Release Notes#
Release v0.22.2 (2025-01-17)#
Bugfixes, compatibility with qblox-instruments v0.15.0, and more nv center element features. Some of the highlights include:
Fix gate-level pulse compensation operation compiling#
There was a bug with the PulseCompensation operation where the body argument wasn’t compiled recursively. This has now been fixed.
Fix v0.2 hardware config does not support multiplexing#
There was a bug where multiplexed readout was no longer supported in the v0.2 hardware config format. This has now been fixed.
Fix low amplitude long ramp pulses do not play#
There was a bug where long ramp pulses with a small slope the amplitude would be rounded to zero. This has now been fixed.
Fix BasicElectronicNVElement misses info when serialized#
When (de)serializing BasicElectronicNVElement objects using to_json and from_json, some of the qubit parameter values were missing. This has now been fixed.
New acquisition protocol: ThresholdedTriggerCount#
A new AcquisitionProtocol has been added which the user to threshold trigger counts. This can be used in combination with conditional playback to play instructions only when the trigger count is above a certain threshold. Example:
schedule = Schedule("example")
schedule.add(ThresholdedTriggerCount(
port="qe0:optical_readout",
clock="qe0.ge0",
duration=10e-6,
threshold=10,
feedback_trigger_label="qe0",
feedback_trigger_condition=TriggerCondition.GREATER_EQUAL,
)
)
sub_schedule = ...
schedule.add(ConditionalOperation(body=sub_schedule, qubit_name="qe0"), rel_time=...)
New bin mode SUM for TriggerCount#
A new bin mode SUM for TriggerCount has been added. This mode will sum the trigger counts over all runs. For example, a schedule that contains a single TriggerCount operation, while APPEND mode would return the trigger counts for each repetition of the schedule, the SUM mode will simply return the total number of times (across all repetitions) a trigger was counted. When using multiple TriggerCount operations, using multiple acquisition indices, the SUM mode will only sum the triggers specific to the acquisition index. For more information, please refer to the trigger count acquisition tutorial
Compatibility with qblox-instruments v0.15.0#
This release adds compatibility with qblox-instruments v0.15.0. For more information, please refer to the qblox-instruments release notes.
Release v0.22.1 (2024-11-21)#
A small bug fix to make the package compatible with the latest version of pydantic.
Release v0.22.0 (2024-11-20)#
Many updates and improvements in this version! Some of the highlights include:
We also fixed a few bugs:
Friendly imports#
To reduce the number of imports in your code, we have added friendly import paths for common quantify operations and other classes:
from quantify_scheduler import Schedule, QuantumDevice, ScheduleGettable, ...
from quantify_scheduler.operations import Measure, DRAGPulse, ConditionalOperation, Trace, ...
for Qblox users, similar convenience imports are
from quantify_scheduler.qblox import ClusterComponent, ...
from quantify_scheduler.qblox.operations import ConditionalReset, SimpleNumericalPulse, ...
To see a complete list of available imports, please refer to the API reference. e.g.:
More spin operations#
We have added a few more operations specific for spin-based elements. These include:
Improve conditional playback#
We have added support for generic subschedules in conditional playback. This allows for more flexible and reusable conditional logic in your schedules. For more details, please refer to the Conditional playback tutorial and the reference guide.
Breaking change
For Qblox users, this is a breaking change. For conditional operations please import from quantify_scheduler.qblox.operations:
from quantify_scheduler.qblox.operations import ConditionalOperation, ConditionalReset
Crosstalk compensation#
For Qblox users we have added support for cross talk compensation to remove unwanted interference and interactions between qubits. For example, if we have a 2 qubit device with qubits q0 and q1, we can define a cross talk matrix in the hardware configuration:
hardware_comp_cfg = {
...
"hardware_options": {
"modulation_frequencies": {
"q0:gt-q0.01": {"interm_freq": 7e9},
"q1:gt-q1.01": {"interm_freq": 7e9},
},
"crosstalk_compensation_enable": True,
"crosstalk": {
"q0:gt-q0.01": {
"q1:gt-q1.01": 0.5
},
"q1:gt-q1.01": {
"q0:gt-q0.01": 0.5
}
},
},
"connectivity": {
"graph": [
("cluster0.module2.real_output_0", "q0:gt"),
("cluster0.module2.real_output_1", "q1:gt"),
]
},
}
Which will compensate pulses using a linear combination of weights:
where \(f(t)\) and \(g(t)\) are the original pulses that were scheduled on qubits q0 and q1 respectively.
Version 0.2 of the Qblox hardware compilation config#
The Qblox hardware compilation configuration is now versioned to make it easier for us to make changes without breaking existing code. v0.2 brings a behavioral change compared to v0.1 (and unversioned), where Measure operations will require to have both input and output channels specified.
