silq.parameters package

Submodules

silq.parameters.acquisition_parameters module

class silq.parameters.acquisition_parameters.AcquisitionParameter(continuous=False, properties_attrs=None, wrap_set=False, save_traces=False, **kwargs)[source]

Bases: silq.tools.general_tools.SettingsClass, qcodes.instrument.parameter.MultiParameter

Parameter used for acquisitions involving a PulseSequence.

Each AcquisitionParameter has an associated pulse sequence, which it directs to the Layout, after which it acquires traces and performs post-processing.

Generally, the flow of an AcquisitionParameter is as follows:

  1. AcquisitionParameter.acquire, which acquires traces. This stage can be subdivided into several steps:

    1. Generate PulseSequence if pulse sequence properties have changed Note that this is only necessary for a PulseSequenceGenerator, which is a more complicated pulse sequences that can be generated from properties.

    2. Target pulse sequence in Layout. This only happens if Layout.pulse_sequence differs from the pulse sequence used.

    3. Call Layout.setup which sets up instruments with new pulse sequence. Again, this is only done if Layout.pulse_sequence differs.

    4. Acquire traces using Layout.acquisition This in turn gets the traces fromm the acquisition instrument. The returned traces are segmented by pulse and acquisition channel.

  2. AcquisitionParameter.analyse, which analyses the traces This method differs per AcquisitionParameter.

  3. Perform ancillary actions such as saving traces, printing results

  4. Return list of results for any measurement. The subset of results that are in AcquisitionParameter.names is returned.

Parameters

  • continuous (bool) – If True, instruments keep running after acquisition

  • properties_attrs (List[str]) – attributes to match with silq.config.properties (see notes below).

  • save_traces (bool) – Save acquired traces to disk

  • **kwargs – Additional kwargs passed to MultiParameter

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs – Attributes to match with silq.config.properties See notes below for more info.

  • save_traces – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

Notes

  • AcquisitionParameters are subclasses of the MultiParameter, and therefore always returns multiple values

  • AcquisitionParameters are also subclasses of SettingsClass, which gives it the ability to temporarily override its attributes using methods single_settings and temporary_settings. These overridden settings can be clear later on. This is useful if you temporarily want to change settings. See SettingsClass for more info.

  • When certain elements in silq.config are updated, this will also update the corresponding attributes in the AcquisitionParameter. Two config paths are monitored:

    • silq.config.properties, though only the attributes specified in properties_attrs.

    • silq.parameters.{self.name}.

acquire(stop=None, setup=None, save_traces=None, **kwargs)[source]

Performs a Layout.acquisition.

Parameters

  • stop (Optional[bool]) – Stop instruments after acquisition. If not specified, it will stop if AcquisitionParameter.continuous is False.

  • setup (Optional[bool]) – Whether to setup layout before acquisition. If not specified, it will setup if pulse_sequences are different

  • save_traces (Optional[bool]) – whether to save traces during

  • **kwargs – Additional kwargs to be given to Layout.acquisition.

Return type

Dict[str, Dict[str, ndarray]]

Returns

acquisition traces dictionary, segmented by pulse. dictionary has the following format: {pulse.full_name: {acquisition_channel_label: traces}} where acquisition_channel_label is specified in Layout.

analyse(traces=None)[source]

Analyse traces, should be implemented in subclass

Return type

Dict[str, Any]

formatter = <qcodes.data.hdf5_format.HDF5Format object>
get_raw()[source]
labels
layout = None
plot_traces(filter=None, channels=['output'], t_skip=True, **kwargs)[source]
print_results()[source]

Print results whose keys are in AcquisitionParameter.names

property sample_rate

Acquisition sample rate

set(**kwargs)[source]

Perform an acquisition with temporarily modified settings

Shorthand for: ` AcquisitionParameter.single_settings(**kwargs) AcquisitionParameter() `

setup(start=None, **kwargs)[source]

Setup instruments with current pulse sequence.

Parameters

  • start (Optional[bool]) – Start instruments after setup. If not specified, will use value in AcquisitionParameter.continuous.

  • **kwargs – Additional kwargs passed to Layout.setup.

snapshot_base(update=False, params_to_skip_update=None, simplify=False)[source]

State of the parameter as a JSON-compatible dict.

Parameters

  • update (bool) – If True, update the state by calling parameter.get(). If False, just use the latest values in memory.

