Quantum Jobs

PRX Quantum seeks an Associate Editor, Quantum Information. Physical Review is home to the most Nobel Prize-winning physics papers in the world. This is an opportunity to be at the forefront of the most exciting breakthroughs in quantum science!

Many postdoctoral openings at the Centre for Quantum Technologies, Singapore, ranging from experimental integrated photonics to applying quantum-inspired algorithms to bioinformatics!

Coming soon: ARC Centre of Excellence in Quantum Biotechnology. This newly-funded centre aims to pioneer paradigm-shifting quantum technologies to observe biological processes and transform our understanding of life. Stay tuned for openings in this exciting new field...

Talks-to-papers with Whisper

Last year I wrote about a neat and lightweight implementation of the Whisper speech-to-text model. One of the potential applications I mentioned was converting recorded presentations (seminars, lectures, etc.) into written notes. A few weeks ago a review article I wrote using this approach was published in AAPPS Bulletin. Here's how I did it:

 1. Identify source material. In this case, I had an online conference talk that had been recorded and uploaded to Youtube.

2. Download the raw audio using a tool such as yt-dlp

3. Convert audio to a text transcript. I used whisper.cpp (can run on CPU). The base and small models sizes already do pretty well in terms of accuracy and run quickly.

4. Transcript editing. Whisper won't have perfect accuracy, especially when attempting to transcribe scientific jargon. So it's necessary to carefully review the generated text.

5. Figure conversion. In this case since it was my own talk, I had access to high resolution version of the figures I wanted to include in the paper. Minor reformatting required.

6. Add references. While I cited papers in the slides, the citations need to be converted to a .bib file or other reference manager format. It would be helpful to have an AI assistant that could do this automatically.

And with that I had a first draft completed! Very nice, since the first draft is usually the hardest to write. I did spend some more time polishing the text, adding some details that didn't make it into the original talk, and making the language more formal in parts, but it ended up being a lot easier than writing the whole text from scratch!

Looking back on 2023

The end of the year is good time to reflect on what went well and what didn't over the past twelve months, and what changes we hope to make in the year to come. Here's my list:

1. Presentations. I gave 11 talks this year to a variety of audiences (conferences, workshops, internal presentations, external seminars). Some went better than others. The main culprits for my bad talks are (still) trying to say too much in the time allotted, and failing to pitch well to the specific audience. It is particularly challenging to convey the broad strokes of the research to everyone present at a level that interests them while still going into enough depth to satisfy the few experts in the audience. The best presentations I gave involved audience participation using QR code polls - even including one or two over the course of an hour-long talk is a great way to get the audience to stop, think, and start paying attention again. The best talks I attended spent most of the time explaining the problem set up and broader context and very little time on the speaker's own contribution.

2. Publications. Midway through the year it seemed like I was going to put out fewer papers than usual. Then in November and December I ended up being swamped with finalising several manuscripts all at once (hence a reduced blogging frequency). The final tally is nine original manuscripts completed this year. Is this too many? Many decry the publish or perish culture, the endlessly increasing rate at which papers are being published, courtesy coauthorships, salami publishing, and whatnot. At least in my case, I think I have made a meaningful contribution to every paper I have coauthored this year, but I need to strike a better balance between deep work on new research directions and easier (but still time-consuming) work on existing areas of expertise.

3. Upskilling. I played around with using AI tools like StableDiffusion (text to image), LLama (text generation), whisper (speech to text), and a few different web-based academic paper summarisation / recommendation tools. Given the tendency of large language models to hallucinate and spit out falsehoods, it's hard to trust them when seeking new knowledge (e.g. summarising or suggesting new papers to read), but I've found them quite useful for rephrasing ideas in an amusing way or making cool images for talks (see below).

Happy 2024!

Towards fault-tolerant quantum computing with Rydberg atoms

 I'm a bit late to the party, but finally managed to get a chance to read the paper "Logical quantum processor based on reconfigurable atom arrays" by Harvard, QuEra, and collaborators, which hit the headlines a few weeks ago. My thoughts:

• Sadly many articles covering the paper gloss over the important distinction between error detection and error correction: QuEra's press release, The Harvard Gazette, EurekaAlert, and others. Optics & Photonics News provides more balanced coverage. The impressively high (above break-even) fidelities demonstrated in the paper require post-selection, discarding experimental runs where errors were detected. The post-selection probability is as low as 0.04% for the largest system sizes studied, and will get exponentially smaller for bigger circuits. The bottom line: scaling up to a useful size needs integration of error correction.
• How to integrate error correction? One needs to process the error detection measurements in real-time and then apply correcting gates to the qubits while the circuit is being run. Figure 4 of the paper does demonstrate implementation of measurement-dependent feedforward operations, but not yet integrated with error decoding and correction operations. This seems to be in principle an engineering challenge that can be solved with more hard work.
• Scaling up to more qubits and deeper circuits will require continuous pumping and replenishment of Rydberg atoms. Otherwise, the circuit width will be limited by the finite success probability for trapping each atom, and the depth by the ~10s trapping lifetime.
• The quantum processor architecture, involving separate storage, processing, and readout zones, as well as the ability to execute gates with arbitrary connectivity and in parallel using just a few structured laser beams, looks much more promising for scalability compared to superconducting quantum processors.

