Wednesday, March 27, 2024

CQT Colloquium on strongly interacting photons in superconducting qubit arrays

 Yesterday at CQT Jonathan Simon from Stanford University gave a wonderful colloquium talk on "Many-body Ramsey Spectroscopy in the Bose Hubbard Model," covering experimental studies of strongly interacting quantum fluids of photons in arrays of superconducting qubits, spanning work from 2019 on the preparation of photonic Mott insulating states to ongoing studies of entangled many-body states of light.

A good colloquium talk should understandable to a broad audience (ideally, including undergraduates) while still going into enough depth to keep specialists in the topic interested. If you cannot frame your research in terms of some simplified model, chances are you do not yet fully understand it.

Simon did this using the neat example of emergence in 2D point clouds: observing non-trivial emergent properties requires three key ingredients: many particles, interactions between the particles, and dissipation (in this case, friction) to allow the system to relax to some ordered state. When all three are included, the cloud self-organizes into a triangular lattice with properties qualitatively different from those of the individual constituent particles, supporting low energy vibrational modes (phonons).

Typically, a colloquium talk will cover research spanning several years. It is important to have some clear common motivation. In this case, the question of how to make quantum states of light exhibit similar emergent properties? Three ingredients are required: give photons an effective mass, achieve strong photon-photon interactions, and introduce a suitable form of dissipation that allows the system to relax to some interesting equilibrium state while preserving non-trivial many-particle effects.

After this framing, the talk went deep into how these ingredients can be realized using arrays of superconducting qubits, and how the relevant dimensionless quantities (interaction strength vs hopping strength vs photon lifetime) compare to other platforms, such as cold atoms (handy, given the mix of expertise in the audience).

The talk finished with a vision for the future - to connect this "photonic quantum simulator" to a small-scale quantum processor to test NISQ-friendly algorithms, such as shadow tomography of many-body quantum states.

A recording will probably be uploaded to the CQT Youtube page later. In the meantime, related talks given at JQI and Munich are already available online!

Wednesday, March 20, 2024

ChatGPT, write my article introduction! And editors versus referees

This paper with an introduction brazenly written by ChatGPT attracted a lot of attention last week. How is it that the first line of the introduction could remain in the final version without anyone (authors, editors, referees, proofing staff) noticing? 

Some said this was no big deal - aren't paper introductions boilerplate junk that nobody reads anyway? Yes and no. While an expert in the field might not expect to learn anything new from reading a paper introduction, it is nevertheless important as a means for the authors to convince the reader that they sufficiently understand the context of the research and are in a position to make a novel and significant contribution.

Others argued this was an example of the failure of peer review and the current scientific publishing system - junk papers that no one (not even the authors!) read.

Who exactly is at fault here (apart from the authors, obviously) - the journal editors or the referees?

Actually, it is not the referees' job to proofread manuscripts! Many referees will not bother to laboriously point out all the obvious typos in a manuscript and will purely focus on the scientific content in their reports. Sloppiness that the authors fail to notice themselves will detract from the credibility of the science reported and may be more damning than scathing technical criticism by the referees that might not be adequately addressed in the final paper!

The editors should have caught this in their initial screening. One of the roles of an editor is to curate content and ensure that the valuable time of the volunteer referees is not wasted on obviously incorrect, unconvincing, or not even wrong manuscripts. At the same time, we don't want to waste the authors' time by agreeing to send the manuscript out for review and then being unable to secure willing referees!

At Physical Review A we desk reject about half of the manuscripts we receive without sending out for peer review. While this might sound like a lot, these manuscripts tend to be of much lower quality than those that are eventually published. There are several red flags that make us lean towards desk rejection:

Out of journal scope. Does the manuscript report results that are of interest to the readers of the journal? One simple way to gauge this is to check the reference list of the finished manuscript - if you are only referring to works from other disciplines, this is not by itself grounds for rejection, but it is a hint that you need to be particularly careful with explaining the relevance of your work to the journal's specific audience.

Poor presentation. Obvious typos. Ugly figures. No figures (passable in rare cases). Too many figures. Illegible axis markers. Incorrectly formatted equations and symbols. Basic stuff, but many authors sadly cannot be bothered.

Transfer after rejection from a sister journal. This one is surprisingly common, particularly for research topics which fall in the scope of multiple APS journals. Most often we see transfers from PR Applied and PRB, which have higher impact factors, so the authors decide to try their luck with PRA. But the standards of all these journals are the same, regardless of their impact factors that fluctuate from year to year. This means that rejection from PR Applied or PRB generally precludes publication in PRA, except in special cases.

