Tutorial 1: Getting Started with SQuADDS#

In this tutorial, we will walk you through some basic usage of SQuADDS. By the end of this tutorial, you will be able to:

Have an HuggingFace account
Access the SQuADDS Database
Use the SQuADDS API to query for closest and “best-guess” interpolated device designs for your chosen Hamiltonian parameters
Simulate the “best-guess” design using an EM solver tool

[1]:

%load_ext autoreload
%autoreload 2

Since the SQuADDS Database is hosted on HuggingFace, we will need to create an account and get an API key to access the database.

HuggingFace 🤗#

HuggingFace is a company that provides a large number of NLP models and datasets. They also provide a platform to host your own models and datasets.

Creating an Account#

Follow the instructions here - HuggingFace: Sign Up - to create an account.

Once you have created an account, you can get your API key from the settings page.

Please update the HUGGINGFACE_API_KEY variable in the .env file with your API key OR execute the following code to set the environment variable.

[ ]:

from squadds.core.utils import set_huggingface_api_key

set_huggingface_api_key()

Accessing the SQuADDS Database using the HuggingFace API#

The SQuADDS Database is hosted on HuggingFace. You can access the database using the datasets library from HuggingFace.

[2]:

from datasets import get_dataset_config_names
from datasets import load_dataset

configs = get_dataset_config_names("SQuADDS/SQuADDS_DB")

You can navigate the database using HuggingFace API. For example, you can access the qubit database using the following code:

[3]:

qubit_data = load_dataset("SQuADDS/SQuADDS_DB", configs[0])

[4]:

qubit_data

[4]:

DatasetDict({
    train: Dataset({
        features: ['sim_options', 'sim_results', 'notes', 'design', 'contributor'],
        num_rows: 1934
    })
})

Each configuration in the dataset is uniquely identified by their config. For the SQuADDS Database, the config string is created in the following format:

config = f"{component}_{component_name}_{data_type}"

where component is the name of the component, component_name is the name of the component (in Qiskit Metal), and data_type is the type of simulation data that has been contributed.

Lets check what the config string looks like for our database:

[5]:

components = []
component_names = []
data_types = []

for config in configs:
    try:
        components.append(config.split("-")[0])
        component_names.append(config.split("-")[1])
        data_types.append(config.split("-")[2])
    except:
        pass

print(components)
print(component_names)
print(data_types)

['qubit', 'cavity_claw', 'coupler', 'coupler', 'measured_device_database']
['TransmonCross', 'RouteMeander', 'NCap', 'CapNInterdigitalTee']
['cap_matrix', 'eigenmode', 'cap_matrix', 'cap_matrix']

The reason we added the try except block is because we have more datasets as well in the database that don’t conform the simulation data format

Using the SQuADDS API to access and anlyze the database#

While it is possible to directly access the SQuADDS Database using the datasets library, we have created a simple API to make it easier to query the database.

The main object we use to access the database is the SQuADDS_DB class.

[1]:

from squadds import SQuADDS_DB

db = SQuADDS_DB()

You can get a summary of the datasets by running.

[7]:

db.view_datasets()

+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| Component   | Component Name      | Data Available   | Component Image                                                                            |
+=============+=====================+==================+============================================================================================+
| qubit       | TransmonCross       | cap_matrix       | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/TransmonCross.png       |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| qubit       | TransmonCross       | cap_matrix       | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/TransmonCross.png       |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| cavity_claw | RouteMeander        | eigenmode        | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/RouteMeander.png        |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| cavity_claw | RouteMeander        | eigenmode        | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/RouteMeander.png        |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| coupler     | NCap                | cap_matrix       | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/NCap.png                |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| coupler     | NCap                | cap_matrix       | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/NCap.png                |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| coupler     | CapNInterdigitalTee | cap_matrix       | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/CapNInterdigitalTee.png |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+
| coupler     | CapNInterdigitalTee | cap_matrix       | https://github.com/LFL-Lab/SQuADDS/tree/master/docs/_static/images/CapNInterdigitalTee.png |
+-------------+---------------------+------------------+--------------------------------------------------------------------------------------------+

To check for the config names

[8]:

db.get_configs()

['qubit-TransmonCross-cap_matrix',
 'cavity_claw-RouteMeander-eigenmode',
 'coupler-NCap-cap_matrix',
 'coupler-CapNInterdigitalTee-cap_matrix']

NOTE: ``’coupler-NCap-cap_matrix’`` and ``’’coupler-CapNInterdigitalTee-cap_matrix’`` are the same datasets. We will support them both since future releases will deprecate the term ``NCap`` and replace it with ``CapNInterdigitalTee``.

If you are interested to learn more about each configuration, you can do so by using the get_dataset_info method.

