Advanced sequencing

In this tutorial we will demonstrate advanced sequencer based operations, where we focus on waveform parametrization (see Sequencer Operation). We will demonstrate this by creating a sequence that will show various sequencer features, including complex looping constructs, dynamic gain control, hardware-based modulation and marker output control.

The sequence itself will use four waveform envelopes with a duration of 1μs each; a gaussian, a sine, a sawtooth and a block. We will have several nested loops in the sequence. The first loop will increase the wait period between the start of the iteration and playback of the waveform envelope and also increase the gain of the waveform envelope on every iteration. At the end of this loop a second loop will do the inverse operations. A third loop will loop over the first and second loops to iterate over the four waveform envelopes. And finally a fourth loop will function as an infinite loop over the third loop. At the same time, the sequence will also control marker output 1 and create a trigger point at the start of each iteration of the first and second loops as well an “enable” during playback. Finally, each waveform envelope will be modulated at 10MHz.

The result of this sequence, when observed on an oscilloscope, will be iterating waveform envelopes that will be sliding over the modulation frequency with varying gain, encapsulated by an “enable” on the marker output. We highly recommend that you take a look at it, to get an impression of what is possible with the sequencers.

We can perform this tutorial with a Cluster QCM/QRM . We use the term ‘module’ encompassing both QCM and QRM modules.

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.

[1]:

'anything'
# Import ipython widgets
import json
import math
import os

import ipywidgets as widgets
import matplotlib.pyplot as plt
import numpy as np

# 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

Scan For Clusters

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).

[2]:

with PlugAndPlay() as p:
    # get info of all devices
    device_list = p.list_devices()

# Scan for available devices and display
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)

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. Note that you need to set select_qrm_type to False is you are using a qcm.

[ ]:

# 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

print(ip_addresses)
print(dev_id)
cluster = Cluster(name=names[dev_id], identifier=ip_addresses[dev_id])

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

[4]:

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

[5]:

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

Select the readout module from the available modules:

[ ]:

module = select_module.value
print(f"{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 gaussian, sine, sawtooth and block waveform envelopes for the sequence.

[6]:

# Waveform parameters
waveform_length = 1000  # 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.windows.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,
    },
    "sawtooth": {
        "data": [(1.0 / (waveform_length)) * i for i in range(0, waveform_length)],
        "index": 2,
    },
    "block": {"data": [1.0 for i in range(0, waveform_length)], "index": 3},
}

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

[7]:

time = np.arange(0, max(map(lambda d: len(d["data"]), waveforms.values())), 1)
fig, ax = plt.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.grid(alpha = 1/10)
ax.set_ylabel("Waveform primitive amplitude")
ax.set_xlabel("Time (ns)")

plt.draw()
plt.show()

../../_images/tutorials_q1asm_tutorials_advanced_sequencing_15_0.png

Create Q1ASM program

Now that we have the waveforms for the sequence, we need a Q1ASM program that sequences the waveforms as previously described.

[8]:

# Sequence program.
seq_prog = """
                  wait_sync    4                    #Wait for synchronization
                  reset_ph                          #Reset absolute phase
                  upd_param    4                    #Update all parameters and wait 4ns
start:            move         4,R0                 #Init number of waveforms
                  move         0,R1                 #Init waveform index

mult_wave_loop:   move         166,R2               #Init number of single wave loops (increasing wait)
                  move         166,R3               #Init number of single wave loops (decreasing wait)
                  move         24,R4                #Init number of dynamic wait time (total of 4us)
                  move         3976,R5              #Init number of dynamic wait time remainder
                  move         32768,R6             #Init gain (Maximum gain)

sngl_wave_loop_0: move         800,R7               #Init number of long wait loops (total of 40ms)
                  set_mrk      15                   #Set marker to 0xF
                  upd_param    4                    #Update all parameters and wait 4ns
                  set_mrk      0                    #Set marker to 0
                  upd_param    96                   #Update all parameters and wait 96ns

                  wait         R4                   #Dynamic wait
                  add          R4,24,R4             #Increase wait

                  set_mrk      1                    #Set marker to 1
                  play         R1,R1,996            #Play waveform and wait 996ns
                  set_mrk      0                    #Set marker to 0
                  upd_param    4                    #Update all parameters and wait for 4ns