For example, previously:
{
"version": "0.1", #<-- this field is optional
"config_type": "quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig",
"hardware_description": {
"cluster0": {
"instrument_type": "Cluster",
"ref": "internal",
"ip": None,
"modules": {"1": {"instrument_type": "QCM"}},
}
},
"hardware_options": {},
"connectivity": {
"graph": [["cluster0.module1.complex_output_0", "q0:mw"]]
}
}
complex_output_0 in the connectivity would imply both input and output channels. In v0.2, this will raise an error, as the compiler will not know which channel to use as input and which as output. The user will have to specify this explicitly:
{
"version": "0.2",
"config_type": "quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig",
"hardware_description": {
"cluster0": {
"instrument_type": "Cluster",
"ref": "internal",
"ip": None,
"modules": {
"1": {"instrument_type": "QRM"},
"2": {"instrument_type": "QRM_RF"},
},
}
},
"hardware_options": {},
"connectivity": {
"graph": [
["cluster0.module1.real_output_0", "q0:res"],
["cluster0.module1.real_input_1", "q0:res"],
["cluster0.module2.complex_output_0", "q1:res"],
["cluster0.module2.complex_input_0", "q1:res"],
]
}
}
See the Qblox hardware compilation config reference guide for more details.
ScheduleGettable optionally returns xarray.DataSet#
We introduced a new boolean parameter return_xarray for the ScheduleGettable class. When set to True, the ScheduleGettable will return an xarray.DataSet object instead of a list of numpy arrays:
gettable = ScheduleGettable(
single_qubit_device,
schedule_function=schedule_function,
schedule_kwargs=schedule_kwargs,
batched=True,
return_xarray=True,
)
Breaking change
This is a breaking change for quantify-core users. In order to use MeasurementControl with quantify-scheduler v0.22.0 or later, you will have to update quantify-core as well to v0.7.9 or later.
Fine delay support in QTM instructions#
We have added support for fine delay in QTM instructions. This allows for picosecond control of the MarkerPulse operation. e.g.
schedule.add(
MarkerPulse(
duration=40e-9,
fine_start_delay=391e-12,
fine_end_delay=781e-12,
port="qe1:switch",
)
)
the fine delay parameters should be multiples of 1/128 ns.
New bin mode for trigger count#
We have renamed a bin mode for trigger count. The AVERAGE mode has been replaced with DISTRIBUTION. The DISTRIBUTION mode records the number of times the trigger condition was met.
For example, if a schedule ran 8 times and we measured:
in 1 run, the trigger was counted 5 times,
in 2 runs, the trigger was counted 4 times,
in 3 runs, the trigger was counted 2 times,
in 2 runs, the trigger was counted once.
a trigger count acquisition, together with the DISTRIBUTION bin mode, will return a dataset of the following form:
<xarray.Dataset> Size: 72B
Dimensions: (repetition: 1, counts: 4)
Coordinates:
* repetition (repetition) int64 8B 0
* counts (counts) int64 32B 5 4 2 1
Data variables:
0 (repetition, counts) int64 32B 1 2 3 2For more details, please visit the Trigger count reference guide.
Improve plotting of final data point#
Due to interpolation, the last data point in most waveform plots were slightly off. This was most visible in ramp and staircase pulses. This has now been fixed.
e.g. a before and after plot for a RampPulse and GaussPulse:
Allow user to change rel_tolerance in to_grid_time function#
Sometimes floating point errors accumulate causing the to_grid_time to raise a ValueError if the time is not a multiple of the grid time within the default tolerance:
ValueError: Attempting to use a time value of 335872000000.0059 ns.
Please ensure that the durations of operations and wait times between
operations are multiples of 4 ns (tolerance: 1e-03 ns).
You can now lower the tolerance by setting constants.GRID_TIME_TOLERANCE_TIME to a larger value, e.g.
from quantify_scheduler.backends.qblox import constants
constants.GRID_TIME_TOLERANCE_TIME = 0.1
The default is set to 0.0011 ns.
Fix unused digitization_thresholds#
When setting the digitization thresholds in the hardware options, e.g.
hardware_options = {
"digitization_thresholds": {
"qe0:optical_readout-qe0.ge0": {"in_threshold_primary": 0.5}
}
}
These were not properly propagated to the hardware. This is now fixed.
Fix thresholded NaN values in Qblox backend#
In some cases NaN values are returned by acquisitions. For example when using a dummy hardware configuration, or when scheduling acquisitions inside a conditional operation. This would lead to a ValueError when trying to convert NaN to an integer. This is now fixed, by mapping them to -1.
More serialization features#
We have added more (de)serialization features to allow for more flexible and reusable schedules. We can now use to_json, from_json, to_json_file and from_json_file on QuantumDevice, DeviceElement (e.g. BasicTransmonElement, BasicNVCenterElement, & BasicSpinElement) and Schedule objects.
For more examples, please refer to the Serialization page.
Reloading hardware config#
In additional to serializing the quantum device, we can now also (re)load hardware configurations from JSON files:
from quantify_scheduler import QuantumDevice
quantum_device = QuantumDevice("")
quantum_device.hardware_config.load_from_json_file("path/to/hardware_config.json")
Similarly, we can serialize the hardware configuration to a JSON file:
quantum_device.hardware_config.write_to_json_file("path/to/hardware_config.json")