[9]:

db.get_dataset_info(component="qubit", component_name="TransmonCross", data_type="cap_matrix")

================================================================================
Dataset Features:
{'contributor': {'PI': Value(dtype='string', id=None),
                 'date_created': Value(dtype='string', id=None),
                 'group': Value(dtype='string', id=None),
                 'institution': Value(dtype='string', id=None),
                 'uploader': Value(dtype='string', id=None)},
 'design': {'design_options': {...},
            'design_tool': Value(dtype='string', id=None)},
 'notes': {},
 'sim_options': {'renderer_options': {...},
                 'setup': {...},
                 'simulator': Value(dtype='string', id=None)},
 'sim_results': {'claw_to_claw': Value(dtype='float64', id=None),
                 'claw_to_ground': Value(dtype='float64', id=None),
                 'cross_to_claw': Value(dtype='float64', id=None),
                 'cross_to_cross': Value(dtype='float64', id=None),
                 'cross_to_ground': Value(dtype='float64', id=None),
                 'ground_to_ground': Value(dtype='float64', id=None),
                 'units': Value(dtype='string', id=None)}}

Dataset Description:


Dataset Citation:


Dataset Homepage:


Dataset License:


Dataset Size in Bytes:
9735651
================================================================================

[10]:

db.get_dataset_info(component="cavity_claw", component_name="RouteMeander", data_type="eigenmode")

================================================================================
Dataset Features:
{'contributor': {'PI': Value(dtype='string', id=None),
                 'date_created': Value(dtype='string', id=None),
                 'group': Value(dtype='string', id=None),
                 'institution': Value(dtype='string', id=None),
                 'misc': Value(dtype='string', id=None),
                 'uploader': Value(dtype='string', id=None)},
 'design': {'coupler_type': Value(dtype='string', id=None),
            'design_options': {...},
            'design_tool': Value(dtype='string', id=None),
            'resonator_type': Value(dtype='string', id=None)},
 'notes': {},
 'sim_options': {'renderer_options': {...},
                 'setup': {...},
                 'simulator': Value(dtype='string', id=None)},
 'sim_results': {'cavity_frequency': Value(dtype='float64', id=None),
                 'kappa': Value(dtype='float64', id=None),
                 'units': Value(dtype='string', id=None)}}

Dataset Description:


Dataset Citation:


Dataset Homepage:


Dataset License:


Dataset Size in Bytes:
4620074
================================================================================

You can get the entire dataset as a Pandas DataFrame by using the get_dataset method.

[11]:

db.see_dataset(component="qubit", component_name="TransmonCross", data_type="cap_matrix")

[11]:

	renderer_options	setup	simulator	claw_to_claw	claw_to_ground	cross_to_claw	cross_to_cross	cross_to_ground	ground_to_ground	units	design_options	design_tool	PI	date_created	group	institution	uploader
0	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	94.97421	90.86585	3.73363	158.40783	158.40783	311.25590	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
1	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	82.44280	79.19378	2.93820	188.15089	188.15089	333.52997	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-10-25-153123	LFL	USC	Andre Kuo
2	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	83.76412	80.18130	3.16131	104.35340	104.35340	237.02548	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
3	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	103.37057	97.22405	5.77590	174.13928	174.13928	335.31609	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-10-25-153126	LFL	USC	Andre Kuo
4	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	68.92854	65.68607	2.87375	120.03923	120.03923	240.34085	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1929	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	106.43025	101.53197	4.45645	174.46380	174.46380	340.62919	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
1930	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	121.10943	112.62570	7.95178	187.43537	187.43537	367.34003	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142549	LFL	USC	Andre Kuo
1931	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	144.56289	136.36810	7.65968	172.14561	172.14561	372.39970	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-10-25-153123	LFL	USC	Andre Kuo
1932	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	68.76413	65.78116	2.48795	56.75230	56.75230	166.57383	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
1933	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	58.45749	55.50796	2.54396	62.01000	62.01000	162.42140	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142549	LFL	USC	Andre Kuo

1934 rows × 17 columns

You can also learn more about the measured device that generated this dataset:

[12]:

db.view_reference_device_of(component="qubit", component_name="TransmonCross", data_type="cap_matrix")

+--------------------+------------------------------------------------------------------------------------+
| Design Code        | https://github.com/LFL-Lab/design_schema_WM1                                       |
+--------------------+------------------------------------------------------------------------------------+
| Paper Link         | https://arxiv.org/pdf/2312.13483                                                   |
+--------------------+------------------------------------------------------------------------------------+
| Image              | https://github.com/LFL-Lab/design_schema_WM1/blob/main/assets/WM1_qiskit_metal.png |
+--------------------+------------------------------------------------------------------------------------+
| Foundry            | SQUILL                                                                             |
+--------------------+------------------------------------------------------------------------------------+
| Fabrication Recipe | Confidential                                                                       |
+--------------------+------------------------------------------------------------------------------------+
| PI                 | Eli Levenson-Falk, PhD                                                             |
+--------------------+------------------------------------------------------------------------------------+
| date_created       | 2023-09-20-142547                                                                  |
+--------------------+------------------------------------------------------------------------------------+
| group              | LFL                                                                                |
+--------------------+------------------------------------------------------------------------------------+
| institution        | USC                                                                                |
+--------------------+------------------------------------------------------------------------------------+
| measured_by        | ['Sadman Ahmed Shanto', 'Malinda Hecht', 'Evangelos Vlachos']                      |
+--------------------+------------------------------------------------------------------------------------+
| name               | WM1                                                                                |
+--------------------+------------------------------------------------------------------------------------+
| uploader           | Elizabeth Kunz                                                                     |
+--------------------+------------------------------------------------------------------------------------+