No significant new physics. This is the most controversial. Who is the editor to decide what is significant - isn't that the job of the referees? We do lean towards giving the benefit of the doubt and sending out to referees for this one. The manuscripts that fail this test generally lack the "so, what?" factor - assuming all the claims are correct, have we learned anything new? It is always possible to tweak models, change terms, make them a bit more complicated, and then apply analysis tools that are standard for the field to get something that is technically correct. But the impact of such technically correct works will be limited unless they open up something new - a novel experimental platform, a way to push the limits of existing theory, and so on.

It is never pleasant for one of your articles to be rejected without review, but it is actually the second best response you can receive! The likely alternative would be for you to wait months before receiving a similar rejection on the basis of anonymous referee reports!

Tuesday, March 12, 2024

Postdoc Opening at Tohoku University: Condensed Matter and AMO Theory

Tomoki Ozawa's group at the Advanced Institute for Materials Research, Tohoku University, has a postdoc opening (the official title would be Specially Appointed Assistant Professor, or Tokunin-Jokyo in Japanese), which can start as soon as the decision is made or from April 1, 2025 at the latest. The position lasts for three years. 
The group works on theoretical condensed matter physics and AMO (atomic, molecular, and optical) physics, in particular on topological phases and/or many-body physics in these systems. A part of the salary of this position will be from the KAKENHI Kiban-B grant, “Geometrical effects in non-Hermitian quantum systems." 
The application deadline is April 30, 2024, and the application should be sent through Academic Jobs Online from the following link:

Friday, March 8, 2024

Vanishing Papers, Vanishing Journals

 A highlight in Nature this week: Millions of research papers at risk of disappearing from the Internet 

What happens when a publisher goes bust? Are their journal articles lost forever? 

The digital object identifier (DOI) system used by academic journals, among others, is supposed to be robust to this; the URL to which a DOI points can be updated when the original source is no longer available, provided another source exists. Dark archives such as LOCKSS were developed to preserve scholarly articles and keep them available after the original publisher is no longer around. 

However, according to M. P. Eve writing in the Journal of Librarianship and Scholarly Communication, a substantial fraction (27%) of journal articles linked to a DOI are not preserved in any centralised archive, making them at risk of being lost forever!

This is a particularly important problem for the growing number of for-profit open access journals. They make their money upon publication. Who will pay for the preservation of their articles? Under the subscription model where the journal holds the article copyright, this is an asset that remains valuable even after the journal has ceased publishing new articles. This is not the case for open access journals - they are only as valuable as long as they maintain a steady stream of submissions and published articles.

Preservation of the scientific record is important. The American Physical Society maintains and sells access to their archive of publications dating all the way back to 1893. How many of today's open access journals will remain accessible a hundred years from now?

 


Thursday, February 8, 2024

Transformer quantum states: more than meets the eye?

The large language models that have boomed in power and popularity over the last year are based on a neural network architecture called the transformer. Transformers were originally designed to efficiently learn complicated long range correlations arising in natural language processing and are now being applied to other areas, including many-body quantum physics, where they being used as flexible variational quantum states. 

Interest in neural network quantum states has grown rapidly since 2017, when Carleo and Troyer showed that a neural network architecture called the Restricted Boltzmann Machine could be trained to find the ground state wavefunction of the transverse-field Ising and antiferromagnetic Heisenberg models. One limitation with this original work was the difficulty of computing expectation values of the ground state using this architecture, since the trained network takes as its input a spin configuration and returns the corresponding probability amplitude, meaning that time-consuming Monte-Carlo sampling is needed to evaluate expectation values.

Monte-Carlo sampling can be avoided using different parameterizations of the many-body quantum state. For example, the autoregressive quantum state encodes the many-body wavefunction $\Psi(\boldsymbol{s})$ as a product of conditional probability distributions:

$$\Psi(\boldsymbol{s}) = \prod_{i=1}^N \psi_i(s_1 | s_1,...,s_{i-1}) = \psi_1(s_1) \psi_2(s_2|s_1) \psi_3 (s_3 | s_2 s_1) ...\psi_N(s_N| s_{N-1}...s_2 s_1) $$

One can thereby draw unbiased samples from the many-body ground state by first drawing the first spin, $s_1$, according to the learned probability distribution $\psi_1(s_1)$, followed by $s_2$ according to the conditional probability distribution $\psi_2(s_2 | s_1)$, and so on until a complete spin configuration is obtained. However, there is a conservation of misery in that we avoid Monte Carlo sampling but instead need to learn an exponential number of conditional probability distributions! Luckily, it is empirically observed that a single neural network is able to encode all this information, if we allow it to take as an additional input a hidden vector $h_i$ that encodes information as to the previously-drawn spins. This gives the neural network an autoregressive structure, in that the output from one pass is sequentially fed back into its input.