There is more discussion over at Shtetl-Optimized.

Dark horse papers

A journal's impact factor - the number of citations it receives in a year divided by the number of papers published in the preceding years - is often used as a proxy for the importance of the papers it publishes. But the impact factor is a poor predictor for individual articles, since article citation distributions are heavy-tailed, so their mean is strongly affected by rare outliers that receive many more citations than a typical article. 

It is difficult to estimate the impact a paper will have before it is published. Thus, one can find papers published in top journals that after several years have only attracted a handful of citations - the editors and referees overestimated the impact the article would have. Similarly, there are papers that were only published in a specialized (e.g. local society-run) journal and ended up having a big impact. Some examples:

S. Aubry and G. Andre, Analyticity breaking and Anderson localization in incommensurate lattices, Ann. Israel Phys. Soc. 3, 133 (1980). A conference proceedings article with more than a thousand citations and even its own wikipedia page, influential as a simple analytically-solvable toy model of a localization transition.

T. Fukui, Y. Hatsugai, and H. Suzuki, Chern Numbers in Discretized Brillouin Zone: Efficient Method of Computing (Spin) Hall Conductances, J. Phys. Soc. Japan 75, 074716 (2006). This is essential reading for anyone who wants to numerically compute Berry curvatures, since it solves the problem of how to fix a smooth gauge to compute the k-space derivatives of the Bloch functions. Also has more than a thousand citations.

M. Fujita, K. Wakabayashi, K. Nakada, and K. Kusakabe, Peculiar Localized State at Zigzag Graphite Edge, J. Phys. Soc. Jpn. 65, 1920 (1996). This was a paper that was ahead of its time, showing that certain edge configurations of graphene give rise to strongly localized edge states. This was more of a theoretical curiosity, until samples of graphene were isolated a few years later, as you can see in the time series of citing articles:

 

These are just a few examples I've come across in my own research. There are many more out there! Do you have your own favourite example?

Updates

Infrequent posting due to other commitments. Here are a few brief items of note from the past month:

• Beng Yee uploaded his second paper from his PhD research to arXiv: A Unified Framework for Trace-induced Quantum Kernels. This project tackled the problem of how to choose the best quantum kernel for a given learning task using tools from classical multiple kernel learning theory. The bottom line: the optimal problem formulation (e.g. as a kernel model, projected kernel model, or quantum neural network) depends on the relative amount of training and test data, whether one wants to impose constraints to the trained model, and whether one has many qubits with low-fidelity gates or a fewer qubits with high fidelity gates. Read to find out more!
• The December issue of Optics & Photonics News highlights some of the most exciting peer-reviewed research in optics and photonics published over the past year. There is also an accompanying perspective on areas to watch in 2024 and beyond by selected summary authors.
• Two papers recently published in PRL caught my eye: Universal Sampling Lower Bounds for Quantum Error Mitigation suggests the quantum error mitigation being pushed by IBM and others as a means of getting useful applications out of current noisy quantum processors may be foiled by an exponentially growing measurement overhead, and Classifying Topology in Photonic Heterostructures with Gapless Environments shows how a recently-developed real space formulation of topological invariants may be a more useful tool for quantifying the robustness of topological states in photonic systems, particularly those exhibiting radiation losses of optical nonlinearities.
• The 7th International Conference on Optical Angular Momentum will be held 24 - 28 June 2024 in South Africa. The abstract submission deadline is 7 January 2024.
• The next edition of the Quantum Techniques in Machine Learning conference will be held in Melbourne, 25-29 November 2024. The abstract submission deadline is 5 July 2024. 
• In the news headlines: Alibaba shuts quantum computing lab. Seems to be part of a wider trend of industry funding shifting from quantum to generative AI - see also Zapata and Normal Computing.
  

Physics meets machine learning and AI

Machine learning research of interest to physicists can be broadly divided into two categories: using machine learning tools to solve physics problems, and using ideas from physics to improve machine learning techniques.

An example of the former is the transformer neural networks used in the design of large language models such as ChatGPT. The ability of the transformer neural network architecture to efficiently learn long-ranged correlations in data is also useful for variational methods for finding ground states of strongly-correlated quantum many-body systems. Two papers demonstrating this approach were published in Physical Review B and Physical Review Letters earlier this year.

Popular image generation tools such as Dall-E and Stable Diffusion (which I wrote about previously) are based on time-reversing a diffusion process to generate desired samples from noise. This approach is heavily inspired by techniques from non-equilibrium statistical mechanics published in Physical Review E in 1997.

Another pressing issue in machine learning and AI is how to understand the emergent properties of large language models as their size or training time is scaled up. This is a problem that physicists are well-posed to tackle using techniques from statistical physics, random matrix theory, and the theory of phase transitions, which have recently been applied to shallow neural network models in a few different studies:

Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models

Learning through atypical phase transitions in overparameterized neural networks

Grokking phase transitions in learning local rules with gradient descent

Droplets of Good Representations: Grokking as a First Order Phase Transition in Two Layer Networks

I'm sure we'll see a growing number of theoretical physicists becoming involved in this exciting area of research in the coming years.