You can also learn more about the other measured devices that are available in SQuADDS

[2]:

db.get_measured_devices()

[2]:

	Name	Design Code	Paper Link	Image	Foundry	Substrate	Materials	Junction Style	Junction Materials
0	WM1	https://github.com/LFL-Lab/design_schema_WM1	https://arxiv.org/pdf/2312.13483	https://github.com/LFL-Lab/design_schema_WM1/b...	SQUILL	Silicon	Aluminium	Dolan	Al-AlOx-Al
1	“dissipator”	https://github.com/LFL-Lab/design_schema_dissi...	https://journals.aps.org/prxquantum/abstract/1...	https://github.com/LFL-Lab/design_schema_dissi...	USC Nanofab	Silicon 111	Aluminium	Manhattan	Al-AlOx-Al
2	MUNNIN	https://github.com/LFL-Lab/design_schema_MUNNIN	https://journals.aps.org/prl/abstract/10.1103/...	https://github.com/LFL-Lab/design_schema_MUNNI...	SQUILL	Silicon	Aluminium	Dolan	Al-AlOx-Al

If you want to learn about the who contributed the simulation data, you can use the following methods:

[14]:

db.view_contributors_of("qubit", "TransmonCross", "cap_matrix")

================================================================================
                        Measured Device Contributor(s):
================================================================================
+-----------------------+-------------------------------------------------------+
| Foundry               | N/A                                                   |
+-----------------------+-------------------------------------------------------+
| PI                    | Eli Levenson-Falk, PhD                                |
+-----------------------+-------------------------------------------------------+
| Group                 | LFL                                                   |
+-----------------------+-------------------------------------------------------+
| Institution           | USC                                                   |
+-----------------------+-------------------------------------------------------+
| Measured By           | Sadman Ahmed Shanto, Malinda Hecht, Evangelos Vlachos |
+-----------------------+-------------------------------------------------------+
| Reference Device Name | WM1                                                   |
+-----------------------+-------------------------------------------------------+
| Uploader              | Elizabeth Kunz                                        |
+-----------------------+-------------------------------------------------------+
================================================================================
                        Simulation Data Contributor(s):
================================================================================
+------------+------------------------+---------+---------------+
| uploader   | PI                     | group   | institution   |
+============+========================+=========+===============+
| Andre Kuo  | Eli Levenson-Falk, PhD | LFL     | USC           |
+------------+------------------------+---------+---------------+

To see the list of all the contributors, you can use the following method:

[15]:

db.view_all_contributors()

+--------------------+------------------------------------+---------------------------------------+
| Name               | Institution                        | Contribution                          |
+====================+====================================+=======================================+
| Clark Miyamoto     | New York University                | Code contributor 💻                   |
+--------------------+------------------------------------+---------------------------------------+
| Madison Howard     | California Institute of Technology | Bug Hunter 🐛                         |
+--------------------+------------------------------------+---------------------------------------+
| Malida Hecht       | University of Southern California  | Data contributor 📀                   |
+--------------------+------------------------------------+---------------------------------------+
| Evangelos Vlachos  | University of Southern California  | Code contributor 💻 and Bug Hunter 🐛 |
+--------------------+------------------------------------+---------------------------------------+
| Anne Whelan        | US Navy                            | Documentation contributor 📄          |
+--------------------+------------------------------------+---------------------------------------+
| Jenny Huang        | Columbia University                | Documentation contributor 📄          |
+--------------------+------------------------------------+---------------------------------------+
| Connie Miao        | Stanford University                | Data Contributor 📀                   |
+--------------------+------------------------------------+---------------------------------------+
| Daria Kowsari, PhD | University of Southern California  | Data contributor 📀                   |
+--------------------+------------------------------------+---------------------------------------+
| Vivek Maurya       | University of Southern California  | Data contributor 📀                   |
+--------------------+------------------------------------+---------------------------------------+
| Haimeng Zhang, PhD | IBM                                | Data contributor 📀                   |
+--------------------+------------------------------------+---------------------------------------+
| Ethan Zheng        | University of Southern California  | Data contributor 📀                   |
+--------------------+------------------------------------+---------------------------------------+
| Sara Sussman, PhD  | Fermilab                           | Bug Hunter 🐛                         |
+--------------------+------------------------------------+---------------------------------------+

As the SQuADDS_DB dataset updates, so will all the information we have queried automatically.