                  wait         R5                   #Compensate previous dynamic wait
                  sub          R5,24,R5             #Decrease wait

                  sub          R6,98,R6             #Decrease gain
                  nop
                  set_awg_gain R6,R6                #Set gain

long_wait_loop_0: wait         50000                #Wait 50 us
                  loop         R7,@long_wait_loop_0 #Wait total of 40ms
                  loop         R2,@sngl_wave_loop_0 #Repeat single wave loop

sngl_wave_loop_1: move         800,R7               #Init number of long wait loops (total of 40ms)
                  set_mrk      15                   #Set marker to 0xF
                  upd_param    8                    #Update all parameters and wait 8ns
                  set_mrk      0                    #Set marker to 0
                  upd_param    92                   #Update all parameters and wait 92ns

                  wait         R4                   #Dynamic wait
                  sub          R4,24,R4             #Decrease wait

                  set_mrk      1                    #Set marker to 1
                  play         R1,R1,996            #Play waveform and wait 996ns
                  set_mrk      0                    #Set marker to 0
                  upd_param    4                    #Update all parameters and wait 4ns

                  wait         R5                   #Compensate previous dynamic wait
                  add          R5,24,R5             #Increase wait

                  sub          R6,98,R6             #Decrease gain
                  nop
                  set_awg_gain R6,R6                #Set gain

long_wait_loop_1: wait         50000                #Wait for 50 us
                  loop         R7,@long_wait_loop_1 #Wait total of 40ms
                  loop         R3,@sngl_wave_loop_1 #Repeat single wave loop

                  add          R1,1,R1              #Adjust waveform index
                  loop         R0,@mult_wave_loop   #Repeat with next waveform envelope
                  jmp          @start               #Repeat entire sequence
"""

Upload sequence

Now that we have the waveforms and Q1ASM program, we can combine them in a sequence stored in a JSON file.

[9]:

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

Let’s write the JSON file to the instruments. We will use sequencer 0 and 1, which will drive outputs \(\text{O}^{[1-2]}\) and \(\text{O}^{[3-4]}\) respectively.

[10]:

# Upload sequence.
module.sequencer0.sequence("sequence.json")
module.sequencer1.sequence("sequence.json")

Play sequence

The sequence has been uploaded to the instrument. Now we need to configure the sequencers in the instrument to use the wait_sync instruction at the start of the Q1ASM program to synchronize and to enable the hardware-based modulation at 10MHz.

[11]:

# Configure the sequencers to synchronize and enable modulation at 10MHz.
module.sequencer0.sync_en(True)
module.sequencer0.mod_en_awg(True)
module.sequencer0.nco_freq(10e6)
module.sequencer1.sync_en(True)
module.sequencer1.mod_en_awg(True)
module.sequencer1.nco_freq(10e6)

# Map sequencers to specific outputs (but first disable all sequencer connections).
module.disconnect_outputs()

# If it is a QRM, we only map sequencer 0 to the outputs.
module.sequencer0.connect_sequencer("out0_1")
if module.is_qcm_type:
    module.sequencer0.connect_sequencer("out2_3")

Now let’s start the sequence. If you want to observe the sequence, this is the time to connect an oscilloscope to marker output 1 and one or more of the four outputs. Configure the oscilloscope to trigger on marker output 1.

[12]:

# Arm and start sequencers.
module.arm_sequencer(0)
module.arm_sequencer(1)
module.start_sequencer()

# Print status of sequencers.
print(module.get_sequencer_state(0))
print(module.get_sequencer_state(1))

Status:
Status: RUNNING, Flags: NONE
Status: RUNNING, Flags: NONE

Before we continue, have you looked at the oscilloscope? Pretty nifty right? This is just an example. Imagine what else you can do with the power of the sequencers to control and/or speed up your experiments.

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 both sequencers.
module.stop_sequencer()

# Print status of both sequencers (should now say it is stopped).
print(module.get_sequencer_state(0))
print(module.get_sequencer_state(1))
print()

# Uncomment the following to print an overview of the instrument parameters.
# print("Snapshot:")
# module.print_readable_snapshot(update=True)

# Close all existing QCoDeS Instrument instances.
Instrument.close_all()