  • params_to_skip_update (Optional[Sequence[str]]) – No effect but may be passed from super Class:

Returns

base snapshot

Return type

dict

class silq.parameters.acquisition_parameters.DCParameter(name='DC', unit='V', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Acquires DC voltage

The pulse sequence contains a single read pulse, the duration of which specifies how long should be averaged.

Parameters

  • name (str) – Parameter name.

  • unit (str) – Unit of DC voltage (e.g. can be changed to nA)

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

Returns

Average DC voltage measured on the output channel DC_noise: noise standard deviation measured on the output channel

Return type

DC_voltage

Notes

  • DCParameter.continuous is True by default

Todo

implement continuous acquisition in the ATS interface

analyse(traces=None)[source]

Analyse traces, should be implemented in subclass

class silq.parameters.acquisition_parameters.TraceParameter(name='trace_pulse', average_mode='none', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

An acquisition parameter for obtaining a trace or multiple traces of a given PulseSequence.

Example

>>> parameter.average_mode = 'none'
>>> parameter.pulse_sequence = my_pulse_sequence

Note that for the above example, all pulses in my_pulse_sequence will be copied.

Parameters

  • trace_pulse (Pulse) – Acquisition measurement pulse. Duration is dynamically set to the duration of acquired pulses.

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

acquire(**kwargs)[source]

Acquires the number of traces defined in self.samples

Parameters

**kwargs – kwargs passed to AcquisitionParameter.acquire

Returns

A tuple of data points. e.g. ((data_for_1st_output), (data_for_2nd_output), …)

analyse(traces=None)[source]

Rearrange traces to match AcquisitionParameter.names

property average_mode

Acquisition averaging mode.

The attribute Pulse.average is overridden.

labels
names
property pulse_sequence
setpoint_names
setpoint_units
setpoints
setup(start=None, **kwargs)[source]

Modifies provided pulse sequence by creating a single pulse which overlaps all other pulses with acquire=True and then acquires only this pulse.

shapes
units
class silq.parameters.acquisition_parameters.DCSweepParameter(name='DC_sweep', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Perform 1D and 2D DC sweeps by rapidly varying AWG output voltages

Using this parameter, a 2D DC sweep of 100x100 points can be obtained in ~1 second. This does of course depend on the filtering of the lines, and the acquisition sampling rate. This is used in the DCSweepPlot to continuously update and display the charge stability diagram.

The pulse sequence is created by first calling DCSweepParameter.add_sweep, which adds a dimension every time it’s called. After adding the sweeps, DCSweepParameter.generate will create the corresponding PulseSequence.

Parameters

  • name – parameter name

  • **kwargs – Additional kwargs passed to AcquisitionParameter

  • trace_pulse (Pulse) – Trace pulse at fixed voltage at the end of sweep. Can be turned off by trace_pulse.enabled = False.

  • pulse_duration (float) – Duration of each point in DC sweep

  • final_delay (float) – Delay at end of pulse sequence.

  • inter_delay (float) – Delay after each row of DC points

  • use_ramp (bool) – Combine single row of DC points into a ramp pulse that will be segmented later. This saves number of waveforms sent, reduces triggers, and creates less Pulse objects.

  • sweep_parameters (UpdateDotDict) – Sweep parameters. Every time an item is updated, DCSweepParameter.generate is called.

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

Note

Currently only works up to 2D.

Todo

Convert pulse sequence and generator into PulseSequenceGenerator

add_sweep(parameter_name, sweep_voltages=None, connection_label=None, offset_parameter=None)[source]

Add sweep to DCSweepParameter.sweep_parameters

Each call will add a sweep as the outer dimension.

Parameters

  • parameter_name (str) – Name of parameter (for axis labelling).

  • sweep_voltages (Optional[ndarray]) – List of sweep voltages. If DCSweepParameter.use_ramp is True, these must be equidistant.

  • connection_label (Optional[str]) – Connection label to target pulses to. For multiple sweeps, each connection label must be distinct. Connection labels are defined in Layout.acquisition_outputs.

  • offset_parameter (Optional[Parameter]) – Parameter used for offsetting the sweep voltages. Usually this is the corresponding DC voltage parameter.

analyse(traces=None)[source]

Analyse traces, ensuring resulting dimensionality is correct

Parameters

traces (Optional[Dict[str, Dict[str, ndarray]]]) – Traces returned by AcquisitionParameter.acquire.

Returns

Dict containing:

DC_voltage (np.ndarray)

DC voltages with dimensionality corresponding to number of sweeps.

trace_voltage (np.ndarray)

voltage trace of final trace pulse. Only used if DCSweepParameter.trace_pulse.enabled. Trace in output connection label is returned.

