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We've a last paper out today, before the times of comfort, joy, and gluttony set in... https://arxiv.org/abs/1612.06566 . In it, we describe a new way to extract randomness from quantum physics, and demonstrate it experimentally too.

As I've written about before (e.g. https://plus.google.com/+JonatanBohrBrask/posts/6eVRfrT4aqs), random numbers are important for cryptography (e.g. keeping your credit card details safe), computer simulations (of anything from your local weather report to astrophysics), and gambling. But good random numbers are not that easy to create. Good means that no one else should be able to predict them in advance.

Something may seem random to you but perfectly non-random to someone else. Say I'm a magician and I practised coin flipping a lot. When I flip a coin, by giving it just the right spin I can make it land on heads or tails as I wish. To you the flip looks random, but to me the outcome is completely predictable. What we want is a guarantee that the numbers we generate are random to anyone - we want to sure that no magician could be playing tricks on us.

Ideally, we would like to have to assume as little as possible about what these 'anyone' can know about the devices used to make the numbers. The less we need to assume, the less risk that any of our assumptions turn out to be wrong, and so the stronger our guarantee on the randomness.

In a classical world, knowing everything there is to know about a system at some point in time in principle allows predicting everything that will happen at all later times. The classical world is deterministic, and there is no randomness, unless we make assumptions about how much an observer knows. It is one of big surprises in quantum physics that there is fundamental randomness in nature. In quantum mechanics it is impossible to predict the outcome of certain measurements even when you know all that can possibly be know about the devices used.

In fact, quantum physics allows us to guarantee randomness under a range of different strength of assumptions about the devices used. On one end of the scale, the measurements made by the devices are assumed to be known, and they are chosen such that their outcomes are unpredictable. In this case, the devices need to be well characterised, but they are relatively easy to implement and random numbers can be generated at very high rates (millions of bits per second). Commercial quantum randomness generators operate in this regime. On the other end of the scale, essentially nothing is assumed to be known about what the devices are doing. Randomness can be guaranteed just be looking at the statistics of the data the devices generate. This regime is known as 'device-independent', and offers an extremely secure form of randomness. However, it requires that the data violates a so-called Bell inequality. This is technologically very challenging to do without filtering the data in some way that might compromise the randomness. For this region, the rates that have been achieved so far for device-independent generation of random numbers are relatively low (some bits per minute).

In between the two extremes, there is room to explore - to look for a good set of assumptions which gives a strong guarantee on the randomness but still allows for reasonable rates to be realised in practice. With my colleagues in Geneva, we are doing this exploration, and implementing our ideas in the lab to check how practical they are.

In the new paper we look at a prepare-and-measure setup with two devices. One prepares quantum states, the other measures them. We make almost no assumptions about the measurement device, while something is known about the preparation device. It doesn't need to be fully characterised, but it is known that the quantum states it prepares are not too different from each other (which, in quantum physics, means that they cannot be perfectly distinguished by any measurement). With these assumptions, the guys in the lab was able to generate random numbers with very high rates - millions of bits per second, comparable to commercial devices :).

So, we've found a nice trade-off between trust in the devices, and random bit rate. There is still plenty of room to explore though. I've made a little plot in the 'space' of trust vs. bitrate. On the lower left, with low trust but also low rates, the yellow stars show the results from device-independent experiments. The yellow star in the top right shows a commercial quantum random number generator. It achieves a high rate, but requires more trust in the device. Our new paper is the top green star - it requires an intermediate level of trust and achieves a high rate. The other green star is an experiment we did a little while back, using another set of assumptions and achieving a somewhat lower rate. Different assumptions may be appropriate in different situations, and so there is still lots of unknown territory in this plot.

The big question is, how close can we get to the sweet spot of low trust and high rate on the top left?

As I've written about before (e.g. https://plus.google.com/+JonatanBohrBrask/posts/6eVRfrT4aqs), random numbers are important for cryptography (e.g. keeping your credit card details safe), computer simulations (of anything from your local weather report to astrophysics), and gambling. But good random numbers are not that easy to create. Good means that no one else should be able to predict them in advance.

Something may seem random to you but perfectly non-random to someone else. Say I'm a magician and I practised coin flipping a lot. When I flip a coin, by giving it just the right spin I can make it land on heads or tails as I wish. To you the flip looks random, but to me the outcome is completely predictable. What we want is a guarantee that the numbers we generate are random to anyone - we want to sure that no magician could be playing tricks on us.

Ideally, we would like to have to assume as little as possible about what these 'anyone' can know about the devices used to make the numbers. The less we need to assume, the less risk that any of our assumptions turn out to be wrong, and so the stronger our guarantee on the randomness.

In a classical world, knowing everything there is to know about a system at some point in time in principle allows predicting everything that will happen at all later times. The classical world is deterministic, and there is no randomness, unless we make assumptions about how much an observer knows. It is one of big surprises in quantum physics that there is fundamental randomness in nature. In quantum mechanics it is impossible to predict the outcome of certain measurements even when you know all that can possibly be know about the devices used.

In fact, quantum physics allows us to guarantee randomness under a range of different strength of assumptions about the devices used. On one end of the scale, the measurements made by the devices are assumed to be known, and they are chosen such that their outcomes are unpredictable. In this case, the devices need to be well characterised, but they are relatively easy to implement and random numbers can be generated at very high rates (millions of bits per second). Commercial quantum randomness generators operate in this regime. On the other end of the scale, essentially nothing is assumed to be known about what the devices are doing. Randomness can be guaranteed just be looking at the statistics of the data the devices generate. This regime is known as 'device-independent', and offers an extremely secure form of randomness. However, it requires that the data violates a so-called Bell inequality. This is technologically very challenging to do without filtering the data in some way that might compromise the randomness. For this region, the rates that have been achieved so far for device-independent generation of random numbers are relatively low (some bits per minute).

