,1)

mat : ndarray with shape = (2^N, 2^N)

The random matrix

densityOperation

densityOperation(D)

Performs the operation on the density matrix

psi : ndarray with shape = (2^nQubits, 2^nQubits)

The state to apply the operation to in density form.
gate : ndarray with shape = (2^nQubits, 2^nQubits)

The operation to apply to the state.

psi : ndarray with shape = (2^nQubits, 2^nQubits)

The state with the operation applied.

ketOperation

ketOperation(D)

Performs the operation on the pure state.

psi : 1darray with length = 2^nQubits

The state to apply the operation to in ket form.
gate : ndarray with shape = (2^nQubits, 2^nQubits)

The operation to apply to the state.

psi : ndarray with shape = (2^nQubits, 2^nQubits)

The state with the operation applied.

quarterWavePlate

quarterWavePlate(D)

returns the quantum gate associated with a quarter wave plate.

theta : float

The angle of the wave plate with respect to horizontal.

halfWavePlate

halfWavePlate(D)

returns the quantum gate associated with a half wave plate.

theta : float

The angle of the wave plate with respect to horizontal.

getWavePlateBasis

getWavePlateBasis(theta_qwp,theta_hwp,flipPBS)

Given the angles for the QWP and HWP plate find the measurement basis. PBS is assumed to transmit Horizontally polarized light and reflect vertical. This function does not take into account crosstalk.

theta_qwp : string

The angle with respect to horizontal for the quarter wave plate.
theta_hwp : string

The angle with respect to horizontal for the quarter wave plate. flipPBS: bool Set this to true to assume the PBS transmits V and reflects H

removeGlobalPhase

removeGlobalPhase(D)

Factors out the global phase of the given state by dividing the entire state by the phase of the first component.

pure_state : 1darray with length = 2^nQubits

The state in ket form.

psi : ndarray with shape = (2^nQubits, 2^nQubits)

The state with global phase factored out.

makeRhoImages

makeRhoImages(p, plt_given, customColor)

Creates matlab plots of the density matrix.

p : ndarray 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 See Also ------ saveRhoImages

saveRhoImages

saveRhoImages(p, pathToDirectory, customColor)

Creates and saves matlab plots of the density matrix.

p : ndarray with shape = (n, 2^numQubits, 2^numQubits)

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

Path to where you want your images to be saved. See Also ------ makeRhoImages

printLastOutput

printLastOutput(tomo,bounds)

Prints the properties of the last tomography to the console. Properties are defined in tomography conf settings. The calculated properties are determined by self.err_functions.

tomo : Tomography

The tomography object you want to see the output of.
bounds : int (optional)

The number of monte carlo runs you want to perform to get a better estimate of each property. Default will use whatever is set in the conf settings.

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. See Also ------ propertiesToHTML

propertiesToHTML

propertiesToHTML(vals)

Creates an HTML table based on the given property values.

vals : ndarray with shape = (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. See Also ------ matrixToHTML

stateToString

stateToString(vals)

Creates a string of the 1d pure state.

pure_state : 1darray with length = 2

The state in ket form.

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.setConfSetting

Tomography.setConfSetting(setting, val)

Parameters:

setting : string

val : ndarray, int, or string

Tomography.importConf

Tomography.importConf(conftxt)

Parameters:

conftxt : string

Tomography.importData

Tomography.importData(datatxt)

Parameters:

datatxt : string

Returns:

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

intensity : float

fvalp : float

Tomography.importEval

Tomography.importEval(evaltxt)

Parameters:

evaltxt : string

Returns:

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

intensity : float

fvalp : float

Tomography.StateTomography

Tomography.StateTomography(measurements, counts)

Parameters:

measurements : ndarray shape = ( Number of measurements , 2*NQubits )

counts : ndarray shape = (Number of measurements, NDetectors**NQubits )

crosstalk : ndarray shape = ( Number of measurements , 2**NQubits,2**NQubits ) (optional)

efficiency : 1darray with length = NQubits*NDetectors (optional)

time : 1darray with length = Number of measurements (optional)

singles : ndarray shape = ( Number of measurements , 2*NQubits ) (optional)

window : 1darray with length = NQubits*NDetectors (optional)

error : int (optional)

intensities : 1darray with length = Number of measurements (optional)

method : ['MLE','HMLE','BME','LINER'] (optional)

Returns:

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

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

fvalp : float

Tomography.StateTomography_Matrix

Tomography.StateTomography_Matrix(tomo_input, intensities)

Parameters:

tomo_input : ndarray

intensities : 1darray with length = number of measurements (optional)

method : ['MLE','HMLE','BME','LINER'] (optional)

Returns:

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

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

fvalp : float

Tomography.tomography_MLE

Tomography.tomography_MLE(starting_matrix, coincidences, measurements, accidentals,overall_norms)

Parameters:

starting_matrix : ndarray with shape = (2^numQubits, 2^numQubits)

coincidences : ndarray shape = (Number of measurements, NDetectors**NQubits)

measurements_densities : ndarray with shape = (NDetectors*number of measurements,2^numQubits, 2^numQubits)

accidentals : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

overall_norms : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

Returns:

rhog : ndarray with shape = (2^numQubits, 2^numQubits)

intensity : float

fvalp : float

Tomography.tomography_HMLE

Tomography.tomography_HMLE(starting_matrix, coincidences, measurements, accidentals,overall_norms)

Parameters:

starting_matrix : ndarray with shape = (2^numQubits, 2^numQubits)

coincidences : ndarray shape = (Number of measurements, NDetectors**NQubits)

measurements_densities : ndarray with shape = (NDetectors*number of measurements,2^numQubits, 2^numQubits)

accidentals : 1darray with length = number of measurements or length = number of measurements * 2^numQubits for 2 det/qubit

crosstalk : ndarray shape = ( Number of measurements , 2NQubits,2NQubits ) (optional)

crosstalk : ndarray shape = ( Number of measurements , 2NQubits,2NQubits ) (optional)