Return type

(Dict[str, Any])

connect_to_config = True
generate()[source]

Generates pulse sequence using sweeps in DCSweepParameter.add_sweep

Note

Currently only works for 1D and 2D

labels
names
setpoint_names
setpoint_units
setpoints
shapes
units
class silq.parameters.acquisition_parameters.EPRParameter(name='EPR', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Parameter for an empty-plunge-read sequence.

Parameters

  • name – Name of acquisition parameter

  • **kwargs – Additional kwargs passed to AcquisitionParameter.

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • t_skip (float) – initial part of read trace to ignore for measuring blips. Useful if there is a voltage spike at the start, which could otherwise be measured as a blip. Retrieved from silq.config.properties.t_skip.

  • t_read (float) – duration of read trace to include for measuring blips. Useful if latter half of read pulse is used for initialization. Retrieved from silq.config.properties.t_read.

  • min_filter_proportion (float) – Minimum number of read traces needed in which the voltage starts low (loaded donor). Otherwise, most results are set to zero. Retrieved from silq.config.properties.min_filter_proportion.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

Note

A read_long pulse is used instead of read because this allows comparison of the start and end of the pulse, giving the contrast.

analyse(traces=None, plot=False)[source]

Analyse traces using analyse_EPR

Return type

Dict[str, Any]

class silq.parameters.acquisition_parameters.ESRParameter(name='ESR', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Parameter for most pulse sequences involving electron spin resonance.

This parameter can handle many of the simple pulse sequences involving ESR. It uses the ESRPulseSequence, which will generate a pulse sequence from settings (see parameters below).

In general the pulse sequence is as follows:

  1. Perform any pre_pulses defined in ESRParameter.pre_pulses.

  2. Perform stage pulse ESRParameter.ESR['stage_pulse']. By default, this is the plunge pulse.

  3. Perform ESR pulse within plunge pulse, the delay from start of plunge pulse is defined in ESRParameter.ESR['pulse_delay'].

  4. Perform read pulse ESRParameter.ESR['read_pulse'].

  5. Repeat steps 2 and 3 for each ESR pulse in ESRParameter.ESR['ESR_pulses'], which by default contains single pulse ESRParameter.ESR['ESR_pulse'].

  6. Perform empty-plunge-read sequence (EPR), but only if ESRParameter.EPR['enabled'] is True. EPR pulses are defined in ESRParameter.EPR['pulses'].

  7. Perform any post_pulses defined in ESRParameter.post_pulses.

A shorthand for using the default ESR pulse for multiple frequencies is by setting ESRParameter.ESR_frequencies. Settings this will create a copy of ESRParameter.ESR[‘ESR_pulse’] with the respective frequency.

Examples

The following code measures two ESR frequencies and performs an EPR from which the contrast can be determined for each ESR frequency:

>>> ESR_parameter = ESRParameter()
>>> ESR_parameter.ESR['pulse_delay'] = 5e-3
>>> ESR_parameter.ESR['stage_pulse'] = DCPulse['plunge']
>>> ESR_parameter.ESR['ESR_pulse'] = FrequencyRampPulse('ESR_adiabatic')
>>> ESR_parameter.ESR_frequencies = [39e9, 39.1e9]
>>> ESR_parameter.EPR['enabled'] = True
>>> ESR_parameter.pulse_sequence.generate()

The total pulse sequence is plunge-read-plunge-read-empty-plunge-read with an ESR pulse in the first two plunge pulses, 5 ms after the start of the plunge pulse. The ESR pulses have different frequencies.

Parameters

  • name – Name of acquisition parameter

  • **kwargs – Additional kwargs passed to AcquisitionParameter.

  • ESR (dict) – ESRPulseSequence generator settings for ESR. Settings are: stage_pulse, ESR_pulse, ESR_pulses, pulse_delay, read_pulse.

  • EPR (dict) – ESRPulseSequence generator settings for EPR. This is optional and can be toggled in EPR['enabled']. If disabled, contrast is not calculated. Settings are: enabled, pulses.

  • pre_pulses (List[Pulse]) – Pulses to place at the start of the sequence.

  • post_pulses (List[Pulse]) – Pulses to place at the end of the sequence.

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • t_skip (float) – initial part of read trace to ignore for measuring blips. Useful if there is a voltage spike at the start, which could otherwise be measured as a blip. Retrieved from silq.config.properties.t_skip.

  • t_read (float) – duration of read trace to include for measuring blips. Useful if latter half of read pulse is used for initialization. Retrieved from silq.config.properties.t_read.

