2022 in review

Quite a lot happened this year:

1. Travel has returned to pre-covid normalcy, and I even had the chance to attend an in-person conference in Korea. Online is no substitute for the discussions that take place in the breaks between talks. I am glad that our students have also had the chance to travel abroad for inspiring conferences (ICOAM and QTML).

2. In academia it is hard to say no - we are always enticed by opportunities to get another paper, get more citations, increase our h-index. In the first half of the year I was incredibly overworked, supervising several PhD students while trying to find time to finish my own projects. After finishing my two overdue review articles in July I decided to cut back on commitments so I would have time to properly supervise students. This was a great success, and it's quite liberating not having to care about getting just one more paper in PRL/Nature/whatever.

3. I have now worked a full year as a remote editor for Physical Review A, handling over 300 submissions. This has been a great learning experience and has given me a better appreciation for how peer review can improve the quality and rigor of research articles. Sadly it is a minority of researchers who are willing to offer their time to provide well-crafted, thoughtful reports. It is promising to see that publishers including APS and Optica are providing more resources for referees, particularly early career researchers. It would be good to see referee training integrated directly into graduate research programs.

4. Machine learning models for image generation (such as Stable Diffusion) and text generation (ChatGPT) are going to change the world. There's no putting the genie back into the bottle now that anyone can download the trained model weights in a few minutes and run them on their own personal computer (InvokeAI doesn't even require a high end GPU!). Some professions such as graphic artists will be irrevocably changed. Still, the models are not perfect and they often fail in subtle and unpredictable ways, requiring human vetting. Thus, at least in the near term they will be primarily used to enhance productivity, not destroy entire professions.

5. In quantum computing, the most exciting developments for me were several groups proposing efficient classical algorithms for spoofing the results of random quantum circuit sampling experiments and debates over quantum supremacy using quantum topological data analysis.

Stay tuned next year for more on flat bands, Weyl semimetals, (quantum) machine learning, quantum scars, and more blogging. Happy 2023!

Quantum circuit simulation using Julia

For better or worse, most researchers working in quantum computing write and simulate quantum circuits using python libraries such as Qiskit, Amazon Braket, QuTip, and Qibo. 

Python is great for prototyping, particularly when one can cobble together existing libraries that call low level C or Fortran code to perform the most time-consuming parts of the computation. However the performance of Python code will inevitably be worse than optimised code written for compiled languages. 

This is usually an acceptable tradeoff for physicists - we prefer to do physics over low level code optimization and debugging. Unfortunately state-of-the-art quantum processors have reached a scale where numerically simulating them is extremely slow and (arguably) intractable for classical computers. Cue waiting for hours or even days for code to run.

That's why many researchers are starting to use Julia for simulating quantum circuits. Julia uses just-in-time compilation to achieve speeds comparable to C without sacrificing being easy to write and debug. As an example, the figure below (taken from arXiv:221209537) demonstrates computation of matrix permanents (crucial for simulation of BosonSampling experiments) two orders of magnitude faster than Matlab and Python implementations!

 Other Julia libraries for quantum being developed include Yao.jl (differentiable quantum circuit simulation), QuantumCumulants.jl (simulation of open quantum systems), QXTools (distributed tensor network simulations of quantum circuits), and Quiqbox.jl (computational quantum chemistry subroutines). For more, see this list of open source quantum software projects.

I have tested Julia on a few photonics-specific problems, including the numerical solutions of the nonlinear Schrodinger equation and photonic band structure calculations. I particularly liked the ease of translating mathematical expressions to working code (for example, Julia supports unicode variables), ability to easily parallelize code, and (optional) type declarations for improving performance and debugging. 

Why not give Julia a try for your next project?

arXiv backlog

I've been too busy finishing papers to carefully read the arXiv postings that looked interesting enough to download. Here is what's caught my attention:

• Textbook myths about early atomic models. The plum pudding model is fake news!
• Topological packing statistics distinguish living and non-living matter. This work develops a method to distinguish different types of point clouds using their distributions of local structural features. Seems like a promising new alternative to persistent homology.
• Strong intensity noise condensation using nonlinear dispersive loss in semiconductor lasers. A follow-up to the group's Fock state laser paper, which seems to be still under review.
• Optical isolation by temporal modulation: size, frequency, and power constraints. The third in a series of perspective-style anti-hype articles by Jacob Khurgin (previous ones on nonlinear optics and high refractive index materials), this one targeting the booming field of temporal metamaterials. Take a look at the entertaining introduction!
• A (simple) classical algorithm for estimating Betti numbers. An efficient path integral Monte Carlo algorithm constrains the region where quantum algorithms for Betti number estimation might provide a quantum advantage.
• Matters Arising: Time-reversal-based quantum metrology with
  many-body entangled states. Is entanglement-enhanced metrology a sham?
  

Students on strike

Graduate students in California have been on strike for three weeks, pushing for a livable wage.

The cost of living in California is amongst the highest in the USA, partially due to expenses for essentials including housing being pushed up by tech workers on much higher salaries. It's hard to carry out deep research if you are distracted by worrying about whether you will have enough food to last the week, or whether the next rent increase will leave you homeless.

Unfortunately universities have little incentive to increase the pay of junior researchers. The majority of graduate students are foreigners on temporary visas with little bargaining power; a few years in poverty can be a pathway to permanent residency, which opens up many more opportunities compared to their home countries. 

Moreover, salaries funded by research grants are often fixed by the funding agencies, with top-ups explicitly forbidden in some cases. Even when professors may be strongly in favour of paying their team members a livable wage, they have little power to effect change with salaries controlled by upper university management.

Despite these hurdles, the University of California system has now agreed to pay rises of up to 29% for postdocs and researchers. 

Will this lead to broader change within the broken academic system?

Meanwhile, in the headlines a few days ago Singapore and New York were tied as the world's most expensive cities. This comes amidst an absolutely insane rental market here, with monthly rents up by more than 70% in some cases.

Two preprints

I haven't had much time for posting recently, since I've been rushing to finish several projects before the end of the year. We have two papers out on arXiv this week, with a few more hopefully ready soon!

Unravelling quantum chaos using persistent homology

We considered the application of (quantum) chaos detection techniques to a simple system, a driven-damped Kerr nonlinear oscillator. Tuning the driving frequency in this system can induce transitions between chaotic and regular dynamics, with classical chaos emerging in the limit of large amplitude coherent states. This provides a nice setting for looking at how persistent homology-based methods for detecting classical chaos can be translated to quantum dynamics. For this purpose, we treat the quantum oscillator as an open quantum system subject to random quantum jumps (photons leaking from the cavity), described by a stochastic Schrodinger equation. Despite the higher complexity of the quantum system (in terms of a larger phase space), we can still distinguish regimes of regular and chaotic dynamics by considering the topology of a time-delay embedding of the detected photon counts!

Pseudospin-2 in photonic chiral borophene

One chapter of my PhD thesis covered wave propagation at conical interactions. At the time, we were most interested in intersections with pseudospin 1/2 (corresponding to Dirac cones in systems such as graphene), and pseudospin 1 (occurring in the Lieb lattice, a topic which I spent some time studying). While I was writing up the thesis, I found the analysis of both kinds of systems could be connected nicely by generalizing the description to arbitrary pseudospin s. We included this in a review article, but didn't give much thought as to how one might go about realizing intersections with higher values of s, assuming they would be unstable and hard to make in practice.

It turns out, higher pseudospin conical intersections can be symmetry-protected (similar to the case of the Lieb lattice), and can they emerge in lattices that don't look too crazy, can be realized using optical waveguide arrays, and may even exist as stable two-dimensional electronic materials!