Making Systems out of Circuit QED Elements#

One of the main use cases of the SQuADDS project is to get the design space parameters for systems of our interest using our desired Hamiltonian parameters.

Using the SQuADDS API, we can “build” a system by choosing the circuit QED components we want to use.

The following subsections walks you through some examples.

Querying for the a target qubit design#

Let’s say you know the Hamiltonian parameters of a qubit you want to use. You can use the SQuADDS API to query for the closest design to your target qubit.

We first need to select our sytem of interest.

[16]:

db.select_system("qubit")

Now, we need to specify to SQuADDS what type of qubit our system is. We can do this by using the select_qubit method.

[17]:

db.select_qubit("TransmonCross")

We now create the system dataframe so that we can query for the design parameters we are interested in.

[18]:

df = db.create_system_df()
df

[18]:

	renderer_options	setup	simulator	claw_to_claw	claw_to_ground	cross_to_claw	cross_to_cross	cross_to_ground	ground_to_ground	units	design_options	design_tool	PI	date_created	group	institution	uploader
0	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	94.97421	90.86585	3.73363	158.40783	158.40783	311.25590	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
1	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	82.44280	79.19378	2.93820	188.15089	188.15089	333.52997	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-10-25-153123	LFL	USC	Andre Kuo
2	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	83.76412	80.18130	3.16131	104.35340	104.35340	237.02548	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
3	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	103.37057	97.22405	5.77590	174.13928	174.13928	335.31609	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-10-25-153126	LFL	USC	Andre Kuo
4	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	68.92854	65.68607	2.87375	120.03923	120.03923	240.34085	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1929	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	106.43025	101.53197	4.45645	174.46380	174.46380	340.62919	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
1930	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	121.10943	112.62570	7.95178	187.43537	187.43537	367.34003	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142549	LFL	USC	Andre Kuo
1931	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	144.56289	136.36810	7.65968	172.14561	172.14561	372.39970	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-10-25-153123	LFL	USC	Andre Kuo
1932	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	68.76413	65.78116	2.48795	56.75230	56.75230	166.57383	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo
1933	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	58.45749	55.50796	2.54396	62.01000	62.01000	162.42140	fF	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142549	LFL	USC	Andre Kuo

1934 rows × 17 columns

Now that we have created our system dataframe, we can query for the closest design to our target qubit parameters. To do this we need to call the Analyzer object.

[19]:

from squadds import Analyzer

We instatantaite the Analyzer object by passing in the SQuADDS_DB instance we created earlier.

[20]:

analyzer = Analyzer(db)

We can now check for what type of Hamiltonian parameters are available for our chosen system

[21]:

analyzer.target_param_keys()

[21]:

['qubit_frequency_GHz', 'anharmonicity_MHz']

Now, Let’s select a geometry which results in the closest qubit characteristics

Call Analyzer.find_closest

Documentation:

Finds the rows in the DataFrame with the closest matching characteristics
to the given target parameters using a specified metric.

Args:
    target_params (dict): A dictionary containing the target values for columns in `self.df`.
                          Keys are column names and values are the target values.
    num_top (int): The number of closest matching rows to return.
    metric (str, optional): The distance metric to use for finding the closest matches.
                            Available options are specified in `self.__supported_metrics__`.
                            Defaults to 'Euclidean'.
    display (bool, optional): Whether to display warnings and logs. Defaults to True.

Returns:
    pd.DataFrame: A DataFrame containing the rows with the closest matching characteristics,
                  sorted by the distance metric.

Raises:
    ValueError: If the specified metric is not supported or `num_top` exceeds the DataFrame size.

You are given the choice of the following metrics.

[22]:

analyzer.__supported_metrics__

[22]:

['Euclidean', 'Manhattan', 'Chebyshev', 'Weighted Euclidean', 'Custom']

Define your Hamiltonian parameters that you want to use for your qubit

[23]:

target_params={"qubit_frequency_GHz": 4, "anharmonicity_MHz": -200}

[24]:

results = analyzer.find_closest(target_params=target_params,
                                       num_top=3,
                                       metric="Euclidean",
                                       display=True)
results

[24]:

	renderer_options	setup	simulator	claw_to_claw	claw_to_ground	cross_to_claw	cross_to_cross	cross_to_ground	ground_to_ground	units	...	design_tool	PI	date_created	group	institution	uploader	EC	EJ	qubit_frequency_GHz	anharmonicity_MHz
643	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	106.91739	101.13161	5.25204	102.49025	102.49025	255.94708	fF	...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo	0.179783	12.278081	4.013772	-201.551532
1862	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	80.01554	76.72741	2.89095	104.64079	104.64079	233.88902	fF	...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo	0.180135	12.278081	4.017505	-201.973598
1714	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	76.27207	73.26136	2.62986	104.89818	104.89818	230.69451	fF	...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo	0.180141	12.278081	4.017570	-201.981031

3 rows × 21 columns

Thats it! You have now found some designs for your qubit that are closest to your target Hamiltonian parameters.