  • min_filter_proportion (float) – Minimum number of read traces needed in which the voltage starts low (loaded donor). Otherwise, most results are set to zero. Retrieved from silq.config.properties.min_filter_proportion.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties. See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

Notes

  • All pulse settings are copies of ESRParameter.pulse_sequence.pulse_settings.

  • For given pulse settings, ESRParameter.pulse_sequence.generate will recreate the pulse sequence from settings.

property ESR_frequencies

Apply default ESR pulse for each ESR frequency given.

analyse(traces=None, plot=False)[source]

Analyse ESR traces.

If there is only one ESR pulse, returns up_proportion_{pulse.name}. If there are several ESR pulses, adds a zero-based suffix at the end for each ESR pulse. If ESRParameter.EPR['enabled'] == True, the results from analyse_EPR are also added, as well as contrast_{pulse.name} (plus a suffix if there are several ESR pulses).

property names
shapes
units
class silq.parameters.acquisition_parameters.NMRParameter(name='NMR', names=['flips', 'flip_probability', 'up_proportions'], **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Parameter for most measurements involving an NMR pulse.

This parameter can apply several NMR pulses, and also measure several ESR frequencies. It uses the NMRPulseSequence, which will generate a pulse sequence from settings (see parameters below).

In general, the pulse sequence is as follows:

  1. Perform any pre_pulses defined in NMRParameter.pre_pulses.

  2. Perform NMR sequence

    1. Perform stage pulse NMRParameter.NMR['stage_pulse']. Default is ‘empty’ DCPulse.

    2. Perform NMR pulses within the stage pulse. The NMR pulses defined in NMRParameter.NMR['NMR_pulses'] are applied successively. The delay after start of the stage pulse is NMRParameter.NMR['pre_delay'], delays between NMR pulses is NMRParameter.NMR['inter_delay'], and the delay after the final NMR pulse is NMRParameter.NMR['post_delay'].

  3. Perform ESR sequence

    1. Perform stage pulse NMRParameter.ESR['stage_pulse']. Default is ‘plunge’ DCPulse.

    2. Perform ESR pulse within stage pulse for first pulse in NMRParameter.ESR['ESR_pulses'].

    3. Perform NMRParameter.ESR['read_pulse'], and acquire trace.

    4. Repeat steps 1 - 3 for each ESR pulse. The different ESR pulses usually correspond to different ESR frequencies (see NMRParameter.ESR_frequencies).

    5. Repeat steps 1 - 4 for NMRParameter.ESR['shots_per_frequency'] This effectively interleaves the ESR pulses, which counters effects of the nucleus flipping within an acquisition.

This acquisition is repeated NMRParameter.samples times. If the nucleus is in one of the states for which an ESR frequency is on resonance, a high up_proportion is measured, while for the other frequencies a low up_proportion is measured. By looking over successive samples and measuring how often the up_proportions switch between above/below NMRParameter.threshold_up_proportion, nuclear flips can be measured (see NMRParameter.analyse and analyse_flips).

Parameters

  • name (str) – Parameter name

  • **kwargs – Additional kwargs passed to AcquisitionParameter

  • NMR (dict) – NMRPulseSequence pulse settings for NMR. Settings are: stage_pulse, NMR_pulse, NMR_pulses, pre_delay, inter_delay, post_delay.

  • ESR (dict) – NMRPulseSequence pulse settings for ESR. Settings are: ESR_pulse, stage_pulse, ESR_pulses, read_pulse, pulse_delay.

  • EPR (dict) – PulseSequenceGenerator settings for EPR. This is optional and can be toggled in EPR['enabled']. If disabled, contrast is not calculated.

  • pre_pulses (List[Pulse]) – Pulses to place at the start of the sequence.

  • post_pulses (List[Pulse]) – Pulses to place at the end of the sequence.

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • ESR_frequencies (List[float]) – List of ESR frequencies to use. When set, a copy of NMRParameter.ESR['ESR_pulse'] is created for each frequency, and added to NMRParameter.ESR['ESR_pulses'].

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • t_skip (float) – initial part of read trace to ignore for measuring blips. Useful if there is a voltage spike at the start, which could otherwise be measured as a blip. Retrieved from silq.config.properties.t_skip.

  • t_read (float) – duration of read trace to include for measuring blips. Useful if latter half of read pulse is used for initialization. Retrieved from silq.config.properties.t_read.

