See also

A Jupyter notebook version of this tutorial can be downloaded here.

Synchronization

In this tutorial we will demonstrate how to synchronise two Qblox instruments using the SYNQ technology (see section Synchronization). For this tutorial we will use one QCM and one QRM and we will be acquiring waveforms sequenced by the QCM using the QRM. By synchronizing the two instruments using the SYNQ technology, timing the acquisition of the waveforms becomes trivial.

If we are using a Pulsar QCM and QRM we need to connect both instruments to the same network, connect the \(\text{REF}^{out}\) of the QCM to the \(\text{REF}^{in}\) of the QRM using a 50cm coaxial cable, connect their SYNQ ports using the SYNQ cable and finally connect \(\text{O}^{[1-2]}\) of the QCM to \(\text{I}^{[1-2]}\) of the QRM respectively.

The Cluster QCM and QRM are internally connected for SYNQ capability and therefore no new need connections need to be made if one is using a Cluster.

To run this tutorial please make sure you have installed and enabled ipywidgets:

pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension

Setup

First, we are going to import the required packages and connect to the instrument.

# Import ipython widgets
import json
import math
import os

import ipywidgets as widgets
import matplotlib.pyplot
import numpy

# Set up the environment.
import scipy.signal
from IPython.display import display
from ipywidgets import fixed, interact, interact_manual, interactive
from qcodes import Instrument
from qblox_instruments import Cluster, PlugAndPlay, Pulsar

Scan For Devices

We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see Plug & Play for more info).

# Scan for available devices and display
with PlugAndPlay() as p:
    # get info of all devices
    device_list = p.list_devices()

names = {
    dev_id: dev_info["description"]["name"] for dev_id, dev_info in device_list.items()
}
ip_addresses = {
    dev_id: dev_info["identity"]["ip"] for dev_id, dev_info in device_list.items()
}

# create widget for names and ip addresses
connect = widgets.Dropdown(
    options=[(names[dev_id] + " @" + ip_addresses[dev_id], dev_id)
             for dev_id in device_list.keys()],
    description="Select Device",
)
display(connect)

The following widget displays all the existing modules that are connected to your PC which includes the Pulsar modules as well as a Cluster. Select the device you want to run the notebook on.

Connect to Cluster

We now make a connection with the Cluster selected in the dropdown widget. We also define a function to find the modules we’re interested in. We select the readout and control module we want to use.

# Connect to device
dev_id = connect.value
# Close the chosen QCodes instrument as to prevent name clash.
try:
    Instrument.find_instrument(names[dev_id]).close()
except KeyError:
    pass

cluster = Cluster(name=names[dev_id], identifier=ip_addresses[dev_id], debug=True) # TODO remove debug

print(f"{connect.label} connected")
print(cluster.get_system_state())

def select_module_widget(device, select_all=False, select_qrm_type: bool=True, select_rf_type: bool=False):
    """Create a widget to select modules of a certain type

    default is to show only QRM baseband

    Args:
        devices : Cluster we are currently using
        select_all (bool): ignore filters and show all modules
        select_qrm_type (bool): filter QRM/QCM
        select_rf_type (bool): filter RF/baseband
    """
    options = [[None, None]]


    for module in device.modules:
        if module.present():
            if select_all or (module.is_qrm_type == select_qrm_type and module.is_rf_type == select_rf_type):
                options.append(
                    [
                        f"{device.name} "
                        f"{module.short_name} "
                        f"({module.module_type}{'_RF' if module.is_rf_type else ''})",
                        module,
                    ]
                )
    widget = widgets.Dropdown(options=options)
    display(widget)

    return widget

print("Select the readout module from the available modules:")
select_readout_module = select_module_widget(cluster, select_qrm_type=True, select_rf_type=False)

readout_module = select_readout_module.value
print(f"{readout_module} connected")

print("Select the control module from the available modules:")
select_control_module = select_module_widget(cluster, select_qrm_type=False, select_rf_type=False)

control_module = select_control_module.value
print(f"{control_module} connected")

Reset the Cluster

We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters, so resetting between experiments is usually not desirable.

cluster.reset()
print(cluster.get_system_state())

Generate waveforms

Next, we need to create the waveforms for the sequence.