Using Custom Metrics#

To use a custom metric first define the function. For example, lets say we want the manhattan metric

[25]:

def manhattan_distance(target, simulated):
    return sum(abs(target[key] - simulated.get(key, 0)) for key in target)

[26]:

analyzer.custom_metric_func = manhattan_distance

[27]:

analyzer.find_closest(target_params=target_params,
                                            num_top=1,
                                            metric="Custom",
                                            display=True)

Either `skip_df_gen` flag is set to True or all target params have been precomputed at an earlier step. Using `df` from memory.
Please set this to False if `target_parameters` have changed.

[27]:

	renderer_options	setup	simulator	claw_to_claw	claw_to_ground	cross_to_claw	cross_to_cross	cross_to_ground	ground_to_ground	units	...	design_tool	PI	date_created	group	institution	uploader	EC	EJ	qubit_frequency_GHz	anharmonicity_MHz
643	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	106.91739	101.13161	5.25204	102.49025	102.49025	255.94708	fF	...	qiskit-metal	Eli Levenson-Falk, PhD	2023-09-20-142547	LFL	USC	Andre Kuo	0.179783	12.278081	4.013772	-201.551532

1 rows × 21 columns

[28]:

best_options = results.iloc[0]["design_options"]
best_options

[28]:

{'aedt_hfss_capacitance': 0,
 'aedt_hfss_inductance': 9.686e-09,
 'aedt_q3d_capacitance': 0,
 'aedt_q3d_inductance': 1e-08,
 'chip': 'main',
 'connection_pads': {'readout': {'claw_cpw_length': '40um',
   'claw_cpw_width': '10um',
   'claw_gap': '5.1um',
   'claw_length': '190um',
   'claw_width': '15um',
   'connector_location': '90',
   'connector_type': '0',
   'ground_spacing': '10um'}},
 'cross_gap': '30um',
 'cross_length': '210um',
 'cross_width': '30um',
 'gds_cell_name': 'my_other_junction',
 'hfss_capacitance': 0,
 'hfss_inductance': 9.686e-09,
 'hfss_mesh_kw_jj': 7e-06,
 'hfss_resistance': 0,
 'layer': '1',
 'orientation': '-90',
 'pos_x': '-1500um',
 'pos_y': '1200um',
 'q3d_capacitance': 0,
 'q3d_inductance': '10nH',
 'q3d_mesh_kw_jj': 7e-06,
 'q3d_resistance': 0}

You can pass in the design_options from the closest design to the options argument of your selected qubit and render it in qiskit metal.

[29]:

# Qiskit Metal imports
import qiskit_metal as metal
from qiskit_metal import designs, draw
from qiskit_metal import MetalGUI, Dict
from qiskit_metal.designs.design_multiplanar import MultiPlanar

from qiskit_metal.qlibrary.qubits.transmon_cross import TransmonCross
from qiskit_metal.qlibrary.couplers.coupled_line_tee import CoupledLineTee
from qiskit_metal.qlibrary.tlines.meandered import RouteMeander
from qiskit_metal.qlibrary.core import QRoute, QRoutePoint

[30]:

design = MultiPlanar(metadata={},
                     overwrite_enabled=True)
gui = MetalGUI(design)

01:14PM 50s CRITICAL [_qt_message_handler]: line: 0, func: None(), file: None  WARNING: Populating font family aliases took 371 ms. Replace uses of missing font family "Courier" with one that exists to avoid this cost.

[31]:

from qiskit_metal.qlibrary.qubits.transmon_cross import TransmonCross

TransmonCross(design, "transmon", options=best_options)

gui.rebuild()
gui.zoom_on_components(['transmon'])
gui.screenshot("qubit_only.png")

../../_images/source_tutorials_Tutorial-1_Getting_Started_with_SQuADDS_63_0.png

Querying for a target cavity design#

The same workflow can be used to query for a target cavity design.

While it is not necessary, it may be a good idea to unselect_all() before creating a new system.

[32]:

db.unselect_all()

Proceed with selecting the system of interest

[33]:

db.select_system("cavity_claw")

[34]:

db.select_cavity_claw("RouteMeander")

[35]:

db.select_resonator_type("quarter")

It’s always a good idea to check that the system you have selected is correct.

[36]:

db.show_selections()

Selected component:  cavity_claw
Selected component name:  RouteMeander
Selected data type:  eigenmode
Selected system:  cavity_claw
Selected coupler:  CLT
Selected resonator type:  quarter

Great! lets create the system dataframe and analyze it.

[37]:

df = db.create_system_df()

[38]:

analyzer = Analyzer(db)

[39]:

analyzer.target_param_keys()

[39]:

['resonator_type', 'cavity_frequency_GHz', 'kappa_kHz']

Select the Hamiltonian parameters you want to use for your cavity and search for the closest designs.