  • threshold_up_proportion (Union[float, Tuple[float, float]) – threshold for up proportions needed to determine ESR pulse to be on-resonance. If tuple, first element is threshold below which ESR pulse is off-resonant, and second element is threshold above which ESR pulse is on-resonant. Useful for filtering of up proportions at boundary. Retrieved from silq.config.properties.threshold_up_proportion.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

Note

  • The NMRPulseSequence does not have an empty-plunge-read (EPR) sequence, and therefore does not add a contrast or dark counts. Verifying that the system is in tune is therefore a little bit tricky.

property ESR_frequencies

ESR frequencies to measure.

For each ESR frequency, NMRParameter.ESR['shots_per_read'] reads are performed.

analyse(traces=None)[source]

Analyse flipping events between nuclear states

Returns

Dict containing:

results_read (dict)

analyse_traces results for each read trace

up_proportions_{idx} (np.ndarray)

Up proportions, the dimensionality being equal to NMRParameter.samples. {idx} is replaced with the zero-based ESR frequency index.

Results from analyse_flips. These are

  • flips_{idx},

  • flip_probability_{idx}

  • combined_flips_{idx1}{idx2}

  • combined_flip_probability_{idx1}{idx2}

Additionally, each of the above results will have another result with the same name, but prepended with filtered_, and appended with _{idx1}{idx2} if not already present. Here, all the values are filtered out where the corresponding pair of up_proportion samples do not have exactly one high and one low for each sample. The values that do not satisfy the filter are set to np.nan.

filtered_scans_{idx1}{idx2}

Return type

(Dict[str, Any])

property names
shapes
units
class silq.parameters.acquisition_parameters.VariableReadParameter(name='variable_read', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Parameter for measuring spin tails.

The pulse sequence is plunge - read - empty. By varying the read amplitude, the voltage should transition between high voltage (empty) to low voltage (plunge), and somewhere in between an increased voltage should be visible at the start, indicating spin-dependent tunneling.

Parameters

  • name – Parameter name

  • **kwargs – Additional kwargs passed to AcquisitionParameter

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples to average over.

  • results (dict) – Results obtained after analysis of traces.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

analyse(traces=None)[source]

Analyse traces, should be implemented in subclass

setpoints
shapes
class silq.parameters.acquisition_parameters.BlipsParameter(name='count_blips', duration=None, pulse_name='DC_trace', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Parameter that measures properties of blips in a trace

The PulseSequence consists of a single read pulse. From this trace, the number of blips per second is counted, as well as the mean time in low and high voltage state. This parameter can be used in retuning sequence.

Parameters

  • name (str) – Parameter name.

  • duration (Optional[float]) – Duration of read trace

  • pulse_name (str) – Name of read pulse

  • **kwargs – Additional kwargs passed to AcquisitionParameter.

  • threshold_voltage (float) – Threshold voltage for a blip in voltage.

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • t_skip (float) – initial part of read trace to ignore for measuring blips. Useful if there is a voltage spike at the start, which could otherwise be measured as a blip. Retrieved from silq.config.properties.t_skip.

  • t_read (float) – duration of read trace to include for measuring blips. Useful if latter half of read pulse is used for initialization. Retrieved from silq.config.properties.t_read.

  • min_filter_proportion (float) – Minimum number of read traces needed in which the voltage starts low (loaded donor). Otherwise, most results are set to zero. Retrieved from silq.config.properties.min_filter_proportion.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

See also

analyse(traces=None)[source]

count_blips analysis.

property duration

Shorthand for read pulse duration.

class silq.parameters.acquisition_parameters.FlipNucleusParameter(name='flip_nucleus', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

get_NMR_pulses(initial_state, final_state, mode='full')[source]
set(initial_state, final_state, run=True)[source]

Perform an acquisition with temporarily modified settings

Shorthand for: ` AcquisitionParameter.single_settings(**kwargs) AcquisitionParameter() `

class silq.parameters.acquisition_parameters.FlipFlopParameter(name='flip_flop', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Parameter for performing flip-flopping, not meant for acquiring data

analyse(traces=None)[source]

Analyse traces, should be implemented in subclass

set(frequencies=None, pre_flip=None, evaluate=True, **kwargs)[source]

Perform an acquisition with temporarily modified settings

Shorthand for: ` AcquisitionParameter.single_settings(**kwargs) AcquisitionParameter() `

class silq.parameters.acquisition_parameters.NeuralNetworkParameter(target_parameter, input_names, output_names=None, model_filepath=None, include_target_output=None, **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Base parameter for neural networks

Todo

acquire()[source]

Performs a Layout.acquisition.

