quantum simulation or supremacy?

Nature this week contains a prominent paper by Arute et al. reporting an experiment on a quantum computer running a simulation on a state-space of dimension 2⁵³ (which is the number of qubits in their machine, plus one dedicated to error correction if I get it right). With a million simulation of the computer state requiring 200 seconds. Which they claim would take 10,000 years (3 10¹¹ seconds) to run on a classical super-computer. And which could be used towards producing certified random numbers, an impressive claim given the intrinsic issue of qubit errors. (This part is not developed in the paper but I wonder how a random generator could handle such errors.)

“…a “challenger” generates a random quantum circuit C (i.e., a random sequence of 1-qubit and nearest-neighbor 2-qubit gates, of depth perhaps 20, acting on a 2D grid of n = 50 to 60 qubits). The challenger then sends C to the quantum computer, and asks it apply C to the all-0 initial state, measure the result in the {0,1} basis, send back whatever n-bit string was observed, and repeat some thousands or millions of times. Finally, using its knowledge of C, the classical challenger applies a statistical test to check whether the outputs are consistent with the QC having done this.” The blog of Scott Aaronson

I have tried reading the Nature paper but had trouble grasping the formidable nature of the simulation they were discussing, as it seems to be the reproduction by a simulation of a large quantum circuit of depth 20, as helpfully explained in the above quote. And checking the (non-uniform) distribution of the random simulation is the one expected. Which is the hard part and requires a classical (super-)computer to determine the theoretical distribution. And the News & Views entry in the same issue of Nature. According to Wikipedia, “the best known algorithm for simulating an arbitrary random quantum circuit requires an amount of time that scales exponentially with the number of qubits“. However, IBM (a competitor of Google in the quantum computer race) counter-claims that the simulation of the circuit takes only 2.5 days on a classical computer with optimised coding. (And this should be old news by the time this blog post comes out, since even a US candidate for the presidency has warned about it!)

stochastic magnetic bits, simulated annealing and Gibbs sampling

A paper by Borders et al. in the 19 September issue of Nature offers an interesting mix of computing and electronics and optimisation. With two preparatory tribunes! One [rather overdone] on Feynman’s quest. As a possible alternative to quantum computers for creating probabilistic bits. And making machine learning (as an optimisation program) more efficient. And another one explaining more clearly what is in the paper. As well as the practical advantage of the approach over quantum computing. As for the paper itself, the part I understood about factorising an integer F via minimising the squared difference between a product of two integers and F and using simulated annealing sounded rather easy, while the part I did not about constructing a semi-conductor implementing this stochastic search sounded too technical (especially in the métro during rush hour). Even after checking the on-line supplementary material. Interestingly, the paper claims for higher efficiency thanks to asynchronicity than a regular Gibbs simulation of Boltzman machines, quoting Roberts and Sahu (1997) without further explanation and possibly out of context (as the latter is not concerned with optimisation).

Nature worries

In the 29 August issue, worries about the collapse of the Italian government coalition for research (as the said government had pledge to return funding to 2009 [higher!] levels), Brexit as in every issue (and the mind of every EU researcher working in the UK), facial recognition technology that grows much faster than the legal protections which should come with it, thanks to big tech companies like Amazon and Google. In Western countries, not China… One acute point in the tribune being the lack of open source software to check for biases. More worries about Amazon, the real one!, with Bolsonaro turning his indifference if not active support of the widespread forest fires into a nationalist campaign. And cutting 80,000 science scholarships. Worries on the ethnic biases in genetic studies and the All of Us study‘s attempt to correct that (study run by a genetic company called Color, which purpose is to broaden the access to genetic counseling to under-represented populations). Further worries on fighting self-citations (with John Ioannidis involved in the analysis). With examples reaching a 94% rate for India’s most cited researcher.

Nature snippets

In the August 1 issue of Nature I took with me to Japan, there were many entries of interest. The first pages included a tribune (“personal take on events”) by a professor of oceanography calling for a stop to the construction of the TMT telescope on the Mauna Kea mountain. While I am totally ignorant of the conditions of this construction and in particular of the possible ecological effects on a fragile altitude environment, the tribune is fairly confusing invoking mostly communitarian and religious, rather than scientific ones. And referring to Western science and Protestant missionaries as misrepresenting a principle of caution. While not seeing the contradiction in suggesting the move of the observatory to the Canary Islands, which were (also) invaded by Spanish settlers in the 13th century.