[40]:

target_params = {"cavity_frequency_GHz": 6.9,
                "kappa_kHz": 120,
                "resonator_type":"quarter"}

[41]:

results = analyzer.find_closest(target_params=target_params,
                                       num_top=5,
                                       metric="Euclidean",
                                       display=True)
results

[41]:

	renderer_options	setup	simulator	cavity_frequency_GHz	kappa_kHz	coupler_type	design_options	design_tool	resonator_type	PI	date_created	group	institution	misc	uploader
1191	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	6.948003	122.218952	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng
1192	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	6.915761	125.765213	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng
1189	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	7.013518	127.327511	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng
190	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	6.371017	121.824395	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2023-11-30-214122	LFL	USC	None	Andre Kuo
1190	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	6.980933	129.751169	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng

Lets say we want to use the “Weighted Euclidean” metric to find the closest design to our target cavity parameters.

Weighted Euclidean Metric#

You can do a weighted Euclidean metric instead.

\[F(\{P_i\},\{p_i\}) = \sum_i w_i\frac{(P_i - p_i)^2}{P_i^2}\]

Here ( w_i ) are weights which default to 1 if not user-defined.

Note: The default metric for find_closest is Euclidean when not user-defined.

[42]:

# Set up the weights
analyzer.metric_weights = {"cavity_frequency_GHz": 2, "kappa_kHz": 1}

[43]:

results = analyzer.find_closest(target_params=target_params,
                                       num_top=3,
                                       metric="Weighted Euclidean",
                                       display=True)
results

[43]:

	renderer_options	setup	simulator	cavity_frequency_GHz	kappa_kHz	coupler_type	design_options	design_tool	resonator_type	PI	date_created	group	institution	misc	uploader
1191	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	6.948003	122.218952	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng
1192	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	6.915761	125.765213	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng
1189	None	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	Ansys HFSS	7.013518	127.327511	CLT	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng

Querying for a target qubit-cavity design#

Again, we follow the same procedure as before.

[44]:

db.select_system(["qubit","cavity_claw"])

[45]:

db.select_qubit("TransmonCross")
db.select_cavity_claw("RouteMeander")
db.select_resonator_type("quarter")

[46]:

db.show_selections()

Selected qubit:  TransmonCross
Selected cavity:  RouteMeander
Selected coupler to feedline:  CLT
Selected resonator type:  quarter
Selected system:  ['qubit', 'cavity_claw']

[47]:

merged_df = db.create_system_df()

[48]:

merged_df

[48]:

	index_qc	renderer_options_qubit	setup_qubit	simulator_qubit	claw_to_claw	claw_to_ground	cross_to_claw	cross_to_cross	cross_to_ground	ground_to_ground	...	design_options_cavity_claw	design_tool_cavity_claw	resonator_type	PI_cavity_claw	date_created_cavity_claw	group_cavity_claw	institution_cavity_claw	misc	uploader_cavity_claw	design_options
0	0	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	94.97421	90.86585	3.73363	158.40783	158.40783	311.25590	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2023-12-09-204334	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...
1	0	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	94.97421	90.86585	3.73363	158.40783	158.40783	311.25590	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2023-12-06-224829	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...
2	0	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	94.97421	90.86585	3.73363	158.40783	158.40783	311.25590	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2023-12-04-124953	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...
3	0	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	94.97421	90.86585	3.73363	158.40783	158.40783	311.25590	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2023-12-08-173545	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...
4	0	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	94.97421	90.86585	3.73363	158.40783	158.40783	311.25590	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2023-11-30-214122	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
22191	1642	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	183.80802	168.04023	15.11184	214.45993	214.45993	454.60312	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-01-25-214631	LFL	USC		Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...
22192	1642	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	183.80802	168.04023	15.11184	214.45993	214.45993	454.60312	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-12-141659	LFL	USC	None	Ethan Zheng	{'cavity_claw_options': {'coupler_type': 'CLT'...
22193	1642	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	183.80802	168.04023	15.11184	214.45993	214.45993	454.60312	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng	{'cavity_claw_options': {'coupler_type': 'CLT'...
22194	1642	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	183.80802	168.04023	15.11184	214.45993	214.45993	454.60312	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-24-230000	LFL	USC	None	Ethan Zheng	{'cavity_claw_options': {'coupler_type': 'CLT'...
22195	1642	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	183.80802	168.04023	15.11184	214.45993	214.45993	454.60312	...	{'claw_opts': {'connection_pads': {'readout': ...	qiskit-metal	quarter	Eli Levenson-Falk, PhD	2024-07-29-154147	LFL	USC	None	Ethan Zheng	{'cavity_claw_options': {'coupler_type': 'CLT'...

22196 rows × 36 columns

Pass the SQuADDS_DB instance to the Analyzer object.