Parameters

  • stop – Stop instruments after acquisition. If not specified, it will stop if AcquisitionParameter.continuous is False.

  • setup – Whether to setup layout before acquisition. If not specified, it will setup if pulse_sequences are different

  • save_traces – whether to save traces during

  • **kwargs – Additional kwargs to be given to Layout.acquisition.

Returns

acquisition traces dictionary, segmented by pulse. dictionary has the following format: {pulse.full_name: {acquisition_channel_label: traces}} where acquisition_channel_label is specified in Layout.

analyse(traces)[source]

Analyse traces, should be implemented in subclass

names
pulse_sequence
class silq.parameters.acquisition_parameters.NeuralRetuneParameter(target_parameter, output_parameters, update=False, **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.NeuralNetworkParameter

Parameter that uses neural network for retuning.

Todo

analyse(traces)[source]

Analyse traces, should be implemented in subclass

property base_folder
names
class silq.parameters.acquisition_parameters.ESRRamseyDetuningParameter(name='ESRRamsey', **kwargs)[source]

Bases: silq.parameters.acquisition_parameters.AcquisitionParameter

Parameter for most pulse sequences involving electron spin resonance.

This parameter can handle many of the simple pulse sequences involving ESR. It uses the ESRPulseSequence, which will generate a pulse sequence from settings (see parameters below).

In general the pulse sequence is as follows:

  1. Perform any pre_pulses defined in ESRParameter.pre_pulses.

  2. Perform stage pulse ESRParameter.ESR['stage_pulse']. By default, this is the plunge pulse.

  3. Perform ESR pulse within plunge pulse, the delay from start of plunge pulse is defined in ESRParameter.ESR['pulse_delay'].

  4. Perform read pulse ESRParameter.ESR['read_pulse'].

  5. Repeat steps 2 and 3 for each ESR pulse in ESRParameter.ESR['ESR_pulses'], which by default contains single pulse ESRParameter.ESR['ESR_pulse'].

  6. Perform empty-plunge-read sequence (EPR), but only if ESRParameter.EPR['enabled'] is True. EPR pulses are defined in ESRParameter.EPR['pulses'].

  7. Perform any post_pulses defined in ESRParameter.post_pulses.

A shorthand for using the default ESR pulse for multiple frequencies is by setting ESRParameter.ESR_frequencies. Settings this will create a copy of ESRParameter.ESR[‘ESR_pulse’] with the respective frequency.

Examples

The following code measures two ESR frequencies and performs an EPR from which the contrast can be determined for each ESR frequency:

>>> ESR_parameter = ESRParameter()
>>> ESR_parameter.ESR['pulse_delay'] = 5e-3
>>> ESR_parameter.ESR['stage_pulse'] = DCPulse['plunge']
>>> ESR_parameter.ESR['ESR_pulse'] = FrequencyRampPulse('ESR_adiabatic')
>>> ESR_parameter.ESR_frequencies = [39e9, 39.1e9]
>>> ESR_parameter.EPR['enabled'] = True
>>> ESR_parameter.pulse_sequence.generate()

The total pulse sequence is plunge-read-plunge-read-empty-plunge-read with an ESR pulse in the first two plunge pulses, 5 ms after the start of the plunge pulse. The ESR pulses have different frequencies.

Parameters

  • name – Name of acquisition parameter

  • **kwargs – Additional kwargs passed to AcquisitionParameter.

  • ESR (dict) – ESRPulseSequence generator settings for ESR. Settings are: stage_pulse, ESR_pulse, ESR_pulses, pulse_delay, read_pulse.

  • EPR (dict) – ESRPulseSequence generator settings for EPR. This is optional and can be toggled in EPR['enabled']. If disabled, contrast is not calculated. Settings are: enabled, pulses.

  • pre_pulses (List[Pulse]) – Pulses to place at the start of the sequence.

  • post_pulses (List[Pulse]) – Pulses to place at the end of the sequence.

  • pulse_sequence (PulseSequence) – Pulse sequence used for acquisition.

  • samples (int) – Number of acquisition samples

  • results (dict) – Results obtained after analysis of traces.

  • t_skip (float) – initial part of read trace to ignore for measuring blips. Useful if there is a voltage spike at the start, which could otherwise be measured as a blip. Retrieved from silq.config.properties.t_skip.

  • t_read (float) – duration of read trace to include for measuring blips. Useful if latter half of read pulse is used for initialization. Retrieved from silq.config.properties.t_read.

