Відео

Summary Professor Fabricio BA introduces ultracold atoms in quantum technologies, focusing on experimental methods and their applications in quantum physics. Highlights - 🔬 Introduction to ultracold atoms and their significance in quantum technologies. - 🎓 Emphasis on experimental perspectives and methodologies in ultracold atom research. - 📏 Overview of quantum degenerate states and their importance in the field. - ⚖ Discussion on precision measurements using ultracold atoms. - 🧪 Exploration of ultracold atoms in studying quantum chemistry. - 🌡 Explanation of reaching quantum degeneracy through laser and evaporative cooling techniques. - 🔭 Techniques for trapping and imaging ultracold atomic gases. Key Insights - 🔄 **Quantum Degeneracy**: Ultracold atoms allow exploration of quantum degeneracy, where quantum effects dominate over classical mechanics, essential for understanding many-body systems. - 🧬 **Precision Measurements**: Ultracold atoms are pivotal in precision measurements, aiding in advancements in timekeeping and gravitational force studies, crucial for quantum technologies. - 🔍 **Experimental Techniques**: The use of laser cooling and evaporative cooling is vital for reaching the nano-kelvin temperatures necessary for creating Bose-Einstein condensates and Fermi gases. - 🌌 **Quantum Chemistry**: Ultracold atoms serve as building blocks to investigate quantum-level interactions and chemical bonds, providing insights into complex molecular systems. - ⚡ **Trapping Mechanisms**: Optical dipole traps play a significant role in isolating ultracold atoms from external influences, enabling detailed experimental studies without environmental noise. - 📈 **Imaging Techniques**: Time-of-flight imaging provides crucial data about the momentum distribution of atoms, essential for characterizing their quantum states and behaviors. - 🎉 **Bose-Einstein Condensation**: The emergence of Bose-Einstein condensates marks a significant milestone in ultracold atom research, demonstrating collective quantum phenomena.

man got a stroke trying to answer the first quesion haha

33:20 very good point x2

Can models using quantum quench and entanglement predict if rockets repeatedly penetrating the atmosphere, damage the electromagnetic field and the stratosphere of earth?

Nice.

5:00 isn't exactly clear. If I wish to have 10^14 extrinsic carrier concentration, the Fermi level should to which level? What are the red and blue lines for? No image source either :(

7:48

Can it be used to simulate the behvaiour of interface

脳とトポロジーと数理物理学

Is the handout still available somewhere?

9:38 🥶🥶

*Can it predict the simple FT-IR spectrum of H2O?*

beware of 36:14 headphone and earbud users, the sound explodes and i am now deaf

Thanks for this session.I learn a lot about the application of Classical Machine Learning for Quantum Physics.

10:08 what stock does that equation tell us to buy?

why does the french like the monsters more than a french film? perhaps someday when confronted by the future and past will be to the eyes that remembered truely.

Yay🎉 thanks for uploading the video ❤. It's amazing 👏👏👍

is it possible that fixed points in and out of equilibrium is a remainder to volume weight in [critical dimension -rate-] of expansion? in liquid nitrogen in a state of -out of equilibrium- the air inside the balloon become contour weight to volume capacity-volume weight to nitrogen in greater ratio (critical dimension). surface dimension is at a state of dystention to volume nitrogen.

why is this argument inclusive to temperature fluctuation and not surface dystention to equilibrium? if temperature fluctuations from advent gaseous material is said to increase then what does science say to volume weight to surface dystention?

why is chaos accepted if proverbiality could be incurred? disorder system... to inherent properties? if the known natural state was not to accept atmospheric condition to sodium metal then wouldn't porous material be a problem to equilibrium? what is disorder system than a system in proverbial dissimilar dimension?

Oh well thanks for your lecture. I actually applied for one year postgraduate programme in same condensed matter and statistical. I am from Pakistan, Quaid-i-Azam university islamabad. For 2024-2025 session. I hope i will be short listed...

Guessing 575563

Hi, can you please upload lectures of the following courses. It would be really helpful and beneficial for all. 1. Linux Basics (CMP-LB) M. Stella and C. Egan (5 lectures of 1.5 h each) 2. Scientific Python (CMP-SP) I. Davidenkova (20 lectures of 1.5 h each) Thank you very much.

Thank you for the discussion

22:11

Thank you.

Some French seeped through on the second slide: si=if Also, fois=times

4:48

Nice

Awesome content! Thanks a lot for sharing it!

Nice

This is great!

An excellent talk!

Excuse me for inquiring, could you please clarify if pythTB has the capability to handle irregular-shaped supercells, such as a hexagonal supercell?

This is very good. Thanks

Can someone 'explain', in layman's terms, why the quaternions created the 'symplectic' structures (No3 after reals, and complex; t=4634 and lecture 2 at t=344). All the wikipedia articles etc., start with the high-falutin deep maths definitions. I'd like to come at it from the growing out of the real, then complex, then quaternion stepping stones (i.e. quaternions as rotation and expansion of the sphere [while complex is in the Argand 2d plane], with maybe a bit of relativity for 'grounding in reality' ;-). Plus maybe a bit about 'self-dual quaternions' for the win. Coming via bi-complex ideas is also a possibility. (see XKCD Purity for the gap ;-)

Lovely. Like the similarity with the radial Rayleigh distribution. Too much looking at optical spot point spread functions as a Gaussian!

Appreciated for sharing !!!!!!!!! Great content.

Woow....

Sorry can’t listen through this

Cover the Mike when you clear your throat please

Summary of “C. Wetterich, Phys. Lett. B 301 (1993) 90” I guess? But I wanna see author’s analysis : how k’^2=2k^2ln(\frac{ u}{k^2}+1) derived from constant solution h_a(x)=h\delta_a1 with (2 u -\mu^2 + \frac{1}{2}\lambda h^2)h = 2 u\phi, P511 in Nucl. Phys. B334 (1990) 506.

The Best!!

thank you so much for uploading this lecture. Very clear and helpful.

It makes it very clear why MERA of a few layers would be useful for language models. Because in a sentence the important correlations are not necessarily nearest neighbor, but maybe 10th neighbor. It is still short range. A few layers of MERA gets you the potential to capture such finite length cells structure.

The clearest introduction that I found

👍🏼

is the package compatible with spin polarization scf calculations

I understood everything until @0:01

The description seems incorrect; the speaker is Soonwon Choi from MIT. This also happens to a few other videos from this list.