The autoregressive quantum states were inspired by the autoregressive neural networks developed for natural language processing tasks. The performance and scalability of autoregressive neural networks is limited by the need for sequential processing to generate a single sample, the potential for vanishing gradients making it difficult to train the network, and a bias of the learned probability distribution to the recently-sampled spins. More advanced formulations based on masked convolutional networks are able to generate entire configurations using a single evaluation of the network, but still encounter issues with trainability and encoding distributions exhibiting complex correlations.

Then along came the transformer architecture. The innovation here was the inclusion of multiple parameterized transformations to the input data that can be trained to pick out different features and (long-range) correlations – the multi-head attention. Once the key features are identified by the multi-head attention, a relatively simple feed-forward neural network is sufficient to compute the output probability. The success at this architecture for language modelling is now inspiring many studies on applications to many-body physics.

One line of research is exploring transformer neural networks as a flexible ansatz capable of describing families of many-body quantum systems, exemplified by the paper "Transformer quantum state: A multipurpose model for quantum many-body problems." In this work, the transformer neural network was trained to learn the many-body ground states of a family of Ising models. Thus, it takes as its input model parameters (e.g. the applied magnetic field strength), and then draws samples of ground state spin configurations. The model can also extrapolate to predict properties of ground states not included in the training data, albeit with lower accuracy, particularly when attempting to extrapolate across a phase transition. The network can also be "inverted" to perform a maximum likelihood estimation of the system's parameters given a few spin configurations drawn from its ground state, analogous to shadow tomography of many-body quantum states.

A second line of research is exploring the use of transformers as a means of accurately computing ground state energies from specific strongly-correlated model Hamiltonians, such as the Shastry-Sutherland model, see for example the paper "Transformer Variational Wave Functions for Frustrated Quantum Spin Systems". In this case, an architecture called the vision transformer is trained to learn the complex correlations present in the ground state. The biggest challenge is training the network, which is particularly difficult for complex-valued networks, however recent work has shown that a two-stage architecture that applies a real-valued transformer followed by a complex fully-connected neural network can be trained more easily.

What next for this hot topic? Better training methods or more easily-trainable transformer architectures are needed, since in training data for quantum many-body systems is much harder to come by than web-scraped training data for large language models. Future research on applications of classical transformer neural networks will likely be divided between problem-specific models tailored to solve certain hard many-body problems, and less accurate general purpose "foundation" models which may be useful for generating initial guesses for other more precise iterative methods. Beyond this, quantum and quantum-inspired generalisations of the transformer architecture are also cool directions to watch!

Thursday, February 1, 2024

A busy January

There's been a lot going on here...

Machine Learning & Physics

Unsupervised learning of quantum many-body scars using intrinsic dimension - Now available on arXiv! We applied manifold learning techniques to identify scar states in the PXP model The take-home message: manifold learning techniques are a powerful alternative to more popular deep learning methods, especially in physics problems where you might not have access to enough training data for deep learning to work well.

Identifying topology of leaky photonic lattices with machine learning - Just published in Nanophotonics! We apply various machine learning methods to distinguish different topological phases in a photonic lattice, assuming one only has access to intensity measurements. This can serve as an alternative to full state tomography or phase retrieval methods, but one needs to be careful when training the models on ideal / pristine systems and then applying them to disordered systems. The journal also published a press release on WeChat!

Quantum Computing

Computing electronic correlation energies using linear depth quantum circuits - Finally published in Quantum Science & Technology, after more than a year and a half working through the peer review system. We use perturbation theory to determine electronic correlation energies in small molecular systems (hydrogen, lithium hydride, etc.) using a large set of shallow circuits, giving an alternative to existing methods which require deeper circuits infeasible for current quantum processors. We also tested the algorithm on cloud quantum processors, observing the detrimental impacts of noise. It would be interesting to run this again now to see how much (or how little) the performance from the different cloud providers has improved!

Landscape approximation of low-energy solutions to binary optimization problems - Published in Physical Review A. We present a method to obtain approximate solutions to binary optimization problems using the localization landscape, a function which is able to place bounds on the regions of Anderson localized eigenstates in disordered media without solving the underlying eigenvalue problem. We lay out the conditions required for these bounds to hold, outline how a quadratic unconstrained binary optimization problem can be transformed to fit these conditions, and provide details on how the quantum state representing the landscape function can be produced and sampled using techniques developed for near-term quantum devices.
 
On a related note, I was interested to see this month a new arXiv preprint in which the localization landscape was used to engineer multifractal resonances in SiN membranes!

Photonic Flatband Resonances

Photonic Flatband Resonances in Multiple Light Scattering - Published in Physical Review Letters. We reveal that flatbands can emerge as collective resonances in fine-tuned arrays of Mie-resonant nanoparticles, leading to giant values of the Purcell factor for dipolar emitters. The article was also highlighted with a Synopsis in Physics Magazine!

Tuesday, January 23, 2024

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...