Demo 1: Quantum Interference¶
This notebook runs on Oqtant hardware and uses 9 jobs
Introduction¶
One of the most striking features demonstrated by quantum matter is that of interference, a consequence of the wavelike and coherent nature of quantum systems. Interference is the fundamental phenomena that enables the burgeoning field of quantum sensing. Many of the world's most sensitive measurements of gravitational waves, accelerations, rotations, gravity, magnetic fields and time all leverage quantum interference and the interferometry techniques built upon it. We can start exploring this vast and fascinating topic using Oqtant QMS.
To demonstrate interference, we need to split the quantum matter into $\geq 1$ distinct spatial regions, release the trapping potential so that the spatially-separated samples can expand and overlap, and detect the interference by taking an image. With Barrier objects and time-of-flight imaging, we have all the tools necessary to accomplish this demonstration.
Note: For further reading, see e.g. the 1997 paper Observation of Interference Between Two Bose Condensates which used an experimental setup very similar to the hardware accessible by Oqtant QMS.
Imports and user authentication¶
from oqtant.schemas.quantum_matter import QuantumMatterFactory
qmf = QuantumMatterFactory()
Authenticate automatically¶
The easiest way to authenticate as an Oqtant user is to execute the following cell. This will activate a widget that will let you sign in.
If popups are blocked, or you are on a system that does not support this method, please follow the steps just below to authenticate manually instead.
Note that currently the automatic authentication method is only compatible with classic jupyter notebooks, and not jupyter lab, nanohub, binder, colab, etc.
qmf.get_login()
Authenticate manually¶
If you cannot use the automatic authentication method above, you can instead authenticate using a slightly more manual process:
- Copy your access token from oqtant.infleqtion.com/oqtantAPI
- Paste that token just below and execute the cell (the if statement keeps the code from executing if you already authenticated above)
if qmf.login.access_token == "":
qmf.login.access_token = "Paste your token here between the quotes!"
Get client¶
At this point you should have a valid access token and be able to establish a client for communicating with the Oqtant REST service. Executing the cell just below should show your current job quota limits.
qmf.get_client()
Split quantum matter into two components¶
First, we will create a barrier that will be used to split our atom ensemble into two distinct spatial regions. The goal is to gently separate the cloud by slowly increasing the barrier height so that the quantum matter isn't heated or excited in the process. On the other hand, the process can't be too slow because the quantum matter clouds on either side of the barrier need to remain coherent. After splitting the cloud, the barrier and overall (magnetic) trapping potential is turned off to allow the clouds to recombine as they fall under gravity.
barrier = qmf.create_barrier(
positions=[0, 0], # fine tune to split the cloud into roughly equal parts
heights=[0, 12],
widths=[4, 4], # adjust as free parameter to optimize observed interference
times=[0, 6],
)
# barrier.evolve(duration=2) # can also hold the barrier for some time (after ramp-on) to explore coherence time
barrier.show_dynamics()
To explore the emergence and evolution of quantum interference, the barrier height, width, shape, ramp-on, and optional hold-time (after ramping on) can all be varied. Another useful parameter to explore is the temperature of the quantum matter before the barrier is introduced.
In-trap image to verify cloud has been split¶
Next, we create a QuantumMatter object to image the atom ensemble in-trap.
matter_in_trap = qmf.create_quantum_matter(
temperature=50, # another free parameter available for optimizing observed interference
lifetime=barrier.death,
barriers=[barrier],
image="IN_TRAP",
)
matter_in_trap.show_potential(times=[0, 3, 6], ylimits=[-2, 40])
We next submit our QuantumMatter object to the client to create a job, wait for the job to execute, and fetch the results:
matter_in_trap.submit(track=True)
Submitting 1 job(s): - Job: quantum matter Job ID: b6b7f389-9652-484d-861f-ae293729b811 Tracking 1 job(s): - Job: quantum matter - RUNNING - COMPLETE All job(s) complete
Plot the resulting in-trap image¶
matter_in_trap.get_result()
matter_in_trap.output.plot_it(figsize=(6, 6))
Observe interference in time-of-flight imaging¶
Next, let's look at the atom ensemble after releasing it from the trap. As the two regions of the quantum matter expand during the (adjustable) time of flight, they begin to spatially overlap, which results in interference.
tofs = [5, 7, 9, 11, 13, 15, 17, 19]
tof_matters = []
for tof in tofs:
tof_matters.append(
qmf.create_quantum_matter(
temperature=50,
barriers=[barrier],
lifetime=barrier.death,
image="TIME_OF_FLIGHT",
time_of_flight=tof,
name="interference w/ tof = " + str(tof) + " ms",
)
)
Submit to QMS and retrieve results, placing the resulting jobs (with outputs) in a list
for matter in tof_matters:
matter.submit()
Submitting 1 job(s): - Job: interference w/ tof = 5 ms Job ID: 6eed37b3-3cbe-4e84-b194-b018552e2d71 Submitting 1 job(s): - Job: interference w/ tof = 7 ms Job ID: bf087c4a-ded1-4e20-95f0-113ad211ac9d Submitting 1 job(s): - Job: interference w/ tof = 9 ms Job ID: e3fbe320-9662-43f2-a389-6115bd5b8ca9 Submitting 1 job(s): - Job: interference w/ tof = 11 ms Job ID: ea22440b-5b8f-49b5-a24e-7e3cc34cb8db Submitting 1 job(s): - Job: interference w/ tof = 13 ms Job ID: 277943dd-9b6e-408f-a42a-9b298745e225 Submitting 1 job(s): - Job: interference w/ tof = 15 ms Job ID: d956893a-cf11-497b-afcd-d5652af6cb2d Submitting 1 job(s): - Job: interference w/ tof = 17 ms Job ID: e141c09b-d71a-42db-b85c-f57ab2bf6f07 Submitting 1 job(s): - Job: interference w/ tof = 19 ms Job ID: fb379e00-12de-4780-aa82-6adfa4b0ddab
Once our sequence of interference jobs are complete and fetched, we can observe the overlapping quantum matter. If your inputs above are tuned correctly, you should observe the onset of quantum interference!
import matplotlib.pyplot as plt
pending_matters = [*tof_matters]
while pending_matters:
for index, matter in enumerate(pending_matters):
matter.get_result()
if matter.status == "COMPLETE":
pending_matters.pop(index)
matter.output.plot_tof(figsize=(6, 6))
The cross sections of the overlapping quantum matter clouds is often particularly revealing:
for matter in tof_matters:
matter.output.plot_slice(axis="x")
