Archive for quantum computing

Nature tidbits [23 October 2025]

Posted in Books, Kids, pictures, Running, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on November 27, 2025 by xi'an

In this October issue of nature, plenty of the “usual” topics, namely AI and Trump.2.0 wrecking balls, along with two cosmology entries that related to my trip to the early universe last week, and a pros-and-cons opposition about animal testing,

a discussion on the nature of the “little red dots” that have been recently observed and whose nature remains open, the most popular explanation (I was given during lunch) being black holes surrounded by gas (even though I cannot understand why the gas is not attracted by the black hole!) [and would have produced a more exciting cover!]

a review of the recent book Discordance: The Troubled History of the Hubble Constant by Jim Baggott, entitled Why we still don’t understand the Universe — even after a century of dispute! A review that regrets that more time is spent on the Hubble “constant” (which varies with time!) rather than more controversial issues like dark matter and dark energy (And strangely bemoans that the book is focussed on scientific developments, missing sociological ones. Duh?! (Bonus for a picture of suit-and-tie Edwin Hubble sitting at the centre of a telescope),

two entries on the well-being [or lack thereof] of PhD students, with nothing particularly surprising (eg, inclusivity and respect help!), and Brazil, Australia and Italy ranking top locations but in a comparative study that does not mention France (as often in international comparisons found in Nature) despite the place being in the top 10 countries delivering PhD degrees, not that I believe PhD students are particularly well-treated in French academia!, the (unexplained) surprise being Italy ranking so high given the close resemblance between the two countries (low stipends, shortage of postdoc and permanent positions, high teaching loads for the advisor, limited travel budgets),

a conference (purposedly) made of AI-written papers reviewed by AI referees, Agents4Science 2025, how universities are rushed into adapting to AI-fluent students, whose skills are changing, and the rise in fake authors produced by paper mills, with a limited range of acceptable solutions,

why Trump 2.0‘s blackmail on pharmaceutical companies is counter-productive and likely to slow down progress, and why his massive increase of highly qualified scientists is shooting (or nuking) USelf in the foot, given the huge proportion) of im/emigrated Nobel prize winners (for physics, chemistry, and medicine), along the (post-) Nobel prize in economics is a direct or indirect reply to this regression by awarding the Prize to economists who worked on the importance of creativity and science on growth (not very surprising at first look!)

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Nature tidbits [30th October 2025]

Posted in Books, Kids, pictures, Running, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on November 22, 2025 by xi'an

In this October issue of nature, items of interest (to me) [already highlighted in an emailed News Highlight]:

Google claim a significant ‘quantum advantage’ by quantum-echoes algorithms that they say is 13,000 faster than classical algorithms, but it is unclear from the nature article where and when their algorithms can be used, apparently missing to apply to realistic scientific applications… (Along with a paper on an “atom-array architecture that enables continuous operation with reloading rates of up to 30,000 initialized qubits per second while preserving coherence across a rearranged large-scale qubit array”.)

On the Trump vs. Science scene, a predicted drop in PhD admissions as an adaptation to (uncertain) Trump’s cuts, bans, visa restrictions, and other attempts at arm-bending and blackmailing  (And the novelty of me receiving applications from the US, a first!) Some U.S. departments have simply cancelled PhD admissions… But some universities have so far resisted the Orange pressure. Namely, the MIT, Brown University, (good old) Penn, UCLA, the University of Virginia (albeit agreeing to a deal), Dartmouth College, and the University of Arizona in Tucson. Vanderbilt has given in. (UT Austin and Harvard seem to be continuing the discussion with the Trump administration.) Meanwhile, China is zooming past! The issue also contains articles on how fundamental science discoveries have had hugely practical consequences, in case one need argue with a sceptic.

As a less urgent issue, some researchers at Institut Pasteur in Paris identified new diseases that did not help Napoléon’s Grande Armée as it retreated from Moscow, from the DNA of 13 soldiers buried in Lithuania. (With nature failing to give credit to the painter Adolph Northen for his famous “Napoleon’s retreat from Moscow” illustrating the story, attributed to the researchers in the paper!)

In this period of ERC announcements of their grantees, an analysis of the two-digit rise in applications. Unsurprising, given the international context. Along with a decrease in funding due to a lack of adjustment against inflation since 2007. (Incidentally, I found out this week that Torsten Elßin—at the Max Planck Institute for Astrophysics—I visited once had been selected for a Synergy grant on a 3D Milky Way Atlas. Along another cosmology Synergy grant at MPA on the Epoch of Reionization.)

A runner’s must-read that starts with the statement “the human body has a ‘metabolic ceiling’ that even the most extreme athletes cannot surpass”. Which would be 2.4 times the basal metabolic rate (BMR) for extended periods—by which the authors mean 30 weeks and over!, not a half-marathon. Not so exciting a paper in the end.

As predicted by the cover, a Royal Society meeting acknowledging the AI language models killed Turing’ test and questioning the next one. Since assessing the capacities and limitations of novel AIs and AGIs sounds more relevant and societally important. To wit, “;the Turing test of the future should question whether an AI is safe, reliable and provides meaningful benefit, he said, and should also ask who bears the cost of that benefit”. (As discussed in the book review of The Means of Prediction: How AI Really Works (and Who Benefits) by Maximilian Kasy, in the same volume. And yet another paper on AI biases.)