[49]:

analyzer = Analyzer(db)

Always good to check whether the system you have selected is correct.

[50]:

db.selected_system

[50]:

['qubit', 'cavity_claw']

[51]:

analyzer.selected_system

[51]:

['qubit', 'cavity_claw']

Define the target_params for your qubit-cavity system.

[52]:

target_params = {
                "qubit_frequency_GHz": 4,
                "cavity_frequency_GHz": 6.2,
                "kappa_kHz": 120,
                "resonator_type":"quarter",
                "anharmonicity_MHz": -200,
                "g_MHz": 70}

[53]:

results = analyzer.find_closest(target_params=target_params,
                                       num_top=3,
                                       metric="Euclidean",
                                       display=True)
results

Time taken to add the coupled H params: 4.033527135848999 seconds

[53]:

	index_qc	renderer_options_qubit	setup_qubit	simulator_qubit	claw_to_claw	claw_to_ground	cross_to_claw	cross_to_cross	cross_to_ground	ground_to_ground	...	group_cavity_claw	institution_cavity_claw	misc	uploader_cavity_claw	design_options	EC	EJ	qubit_frequency_GHz	anharmonicity_MHz	g_MHz
17659	1441	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	113.99245	107.65111	5.75841	112.70740	112.70740	274.49373	...	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...	0.163509	12.278081	3.836546	-182.146843	68.095121
3022	812	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	105.76081	99.80185	5.38260	100.41444	100.41444	251.82560	...	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...	0.183089	12.278081	4.048670	-205.518797	70.226899
12116	1180	{'Cj': 0, 'Lj': '10nH', '_Rj': 0, 'design_name...	{'auto_increase_solution_order': True, 'enable...	Ansys HFSS	109.80541	103.57639	5.68548	105.83609	105.83609	261.84982	...	LFL	USC	None	Andre Kuo	{'cavity_claw_options': {'coupler_type': 'CLT'...	0.173690	12.278081	3.948506	-194.262295	70.978895

3 rows × 41 columns

Awesome! we have some designs for our qubit-cavity system. To see where the closest design lies in the Hamiltonian parameter space, we can use the closest_design_in_H_space method.

[54]:

%matplotlib inline

[55]:

analyzer.closest_design_in_H_space()

../../_images/source_tutorials_Tutorial-1_Getting_Started_with_SQuADDS_99_0.svg

Similarly, we can query for the best-guess design for our qubit-cavity system with a half-wave resonator.

Lets start by selecting the system of interest and creating the system dataframe.

[56]:

db.unselect_all()
db.select_system(["qubit", "cavity_claw"])
db.select_qubit("TransmonCross")
db.select_cavity_claw("RouteMeander")
db.select_resonator_type("half")
db.show_selections()
half_df = db.create_system_df()

Selected qubit:  TransmonCross
Selected cavity:  RouteMeander
Selected coupler to feedline:  NCap
Selected resonator type:  half
Selected system:  ['qubit', 'cavity_claw']

Now, lets pass on our new system info to the Analyzer object. We can either re-instantiate the Analyzer object like before.

analyzer_hdf = Analyzer(db)

Or we can just update the analyzer object with the new system info using the following method.

[57]:

analyzer.reload_db()

Doing a sanity check to see if the system we have selected is correct.

[58]:

analyzer.db.show_selections()

Selected qubit:  TransmonCross
Selected cavity:  RouteMeander
Selected coupler to feedline:  NCap
Selected resonator type:  half
Selected system:  ['qubit', 'cavity_claw']

Great! Now lets query for the best-guess design for our system.

[59]:

target_params = {
                "qubit_frequency_GHz": 4,
                "cavity_frequency_GHz": 9.2,
                "kappa_kHz": 80,
                "resonator_type":"half",
                "anharmonicity_MHz": -200,
                "g_MHz": 70}

results = analyzer.find_closest(target_params=target_params, num_top=1, metric="Euclidean", parallel=True, num_cpu="auto")

Using 10 chunks for parallel processing

WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336
WARNING:py.warnings:NumbaPerformanceWarning: 
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.

File "../squadds/calcs/transmon_cross.py", line 48:
@jit(nopython=True, parallel=True)
def g_from_cap_matrix_numba(C, C_c, EJ, f_r, res_type, Z0=50):
^

 /Users/shanto/miniconda3/envs/qiskit-metal-env/lib/python3.11/site-packages/numba/core/typed_passes.py: 336

Time taken to add the coupled H params: 391.506459236145 seconds
Using 10 CPUs for parallel processing

WARNING:py.warnings:SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
 /Users/shanto/LFL/SQuADDS/SQuADDS/squadds/core/analysis.py: 432

Note: We had used the flags - parallel=True and num_cpu="auto" to speed up the process since we have very fine coverage in this dataset.

