Pulsed ODMR for Color Centers#

Introducing Color Centers#

One of the commonly known color centers is Nitrogen-Vacancy (NV) centers in diamond. They are unique quantum systems with applications in quantum computing, sensing, and communications. In this notebook, we are going to use the qblox hardware to tune-up NV center qubit.

To use a NV-center as a quantum device, it must first be tuned and characterized. The process depends on the specific color center and involves sequential experiments, where each step builds on the results of the previous one. The workflow for tuning a color center is illustrated in the accompanying figure.

In this diagram, arrows indicate dependencies, showing that experiments are typically conducted from left to right.

One key experiment in characterizing color centers is Optically Detected Magnetic Resonance (ODMR), which is analogous to qubit spectroscopy. This notebook demonstrate two crucial methods, Pulsed ODMR and Pulsed ODMR with power, to measure the energy levels of an NV center.

[1]:

from __future__ import annotations  # noqa: I001
from typing import Any

from dependencies.data_analysis import odmr_curve_fit

import pickle

from qblox_scheduler import HardwareAgent, Schedule

from qblox_scheduler.operations.expressions import DType
from qblox_scheduler.operations.loop_domains import linspace
from qblox_scheduler.operations import Measure, SetClockFrequency, SquarePulse
from qblox_scheduler.enums import BinMode

import json

/.venv/lib/python3.14/site-packages/quantify_core/utilities/general.py:13: QCoDeSDeprecationWarning: The `qcodes.utils.helpers` module is deprecated. Please consult the api documentation at https://microsoft.github.io/Qcodes/api/index.html for alternatives.
  from qcodes.utils.helpers import NumpyJSONEncoder

Initializing the HardwareAgent#

Here we initialize our HardwareAgent using our cluster (Qblox hardware) and quantum device (qubit) parameters - see this start-up guide for NV centers and the Qblox Scheduler Hello World for further details.

In short, the Hardware Configuration file contains the information specific to Qblox hardware, while the Device Configuration file (QuantumDevice) contains device elements (BasicElectronicNVElement), where we save the various qubit parameters. Here we are loading a template for 1 qubit.

[2]:

## Set to True if not using real cluster
using_dummy: bool = True

## Set to True if using QTM for counting photons, if using QRM set to False
using_qtm: bool = True

# Device Config
device_path = "dependencies/configs/nv_center1q.yaml"

# Hardware config — single base file, patched per setup
with open("dependencies/configs/nv_center_hw.json") as f:
    hw_config: dict[str, Any] = json.load(f)

match (using_dummy, using_qtm):
    case (True, _):
        pass  # Base config is ready for dummy use

    case (False, True):
        hw_config["hardware_description"]["cluster0"]["ip"] = "10.10.200.42"
        del hw_config["hardware_description"]["cluster0"]["modules"]
        # Adjust QTM input to match physical wiring
        hw_config["connectivity"]["graph"] = [
            ["cluster0.module10.digital_input_5", "qe0:optical_readout"]
            if edge[1] == "qe0:optical_readout"
            else edge
            for edge in hw_config["connectivity"]["graph"]
        ]

    case (False, False):
        hw_config["hardware_description"]["cluster0"]["ip"] = "10.10.200.42"
        del hw_config["hardware_description"]["cluster0"]["modules"]
        # QRM handles optical readout instead of QTM
        hw_config["connectivity"]["graph"] = [
            ["cluster0.module4.real_input_0", "qe0:optical_readout"]
            if edge[1] == "qe0:optical_readout"
            else edge
            for edge in hw_config["connectivity"]["graph"]
        ]
        hw_config["hardware_options"]["sequencer_options"] = {
            "qe0:optical_readout-qe0.ge0": {"ttl_acq_threshold": 0.5}
        }
        hw_config["hardware_options"]["digitization_thresholds"] = {
            "qtm0:in-digital": {"analog_threshold": 0.8},
            "qtm1:in-digital": {"analog_threshold": 0.8},
        }

[3]:

# Create a new hardware agent based on the configuration files discussed above
hw_agent = HardwareAgent(hardware_configuration=hw_config, quantum_device_configuration=device_path)

hw_agent.connect_clusters()

# Create an alias for a specific element of the DUT to conveniently interact with its properties
qubit = hw_agent.quantum_device.get_element("qe0")

/.venv/lib/python3.14/site-packages/qblox_scheduler/qblox/hardware_agent.py:499: UserWarning: cluster0: Trying to instantiate cluster with ip 'None'.Creating a dummy cluster.
  warnings.warn(

Analysis#

Once we have measured the pulsed ODMR, the acquired data can be fitted to find the qubit transition frequency.

Here we analyze the experiment data taken for NV center at Fraunhofer IAF, Freiburg Germany with our modules.

[10]:

cl = hw_agent.get_clusters()


# If using a dummy cluster, load the dataset from the example data
if list(cl.items())[0][1].is_dummy:
    with open("dependencies/pulsed_odmr_nv_center_qubit/pulsedodmr_one_amp_.pkl", "rb") as file:
        odmr_dataset = pickle.load(file)

transition_freq = odmr_curve_fit(odmr_dataset)

../../../_images/applications_color_centers_color_centers_pulsed_odmr_nv_center_qubit_18_0.png

Transition Frequency: 2.87220 GHz with Contrast: 9.32%

[11]:

# Update the qubit frequency
qubit.clock_freqs.spec = transition_freq * 1e9

Pulsed ODMR for Color Centers#

Introducing Color Centers#

Initializing the HardwareAgent#

Pulsed ODMR Schedule#

Analysis#

Pulsed ODMR versus Power Schedule#