  • min_filter_proportion (float) – Minimum number of read traces needed in which the voltage starts low (loaded donor). Otherwise, most results are set to zero. Retrieved from silq.config.properties.min_filter_proportion.

  • traces (dict) – Acquisition traces segmented by pulse and acquisition label

  • silent (bool) – Print results after acquisition

  • continuous (bool) – If True, instruments keep running after acquisition. Useful if stopping/starting instruments takes a considerable amount of time.

  • properties_attrs (List[str]) – Attributes to match with silq.config.properties. See notes below for more info.

  • save_traces (bool) – Save acquired traces to disk. If the acquisition has been part of a measurement, the traces are stored in a subfolder of the corresponding data set. Otherwise, a new dataset is created.

  • dataset (DataSet) – Traces DataSet

  • base_folder (str) – Base folder in which to save traces. If not specified, and acquisition is part of a measurement, the base folder is the folder of the measurement data set. Otherwise, the base folder is the default data folder

  • subfolder (str) – Subfolder within the base folder to save traces.

Notes

  • All pulse settings are copies of ESRParameter.pulse_sequence.pulse_settings.

  • For given pulse settings, ESRParameter.pulse_sequence.generate will recreate the pulse sequence from settings.

property ESR_frequencies

Apply default ESR pulse for each ESR frequency given.

analyse(traces=None, plot=False)[source]

Analyse ESR traces.

If there is only one ESR pulse, returns up_proportion_{pulse.name}. If there are several ESR pulses, adds a zero-based suffix at the end for each ESR pulse. If ESRParameter.EPR['enabled'] == True, the results from analyse_EPR are also added, as well as contrast_{pulse.name} (plus a suffix if there are several ESR pulses).

property names
shapes
units

silq.parameters.acquisition_parameters_composite module

silq.parameters.general_parameters module

class silq.parameters.general_parameters.CombinedParameter(parameters, name=None, label='', unit=None, offsets=None, scales=None, full_label=True, **kwargs)[source]

Bases: qcodes.instrument.parameter.Parameter

Combines multiple parameters into a single parameter.

Setting this parameter sets all underlying parameters to this value, after applying possible scale and offset in that order. Getting this parameter gets the value of the first parameter, and applies offset and scale in that order.

Parameters

  • parameters (List[Parameter]) – Parameters to be combined.

  • name (Optional[str]) – Name of CombinedParameter, by default equal to the names of the composed parameters separated by underscores.

  • label (str) – Label of CombinedParameter, by default equal to the labels of the composed parameters separated by and. Also includes any scale and offset.

  • unit (Optional[str]) – Parameter unit.

  • offsets (Optional[List[float]]) – Optional offset for parameters. If set, must have equal number of elements as parameters

  • scales (Optional[List[float]]) – Optional scale for parameters. If set, must have equal number of elements as parameters.

  • full_label (bool) – Add scales and offsets for all parameters to the label

  • **kwargs – Additional kwargs passed to Parameter.

Note

  • All args are also attributes.

  • While QCoDeS already has a CombinedParameter, it has some shortcomings which are addressed here. Maybe in the future this will be PR’ed to the main QCoDeS repository.

calculate_individual_values(value)[source]

Calulate values of parameters from a combined value

Parameters

value – combined value

Returns

list of values for each parameter

get_raw()[source]
property label
set_raw(value)[source]
zero_offset(offset=0)[source]

Use current values of parameters as offsets.

class silq.parameters.general_parameters.AttributeParameter(object, attribute, name=None, is_key=None, **kwargs)[source]

Bases: qcodes.instrument.parameter.Parameter

Creates a parameter that can set/get an attribute from an object.

Parameters

  • object (object) – Object whose attribute to set/get.

  • attribute (str) – Attribute to set/get

  • is_key (Optional[bool]) – whether the attribute is a key in a dictionary. If not specified, it will check if AttributeParameter.object is a dict.

  • **kwargs – Additional kwargs passed to Parameter.

get_raw()[source]
set_raw(value)[source]

silq.parameters.measurement_parameters module

class silq.parameters.measurement_parameters.MeasurementParameter(name, acquisition_parameter=None, discriminant=None, silent=True, **kwargs)[source]

Bases: silq.tools.general_tools.SettingsClass, qcodes.instrument.parameter.MultiParameter

Base class for parameters that perform measurements.

A MeasurementParameter usually consists of several acquisitions, which it uses for complex sequences.

A MeasurementParameter usually uses a qcodes.Loop or a qcodes.Measure or several in succession. The results in the DataSet are analysed and often some post-action is performed.