Among other news, Indonesia following regional tendencies to nationalise research by forcing foreign researchers to have their data vetted by the national research agency and to include Indonesian nationals in their projects. And, although this now sounds stale news, the worry about the buffoonesque Prime Minister of the UK. And of the eugenic tendencies of his cunning advisor… A longer article by Patrick Riley from Google on three problems with machine learning, from splitting the data inappropriately (biases in the data collection) to hidden variables (unsuspected confounders) to mistaking the objective (impact of the loss function used to learn the predictive function). (Were these warnings heeded in the following paper claiming that deep learning was better at predicting kidney failures?)  Another paper of personal interest was reporting a successful experiment in Guangzhou, China, infecting tiger mosquitoes with a bacteria to make the wild population sterile. While tiger mosquitoes have reached the Greater Paris area,  and are thus becoming a nuisance, releasing 5 million more mosquitoes per week in the wild may not sound like the desired solution but since the additional mosquitoes are overwhelmingly male, we would not feel the sting of this measure! The issue also contained a review paper on memory editing for clinical treatment of psychopathology, which is part of the 150 years of Nature anniversary collection, but that I did not read (or else I forgot!)

unimaginable scale culling

Despite the evidence brought by ABC on the inefficiency of culling in massive proportions the British Isles badger population against bovine tuberculosis, the [sorry excuse for a] United Kingdom government has permitted a massive expansion of badger culling, with up to 64,000 animals likely to be killed this autumn… Since the cows are the primary vectors of the disease, what about starting with these captive animals?!

Nature tidbits

Before returning a few older issues of Nature to the coffee room of the maths department, a quick look brought out the few following items of interests, besides the great cover above:

• France showing the biggest decline in overal output among the top 10 countries in the Nature Index Annual Tables.
• A tribune again the EU’s Plan S, towards funding (private) publishers directly from public (research) money. Why continuing to support commercial journals one way or another?!
• A short debate on geo-engineering towards climate control, with the dire warning that “little is known about the consequences” [which could be further damaging the chances of human survival on this planet].
• Another call for the accountability of companies designing AI towards fairness and unbiasedness [provided all agree on the meaning of these terms]
• A study that argues that the obesity epidemics is more prevalent in rural than urban areas due to a higher recourse to junk food in the former.
• A data mining venture in India to mine [not read] 73 million computerised journal articles, which is not yet clearly legal as the publishers object to it. Although the EU (and the UK) have laws authorising mining for non-commercial goals. (And India has looser regulations wrt copyright.)

FALL [book review]

The “last” book I took with me to Japan is Neal Stephenson’s FALL. With subtitle “Dodge in Hell”. It shares some characters with REAMDE but nothing prevents reading it independently as a single volume. Or not reading it at all! I am rather disappointed by the book and hence  sorry I had to carry it throughout Japan and back. And slightly X’ed at Nature writing such a positive review. And at The Guardian. (There is a theme there, as I took REAMDE for a trip to India with a similar feeling at the end. Maybe the sheer weight of the book is pulling my morale down…) The most important common feature to both books is the game industry, since the main (?) character is a game company manager, who is wealthy enough to ensure the rest of the story holds some financial likelihood. And whose training as a game designer impacts the construction of the afterlife that takes a good (or rather terrible) half of the heavy volume. The long minutes leading to his untimely death are also excruciatingly rendered (with none of the experimental nature of Leopold Bloom’s morning). With the side information that Dodge suffers from ocular migraine, a nuisance that visits me pretty regularly since my teenage years! The scientific aspects of the story are not particularly exciting either, since the core concept is that by registering the entire neuronal network of the brain of individuals after their death, a computer could revive them by simulating this network. With dead people keeping their personality if very little of their memories. And even more fanciful, interacting between them and producing a signal that can be understood by (living) humans. Despite having no sensory organs. The reconstruction of a world by the simulated NNs is unbearably slow and frankly uninteresting as it reproduces both living behaviours and borrows very heavily from the great myths, mostly Greek, with no discernible depth. The living side of the story is not much better, although with a little touch of the post-apocalyptic flavour I appreciated in Stephenson. But not enough to recover from the fall.

Among other things that set me off with the book, the complete lack of connection with the massive challenges currently facing humanity. Energy crisis? climate change? Nope. Keep taking an hydroplane to get from Seattle to islands on Puget Sound? Sure. Spending abyssal amounts of energy to animate this electronic Hades? By all means. More and more brittle democracies? Who cares, the Afterworld is a pantheon where gods clash and rule lower beings. Worse, the plot never reaches beyond America, from the heavily focused philosophical or religious background to the character life trajectories. Characters are surprisingly unidimensional, with no default until they become evil. Or die. Academics are not even unidimensional. For instance Sophie’s thesis defence is at best a chat in a café… And talks at a specialist workshop switch from impressive mathematical terms to a 3D representation of the activity of the simulated neuronal networks. Whille these few individuals keep impacting the whole World for their whole life. And beyond… By comparison, the Riverworld series of Phillip José Farmer (that I read forty years ago) is much more enjoyable as a tale of the Afterworld, even if one can object at “famous” people been central to the action. At least there are more of them and, judging from their (first) life, they may have interesting and innovative to say.