# Waveform parameters
waveform_length = 120  # nanoseconds

# Waveform dictionary (data will hold the samples and index will be used to select the waveforms in the instrument).
waveforms = {
    "gaussian": {
        "data": scipy.signal.gaussian(
            waveform_length, std=0.12 * waveform_length
        ).tolist(),
        "index": 0,
    },
    "sine": {
        "data": [
            math.sin((2 * math.pi / waveform_length) * i)
            for i in range(0, waveform_length)
        ],
        "index": 1,
    },
}

Let’s plot the waveforms to see what we have created.

time = numpy.arange(0, max(map(lambda d: len(d["data"]), waveforms.values())), 1)
fig, ax = matplotlib.pyplot.subplots(1, 1, figsize=(10, 10 / 1.61))

for wf, d in waveforms.items():
    ax.plot(time[: len(d["data"])], d["data"], ".-", linewidth=0.5, label=wf)

ax.legend(loc=4)
ax.yaxis.grid()
ax.xaxis.grid()
ax.set_ylabel("Waveform primitive amplitude")
ax.set_xlabel("Time (ns)")

matplotlib.pyplot.draw()
matplotlib.pyplot.show()

Specify acquisitions

We also need to specify the acquisitions so that the instrument can allocate the required memory for it’s acquisition list. In this case we will create one acquisition specification that creates a single bin. However, we will not be using the bin in this turorial.

# Acquisitions
acquisitions = {"measurement": {"num_bins": 1, "index": 0}}

Create Q1ASM programs

Now that we have the waveforms and acquisition specifications for the sequence, we need a simple Q1ASM program that sequences the waveforms in the QCM and acquires the waveforms in the QRM.

# QCM sequence program.
qcm_seq_prog = """
wait_sync 4         #Synchronize sequencers over multiple instruments.
play      0,1,16384 #Play waveforms and wait remaining duration of scope acquisition.
stop                #Stop.
"""

# QRM sequence program.
qrm_seq_prog = """
wait_sync 4         #Synchronize sequencers over multiple instruments.
acquire   0,0,16384 #Acquire waveforms and wait remaining duration of scope acquisition.
stop                #Stop.
"""

Upload sequences

Now that we have the waveforms and Q1ASM programs, we can combine them in the sequences stored in JSON files.

# Add QCM sequence to single dictionary and write to JSON file.
sequence = {
    "waveforms": waveforms,
    "weights": {},
    "acquisitions": acquisitions,
    "program": qcm_seq_prog,
}
with open("qcm_sequence.json", "w", encoding="utf-8") as file:
    json.dump(sequence, file, indent=4)
    file.close()

# Add QRM sequence to single dictionary and write to JSON file.
sequence = {
    "waveforms": waveforms,
    "weights": {},
    "acquisitions": acquisitions,
    "program": qrm_seq_prog,
}
with open("qrm_sequence.json", "w", encoding="utf-8") as file:
    json.dump(sequence, file, indent=4)
    file.close()

Let’s write the JSON file to the instruments. We will use sequencer 0 of both QCM and QRM, which will drive outputs \(\text{O}^{[1-2]}\) of the QCM and acquire on inputs \(\text{I}^{[1-2]}\) of the QRM.

# Upload waveforms and programs to QCM.
control_module.sequencer0.sequence("qcm_sequence.json")

# Upload waveforms and programs to QRM.
readout_module.sequencer0.sequence("qrm_sequence.json")

Play sequences

The sequence has been uploaded to the instruments. Now we need to configure the sequencers of both the QCM and QRM to use the wait_sync instruction to synchronise and we need to configure the sequencer of the QRM to trigger the acquisition with the acquire instruction. Furthermore we also need to attenuate the QCM’s outputs to 40% to be able to capture the full range of the waveforms on the QRM’s inputs.