Example

An example of a MeasurementParameter is a retuning sequence, which uses an AcquisitionParameter, and from that determines how much voltages have to be modified to retune the system (e.g. RetuneBlipsParameter).

Note

The MeasurementParameter needs to be updated. It was originally created to be used with the MeasurementSequence, but this class turned out to be too rigid. Instead, measurements should be programmed by subclassing the MeasurementParameter.

Parameters

  • Name – Parameter name

  • acquisition_parameter – Acquisition_parameter to use. Fails in case of multiple or no acquisition parameters

  • discriminant – data array in dataset to discriminate. Fails if there is no single discriminant.

  • silent (str) – Print results during .get()

Todo

  • Clean up MeasurementParameter, remove attributes MeasurementParameter.discriminant and MeasurementParameter.acquisition_parameter.

property acquisition_parameter_name
property base_folder

Obtain measurement base folder (if any).

Returns

If in a measurement, the base folder is the relative path of the data folder. Otherwise None

property discriminant
property discriminant_idx
layout = None
property loc_provider
print_results()[source]
class silq.parameters.measurement_parameters.RetuneBlipsParameter(name='retune_blips', coulomb_peak_parameter=None, blips_parameter=None, sweep_parameter=None, sweep_vals=None, tune_to_coulomb_peak=True, tune_to_optimum=True, optimum_DC_offset=None, model_filepath=None, voltage_limit=None, optimum_method='neural_network', **kwargs)[source]

Bases: silq.parameters.measurement_parameters.MeasurementParameter

Parameter that retunes by analysing blips using a neural network

The first (optional) stage is to use a CoulombPeakParameter to find the center of the Coulomb peak.

Second, a sweep parameter is varied for a range of sweep values. For each sweep point, a trace is acquired, and its blips measured in a BlipsParameter. This information is then analysed by a neural network, from which the optimal tuning position is predicted.

The Neural network is a Keras model that needs to be pre-trained with data. More info: Experiments/personal/Serwan/Neural networks/Retune blips.ipynb The following Neural Network seems to produce decent results:

>>> model = Sequential()
>>> model.add(Dense(3, activation='linear', input_shape=(21,3)))
>>> model.add(Flatten())
>>> model.add(Dense(1, activation='linear'))
calculate_optimum()[source]

Calculate optimum of dataset from neural network Returns: optimal voltage of combined set parameter

create_loop()[source]

Create loop that sweep sweep_parameter over sweep_vals and measures blips_parameter at each sweep point. Returns: loop

get_raw()[source]
shapes
class silq.parameters.measurement_parameters.CoulombPeakParameter(name='coulomb_peak', sweep_parameter=None, acquisition_parameter=None, combined_set_parameter=None, DC_peak_offset=None, tune_to_peak=True, min_voltage=0.5, interpolate=True, **kwargs)[source]

Bases: silq.parameters.measurement_parameters.MeasurementParameter

Parameter that finds Coulomb peak and can tune to it. Finding the Coulomb peak is done by sweeping a gate and measuring the DC voltage at each point.

calculate_sweep_vals()[source]
create_loop(sweep_vals)[source]
get_raw()[source]
names
shapes
class silq.parameters.measurement_parameters.DCMultisweepParameter(name, acquisition_parameter, x_gate, y_gate, **kwargs)[source]

Bases: silq.parameters.measurement_parameters.MeasurementParameter

property AC_x_vals
property AC_y_vals
property DC_x_vals
property DC_y_vals
formatter = None
get()[source]
setpoints
setup()[source]
shapes
property x_sweep_range
property x_sweeps
property y_sweep_range
property y_sweeps
class silq.parameters.measurement_parameters.MeasurementSequenceParameter(name, measurement_sequence=None, set_parameters=None, discriminant=None, start_condition=None, **kwargs)[source]

Bases: silq.parameters.measurement_parameters.MeasurementParameter

get()[source]
class silq.parameters.measurement_parameters.SelectFrequencyParameter(threshold=0.5, discriminant=None, frequencies=None, mode=None, acquisition_parameter=None, update_frequency=True, **kwargs)[source]

Bases: silq.parameters.measurement_parameters.MeasurementParameter

get()[source]
property spin_states
class silq.parameters.measurement_parameters.TrackPeakParameter(name, set_parameter=None, acquisition_parameter=None, step_percentage=None, peak_width=None, points=None, discriminant=None, threshold=None, **kwargs)[source]

Bases: silq.parameters.measurement_parameters.MeasurementParameter

get()[source]
property set_vals
property shapes

Module contents