[60]:

%matplotlib inline

[61]:

analyzer.closest_design_in_H_space()

../../_images/source_tutorials_Tutorial-1_Getting_Started_with_SQuADDS_110_0.svg

Interpolation of Best Designs#

Even though the closest_design will become better as more validated pre-simulated points are added to the database, it is still a good idea to interpolate to get the best designs.

We use the physics inspired interpolation algorithm described in our paper - ScalingInterpolator class to interpolate the best designs.

[62]:

from squadds.interpolations.physics import ScalingInterpolator

We pass the Analzyer object and the target_params dict to the ScalingInterpolator class.

[63]:

# Create an instance of ScalingInterpolator
interpolator = ScalingInterpolator(analyzer, target_params)

design_df = interpolator.get_design()

Either `skip_df_gen` flag is set to True or all target params have been precomputed at an earlier step. Using `df` from memory.
Please set this to False if `target_parameters` have changed.
Using 10 CPUs for parallel processing

WARNING:py.warnings:SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
 /Users/shanto/LFL/SQuADDS/SQuADDS/squadds/core/analysis.py: 432

Either `skip_df_gen` flag is set to True or all target params have been precomputed at an earlier step. Using `df` from memory.
Please set this to False if `target_parameters` have changed.
Using 10 CPUs for parallel processing

WARNING:py.warnings:SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
 /Users/shanto/LFL/SQuADDS/SQuADDS/squadds/core/analysis.py: 432

==================================================
Kappa scaling: 0.9974902538805511
g scaling: 1.000220775604248
alpha scaling: 1.0098679065704346
resonator scaling: 0.9813371948573901
==================================================

The design_df contains the various design_options for the best designs and also the sim_options needed to simulate them.

[64]:

design_df

[64]:

	coupler_type	design_options_qubit	design_options_cavity_claw	setup_qubit	setup_cavity_claw	design_options
0	NCap	{'aedt_hfss_capacitance': 0, 'aedt_hfss_induct...	{'claw_opts': {'connection_pads': {'readout': ...	{'auto_increase_solution_order': True, 'enable...	{'basis_order': 1, 'max_delta_f': 0.05, 'max_p...	{'cavity_claw_options': {'coupler_type': 'NCap...

Let’s use this interpolated deisgn to generate a .gds file.

[65]:

from squadds.components.coupled_systems import QubitCavity
import qiskit_metal as metal
from qiskit_metal import Dict, MetalGUI, designs, draw
from qiskit_metal.toolbox_metal import math_and_overrides

design = metal.designs.design_planar.DesignPlanar()
gui = metal.MetalGUI(design)
design.overwrite_enabled = True

qc_ncap = QubitCavity(design, "qubit_cavity", options=design_df.iloc[0]["design_options"])
gui.rebuild()
gui.autoscale()
gui.screenshot("qubit_half_wave_cavity")

../../_images/source_tutorials_Tutorial-1_Getting_Started_with_SQuADDS_119_0.png

[67]:

qc_ncap.show(gui, include_wirebond_pads=True)

{'pos_x': '0.0um', 'pos_y': '0.0um', 'orientation': -90.0, 'chip': 'main', 'layer': '1', 'prime_width': 0.0117, 'prime_gap': 0.0050999999999999995, 'second_width': 0.0117, 'second_gap': 0.0050999999999999995, 'cap_gap': 0.0040999999999999995, 'cap_width': 0.004900000000000001, 'finger_length': 0.026000000000000002, 'finger_count': 2.0, 'cap_distance': 0.0509, 'hfss_wire_bonds': False, 'q3d_wire_bonds': False, 'aedt_q3d_wire_bonds': False, 'aedt_hfss_wire_bonds': False, 'coupling_length': None, 'cap_gap_ground': 0.0050999999999999995, 'coupling_space': None, 'down_length': None, 'open_termination': None}

../../_images/source_tutorials_Tutorial-1_Getting_Started_with_SQuADDS_120_1.png

[68]:

qc_ncap.to_gds("qubit_cavity", include_wirebond_pads=False)

01:25PM 57s WARNING [_import_junction_gds_file]: Not able to find file:"../resources/Fake_Junctions.GDS".  Not used to replace junction. Checked directory:"/Users/shanto/LFL/SQuADDS/SQuADDS/resources".

Congrats for making it to the end of this tutorial! 🤗🎉 You have now learned how to use the SQuADDS API to query for closest and “best-guess” interpolated device designs for your chosen Hamiltonian parameters.

Next Steps…#

In the next tutorial, we will learn how to simulate the “best-guess” design using an EM solver tool and the SQuADDS API.

License#

This code is a part of SQuADDS

Developed by Sadman Ahmed Shanto

This tutorial is written by Sadman Ahmed Shanto

This code is licensed under the MIT License. You may obtain a copy of this license in the LICENSE.txt file in the root directory of this source tree.

Any modifications or derivative works of this code must retain thiscopyright notice, and modified files need to carry a notice indicatingthat they have been altered from the originals.