Saving Data and Figures#

Much of the data and plots computed with scqubits can easily be stored in memory, or exported and written to files. Data that we may want to save includes

  • computed eigenvalues and eigenvectors,

  • qubit parameters,

  • matrix elements,

  • parameter sweeps, and

  • plots,

to name a few. Further, scqubits makes it possible to write the configuration of a qubit or a HilbertSpace object to disk, and recreate the object instance later on. The following subsections illustrate usage the different usage cases.

Storing qubit spectral data in memory#

Consider an instance of a transmon qubit:

import numpy as np
import scqubits as scq

tmon = scq.Transmon(

As usual, we may obtain eigenvalues and eigenvectors via tmon.eigensys(), returned as two numpy arrays:

evals, evecs = tmon.eigensys(evals_count=4)
[-12.07703386  -6.39445819  -1.05664942   3.89594699]

Alternatively, we can choose to store the results in a SpectrumData object by setting the optional keyword argument return_spectrumdata:

specdata = tmon.eigensys(evals_count=4, return_spectrumdata=True)
< at 0x1e444838e88>

The relevant data is accessible through the attributes .energy_table, .state_table. The system parameters used to generate the data is stored alongside under .system_params.

array([-12.07703386,  -6.39445819,  -1.05664942,   3.89594699])
{'EJ': 15.0, 'EC': 0.3, 'ng': 0.0, 'ncut': 30, 'truncated_dim': None}

In a similar manner, matrix elements can be stored in a DataStore object, accessible via the attribute .matrixelem_table:

m_stored = tmon.matrixelement_table(operator='n_operator', return_datastore=True)
array([[-1.94289029e-16, -1.08780080e+00,  9.71445147e-17,
        -3.95057287e-02,  0.00000000e+00,  3.24570806e-03],
       [-1.08780080e+00,  5.55111512e-17,  1.49027312e+00,
        -2.22044605e-16, -8.66090220e-02, -4.16333634e-17],
       [ 9.71445147e-17,  1.49027312e+00, -5.55111512e-17,
         1.75524483e+00,  0.00000000e+00, -1.51360095e-01],
       [-3.95057287e-02, -2.22044605e-16,  1.75524483e+00,
        -1.66533454e-16, -1.92581364e+00,  5.55111512e-17],
       [ 0.00000000e+00, -8.66090220e-02,  0.00000000e+00,
        -1.92581364e+00, -2.22044605e-16,  1.95455333e+00],
       [ 3.24570806e-03, -5.55111512e-17, -1.51360095e-01,
         1.66533454e-16,  1.95455333e+00, -1.94289029e-16]])

Finally, scans of qubit spectral data over a set of parameter values are automatically returned in the form of SpectrumData objects. Again, data is accessible through the attributes mentioned above:

spec_scan = tmon.get_spectrum_vs_paramvals(param_name='ng', param_vals=np.linspace(0, 1, 10))
array([[-12.07703386,  -6.39445819,  -1.05664942,   3.89594699,
          8.34157217,  12.54032139],
       [-12.07703384,  -6.39445956,  -1.0566065 ,   3.89515784,
          8.35028668,  12.4619384 ]])

Writing qubit spectral data to disk, and reading back from file#

SpectrumData objects can easily be written to an .h5 file and read back from disk:

new_specdata ='test.h5')

array([-12.07703386,  -6.39445819,  -1.05664942,   3.89594699])

Note: as a convenient shortcut for directly writing to file, many methods accept filename as an optional argument:

tmon.eigensys(evals_count=4, filename='test.h5')

Writing a qubit instance to disk, and recreating it from file#

Once a qubit is initialized, it may be useful to save it to file and recreate it later. This is possible for all qubit classes, and stores all circuit parameters and other metadata to create an identical copy of the current qubit configuration.

For the Transmon instance used above, this works as follows:


new_tmon ='my_tmon.h5')

Indeed, new_tmon is now a new Transmon instance with the same parameters as tmon:

Transmon(**{'EJ': 15.0, 'EC': 0.3, 'ng': 0.0, 'ncut': 30, 'truncated_dim': None})

Summary of classes enabling file input/output#

A variety of class instances can be saved to disk via


and read back by<filename>)

when using the .h5 file format. (CSV file format only supports a small subset of the functionality.)

The following table lists the classes that support file input and output,




storage of eigenvalues, eigenvectors, and relevant metadata


storage of matrix elements or coherence data (and general purpose storage)


stores data needed for recreating object instance


stores data needed for recreating object instance


stores data needed for recreating object instance


stores data needed for recreating object instance


stores data needed for recreating object instance


stores data needed for recreating object instance


stores data needed for recreating object instance


stores data needed for recreating object instance

Data stored to .h5 files includes information about the type of object stored. Upon reading, the same type of object is recreated and returned by the read(...) function.

ParameterSweep forms an exception: the returned object is of type StoredSweep rather than ParameterSweep. It can be called to perform spectrum lookups, produce plots from sweep spectral data etc., but it does not support updates with new parameters. For that, a new ParameterSweep object can be created by using

<StoredSweep>.new_sweep(subsys_update_list, update_hilbertspace)

Exporting plots to file#

All plotting routines in scqubits can be called with a keyword argument filename='output.pdf'. This will generate the plot, display it, and in addition write it to the specified file. The output format is determined by the file suffix provided in the filename. A variety of output formats is supported via Matplotlib, including pdf, jpg, png, svg.