\(\text{Attenuation}={Input}/{Output}={2V}/{5V}={0.4}\)

# Configure the sequencer of the QCM.
control_module.sequencer0.sync_en(True)
control_module.sequencer0.gain_awg_path0(0.35)  # Adding a bit of margin to the 0.4
control_module.sequencer0.gain_awg_path1(0.35)

# Map sequencer of the QCM to specific outputs (but first disable all sequencer connections)
for sequencer in control_module.sequencers:
    for out in range(0, 2):
        sequencer.set("channel_map_path{}_out{}_en".format(out % 2, out), False)
control_module.sequencer0.channel_map_path0_out0_en(True)
control_module.sequencer0.channel_map_path1_out1_en(True)

# Configure the scope acquisition of the QRM.
readout_module.scope_acq_sequencer_select(0)
readout_module.scope_acq_trigger_mode_path0("sequencer")
readout_module.scope_acq_trigger_mode_path1("sequencer")

# Configure the sequencer of the QRM.
readout_module.sequencer0.sync_en(True)

Now let’s start the sequences.

# Arm and start sequencer of the QCM (only sequencer 0).
control_module.arm_sequencer(0)
control_module.start_sequencer()

# Print status of sequencer of the QCM.
print("QCM status:")
print(control_module.get_sequencer_state(0))
print()

# Arm and start sequencer of the QRM (only sequencer 0).
readout_module.arm_sequencer(0)
readout_module.start_sequencer()

# Print status of sequencer of the QRM.
print("QRM status:")
print(readout_module.get_sequencer_state(0))

QCM status:
Status: Q1_STOPPED, Flags: NONE

QRM status:
Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE

Retrieve acquisition

The waveforms have now been sequenced on the outputs and acquired on the inputs by both instruments. And as you might have noticed, timing these operations was simplified significantly by the SYNQ technology. Lets retrieve the resulting data, but first let’s make sure the sequencers have finished.

# Wait for the QCM sequencer to stop with a timeout period of one minute.
control_module.get_sequencer_state(0, 1)

# Wait for the QRM acquisition to finish with a timeout period of one minute.
readout_module.get_acquisition_state(0, 1)

# Move acquisition data from temporary memory to acquisition list.
readout_module.store_scope_acquisition(0, "measurement")

# Get acquisition list from instrument.
acq = readout_module.get_acquisitions(0)

Let’s plot the result.

# Plot acquired signal on both inputs.
fig, ax = matplotlib.pyplot.subplots(1, 1, figsize=(15, 15 / 2 / 1.61))
ax.plot(acq["measurement"]["acquisition"]["scope"]["path0"]["data"][130:290])
ax.plot(acq["measurement"]["acquisition"]["scope"]["path1"]["data"][130:290])
ax.set_xlabel("Time (ns)")
ax.set_ylabel("Relative amplitude")
matplotlib.pyplot.show()

Stop

Finally, let’s stop the sequencers if they haven’t already and close the instrument connection. One can also display a detailed snapshot containing the instrument parameters before closing the connection by uncommenting the corresponding lines.

# Stop sequencers.
control_module.stop_sequencer()
readout_module.stop_sequencer()

# Print status of sequencers.
print("QCM:")
print(control_module.get_sequencer_state(0))
print()

print("QRM:")
print(readout_module.get_sequencer_state(0))
print()

# Uncomment the following to print an overview of the instrument parameters.
# Print an overview of instrument parameters.
# print("QCM snapshot:")
# qcm.print_readable_snapshot(update=True)
# print()

# print("QRM snapshot:")
# readout_module.print_readable_snapshot(update=True)

# Close the instrument connections.
Pulsar.close_all()
Cluster.close_all()