Hi, and welcome to the next module on quantum sensing.
In this module, we will look at applications of quantum radar, quantum lidar,
and how to implement a TDC application on the FPGA of a digitizer.
Let's get started.
A quantum sensing application called quantum elimination
plays an important role for new radar technologies.
This research has gained attention due to the possible improvements
to sensitivity of radar and other target detection technologies.
The approach takes advantage of strong signal correlations
that can be created in electromagnetic beams
using quantum processes.
These quantum correlations are a form of quantum entanglement
giving advantage to the detection process.
To get a higher range in sensitivity of stealth objects,
we would look at strong quantum correlations.
The path to detect unknown objects through air requires a method
known as two-mode squeezing.
An entanglement source emits two frequency beams
called a signal and an idler.
The entanglement then helps with the signal-to-noise ratio
via the correlations inherent in entanglement.
The idler is immediately measured, then kept at some base station,
while the signal is sent to a probe toward the unknown stealth object.
The receiving signals from the environment
both pass through a receiver.
The two signals are then compared or read for post processing.
There's a lot of hype around this topic of quantum radar.
To add a disclaimer,
this research field still requires a lot of work
to prove that quantum radar is still more sensitive than classical radar.
There's work being done at the University of Waterloo
in research of quantum illumination.
On the left, you will see two-mode squeezing
created in the two entanglement beams as input into a radar system.
Quantum illumination seems to be very robust to the presence
of background noise and loss,
suggesting broader practical applications.
In this research,
they demonstrated proof of concept to quantum enhancement
in the detected signal-to-noise ratio of an order of magnitude
when comparing the performance of an entangled photon source
to an ideal classical noise source that saturates the classical bound for correlation.
You can see that it involves the use of a Josephson parametric amplifier,
used as a microwave parametric downconversion source,
along with classical amplifiers to measure the quadratures
of the signal and idler.
In this case, they measured the frequency modes of the JPA
between 4 and 8 GHz.
The quantumness is characterized as a function of the output power,
shown in the figure to the left.
This figure demonstrates an Eigenvalue of less than 1,
implying that they are entangled,
as described by the test within the group of Chang et al.
On the right is the same experiment
but a more detailed schematic setup
from the group at the Institute of Quantum Computing.
At the heart is the amplified quantum source
to generate the entanglement.
This device sits in a dilution refrigerator at 4 K,
which limits how these devices can be used in the real world.
For example, an ideal quantum radar device would be done at room temperature.
But there's still work that needs to be done
for this research to bring it into the real world
outside of the cryogenic chamber.
This is the beginning of a new area that could affect, say,
the aerospace and defense industries.
The other topic in this module is quantum lidar.
Lidar, which is an acronym for light detection and ranging,
is a remote sensing method that uses light in the form of pulsed lasers
to measure ranges.
Quantum lidar has many advantages over some of the classical radar systems
that are used today.
When compared to classical radar, which uses pulsed RF for detection,
lidar uses pulsed photons to generate precise,
three-dimensional information about the shape of objects
and their surface characteristics.
Another improvement that lidar has is its sensitivity.
This is because of the squeezed-state quantum effects that make for
better resolution, strong anti-interference ability,
and good low-altitude detection.
A lidar experiment consists of these core components:
a pulsed laser to generate optical pulses,
lenses to collect scattered photons,
a single photon-sensitive detector like a photomultiplier tube
or an avalanche photodiode,
and processing electronics for timestamping and counting photons.
Here is what that experiment might look like
to transmit and receive these photons
after they interact with an object in the environment.
The hardware that is used here demonstrates a TDC implementation
on a PXI digitizer with custom FPGA code.
PathWave FPGA is software that gives us access to program
the open FPGA sandbox region of the M3102A digitizer.
Through this software,
we can customize our FPGA design from a graphical user interface.
Our custom FPGA design was implemented and was created from a combination
of standard PathWave FPGA IP blocks, Vivado IP blocks, and VHDL blocks.
These blocks are wired together and completed into a bitfile
using the PathWave FPGA GUI.
The basic idea behind implementing the pulse counting
on a digitizer is as follows.
First, we set a voltage threshold that allows us to determine
when a pulsed edge occurs.
The frequency at which the capture pulses is based on
is the clock rate of the FPGA within the digitizer.
In this case, our demonstration, the clock rate is 100 MHz.
This means we can count 1 pulse per 10 ns.
For time tagging, we can increase the time resolution beyond 10 ns.
We can achieve this in two parts.
First, the FPGA oversamples the data by five,
effectively leading to a 500 MSa/s sampling rate.
This gives us 2 ns between each sample.
Secondly, the linear interpolation is implemented based on
the user-defined voltage threshold and the sample voltage
before and after the threshold crossing.
This improves our time resolution significantly,
based on the properties of the time-interpolator algorithm.
Here's our experimental setup.
It's quite simple.
There's channel 1 of an M3201A AWG
that is connected to channel 2 of the M3102A digitizer.
The AWG generates a train of square pulses
to simulate what you may receive from a photon detector.
The digitizer is loaded with our custom TDC bitfile
generated by PathWave FPGA.
A simplified version of the FPGA design is shown here.
Analog data from channel 2 of the digitizer
is sent down two paths of the sandbox.
In the first path,
the data is sent to the data acquisition block
associated with channel 2,
just as it would be in a standard digitizer design.
In the second path,
the data is sent to the custom TDC block.
The block implements pulse counting and timestamping
of the channel 2 data.
The TDC settings are configured from an FPGA memory-mapped block
that is accessible through Python API.
The output of the TDC is then routed to the data acquisition block
typically associated with channel 1 of the digitizer.
By using both data acquisition blocks,
we can read the standard analog input data and the TDC simultaneously.
This wraps it up for our lesson on quantum radar and quantum lidar.
Quantum radar and quantum lidar are very much on the horizon
and have the probability to change the industry
for classical radar applications.
Thank you for listening to this module.
I'll see you at the next one.