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Natural quantum MCMC

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on November 10, 2025 by xi'an

Quite unexpectedly, I opened Nature of 16 October 2025 only to realise it featured a paper by Chi-Fang Chen et al. on a valid MCMC algorithm for quantum computing. The motivation is for simulating quantum multi-body physics, with details that escape me. The paper claims this is the first valid quantum MCMC quantum thermal simulation, with further application similar to the MCMC revolution in Bayesian inference. (An earlier proposal by Davies in 1974 cannot be implemented for many-bodies problems.)

“Although early proposals for simulating thermalization directly considered simulating the global system–bath Hamiltonian evolution, more tractable approaches use a master equation, or Lindbladian, to capture the effect of a large bath on the small system using a continuous-time Markovian process. However, previous approaches inherited issues of the prototypical Davies generator, which worked well in quantum optics but has unphysical features in noncommuting many-body systems because of the exponentially small level spacing. Our nature-inspired algorithm takes precisely the form of a Lindbladian, inherits the locality from the physical model, exactly satisfies detailed balance and resembles the interactions we expect from weak coupling to a Markovian thermal bath.”

The setup is one of a continuous-time quantum Markov chain (or Lindbladian) over a finite set of configurations. The arguments for validating the algorithm (into a completely spelled out theorem!) involve quantum detailed balance (illustrated by the above cartoon) and locality (which I understand as a form of Markovianity in the updating rule). The transition attached to the algorithm however remains incomprehensible to me, involving a Fourier transform of a Hermitian jump operator (both notions escaping me in this context).  And the illustration on an Ising model does not seem particularly quantum related, albeit the “Pauli operators” may be unrelated with the original spin binaries… There is also no MCMC reject step.

“Detailed balance (…) gives a coherent Gibbs sampler, in which we prepare the purified Gibbs state by a natural adiabatic path parametrized by the inverse temperature β. Of course, the purified Gibbs state reduces to the (mixed) Gibbs state once we trace out the purifying replica system, but the purification opens doors to advanced quantum algorithmic tools, such as verification (for example, through the swap test or measuring the energy of the parent Hamiltonian) or faster mean estimation.”

If I understand correctly the above “Gibbs sampler” relates to the Gibbs distribution as in the original 1984 paper of Geman and Geman! It may thus be that the current paper is similarly annunciating a new era in scientific computing, although I remain in the dark as to how to link it with a practical problem.

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Data science ethics [book review]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , on May 5, 2025 by xi'an

Data science ethics (concepts, techniques and cautionary tales), by David Martens, was published in 2022 by Oxford University Press. The book is inspired by the author’s  course on Data Science and ethics he has been teaching at the University of Antwerp. (With a link to his slides.) The 255p book proceeds by decomposing the ethics of data science into its different steps: data gathering (Chap. 2), data preprocessing (Chap. 3), modelling (Chap. 4), evaluation (Chap. 5), and deployment (Chap. 6). Following the `FAT Flow Framework´, where FAT stands for fairness, accountability, and transparency.

Do not expect much maths, stats, or anything quantitative: this book is mostly about concepts, even though some (mostly well-known) illustrations are provided. Chapter 2 includes a description of encryption (with homomorphic encryption treated in Chapter 4). And somewhat improbably quantum computing. Differential privacy gets a few pages with not a single formula (until Chapter 4, again).

Chapter 3 covers k-anonymity, record linkage, reidentification, (through cautionary tales) and discrimination through biases in the (learning) dataset.  Chapter 4 is defining ε differential privacy with the Laplace randomization as a possible implementation and with no critical stance on the limitations of the concept. The computation limitations of homomorphic encryption are more clearly pointed out. Federated learning is only quickly mentioned. The section about measuring fairness and reducing bias implies that some prior knowledge is available about whom is potentially discriminated and which covariates to add to the model. The last section on explicability of predictions is worthwhile in signalling the difficulty with most (black box) AI but the example opposing an SVM model to a logistic model is not tremendously convincing in that neither model is true.

Chapter 5 addresses the crucial challenge of ethical evaluation in a rather verbose and vague manner. For instance, with no instruction on how to resist adversarial attacks. Or criticising p-hacking and multiple testing while missing the elephant in the room (p-values!). Drifting from the topic when discussing the misdeeds of Diederik Stapel. Most of the same goes about Chapter 6 and its take on ethical deployment, when going through examples such as Google’s policies in China. Or general musing on the impact of AI on societal inequalities (with mentions of companies and CEOs who have since then back-pedalled on their ethical engagement). These chapters are lacking in tools and (more) practical recommendations.

One interesting aspect of the book is the attention paid to the EU(ropean) aspect of these concerns, through the GDPR (General DAta Protection Regulations) adopted by the European Parliament in 2016. (There is also a brief mention of China’s regulations, but no details beyond a reference. Maybe the Chinese edition differs.)

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Nature (statistical) tidbits

Posted in Statistics with tags , , , , , , , , , , , , , , , , , , , , , , , , , , on January 3, 2025 by xi'an

In the 28 November issue of Nature, with this black wallaby left with little to survive after the massive wildfires of 2020, which I read on my way to Nice, several entries related with statistics at large:

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