In between the two extremes, there is room to explore - to look for a good set of assumptions which gives a strong guarantee on the randomness but still allows for reasonable rates to be realised in practice. With my colleagues in Geneva, we are doing this exploration, and implementing our ideas in the lab to check how practical they are.

In the new paper we look at a prepare-and-measure setup with two devices. One prepares quantum states, the other measures them. We make almost no assumptions about the measurement device, while something is known about the preparation device. It doesn't need to be fully characterised, but it is known that the quantum states it prepares are not too different from each other (which, in quantum physics, means that they cannot be perfectly distinguished by any measurement). With these assumptions, the guys in the lab was able to generate random numbers with very high rates - millions of bits per second, comparable to commercial devices :).

So, we've found a nice trade-off between trust in the devices, and random bit rate. There is still plenty of room to explore though. I've made a little plot in the 'space' of trust vs. bitrate. On the lower left, with low trust but also low rates, the yellow stars show the results from device-independent experiments. The yellow star in the top right shows a commercial quantum random number generator. It achieves a high rate, but requires more trust in the device. Our new paper is the top green star - it requires an intermediate level of trust and achieves a high rate. The other green star is an experiment we did a little while back, using another set of assumptions and achieving a somewhat lower rate. Different assumptions may be appropriate in different situations, and so there is still lots of unknown territory in this plot.

The big question is, how close can we get to the sweet spot of low trust and high rate on the top left?

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Captain Skæg and his pancake-eating friends as sand sculptures.

:-)

:-)

Two fully grown guys hunting Pokémon Copenhagen style: One cycling his cargo bike, his friend sitting in the box with a phone in each hand.

:-)

:-)

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Please do read the full answer. Pure horror.

The

sample:

*best*Stackoverflow answer to "how do I parse my HTML with RegEx?"sample:

*"[...]The force of regex and HTML together in the same conceptual space will destroy your mind like so much watery putty. If you parse HTML with regex you are giving in to Them and their blasphemous ways which doom us all to inhuman toil for the One whose Name cannot be expressed in the Basic Multilingual Plane, he comes.[...]"* Post has shared content

Physicists at Aarhus University take advantage of human intuition to optimize quantum mechanical experiments.

**Well, Well!**

When provided with a suitable interface to collaborate with them, humans can help computers find novel solution strategies and starting points for more efficient numerical solutions, to problems in

**Quantum Computing**.

*Sherson’s team got around 300 people to play this level a total of 12,000 times on a volunteer-research platform called ScienceAtHome. The researchers then fed the human solutions into a computer for further refinement. Not only were more than half of the human-inspired solutions more efficient than those produced by just computer algorithms, but the two best hybrid strategies were faster than what the quickest computers had been able to achieve working alone. “I was completely amazed when we saw the results,” says Sherson.*

**More at picture link**

*This fun and addictive game lets you make an impact without any previous knowledge of the wonderful world of atoms. We train you through introductory games and use your ingenuity—not textbook knowledge—in the Quantum Moves challenges.*

*Precision and accuracy are key elements for obtaining high quality data that can be then transferred into actual laser movements in the lab. Therefore, some of the Quantum Moves game missions are quite challenging; they have to be completed close to perfection in a very limited time. Don't be discouraged—we know you can do it!*

**More and download here (Windows, OS X, Android, iOS):**https://goo.gl/uxqUkx

*Players discover novel solution strategies which numerical optimizations fail to find. Guided by player strategies, a new low-dimensional heuristic optimization method is formed, efficiently outperforming the most prominent established methods. We have developed a low-dimensional rendering of the optimization landscape showing a growing complexity when the player solutions get fast. These fast results offer new insight into the nature of the so-called Quantum Speed Limit. We believe that an increased focus on heuristics and landscape topology will be pivotal for general quantum optimization problems beyond the type presented here.*

Paper (open): https://goo.gl/V6tvjd

Repurposing Neural Structures: https://goo.gl/Hwltzb

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Splendid!

**Fourier Analysis**Look and Listen

For this one headphones and a microphone are recommended!

And a fairly powerful, up-to-date computer supporting WebGL and the Web Audio API in Chrome or Firefox is needed to enjoy another of +Steven Wittens step-by-step visual mathematical explorations. This time he takes us first through some basics of visualising the magic of exponents and logs and then music starts and the beautiful explanation of Fourier Analysis begins.

*Presented at the Tools for Thought workshop, Recurse Center, NYC 2016*

*Hi, I'm Steven. I usually start with my website, aka that site with that header, as my defacto calling card. This effect is powered by WebGL, and consists of live geometry generated in JavaScript and streamed into the GPU. This way I can feed large amounts of data in very efficiently, in this case about 45,000 triangles.*

**More here:**http://goo.gl/D5mRcS

Related posts: https://goo.gl/D2Vn9M https://goo.gl/snAxjO

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Complete attention from the QPIT group at DTU (Technical University of Denmark) as the news of the gravitational wave observation at LIGO are announced.

Brilliant stuff!! Much better than I expected.

Brilliant stuff!! Much better than I expected.

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Internet of Things in your bathroom? Slick, clever bathroom DIY project in progress by +Max Braun.

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Star Wars IV retold using clips from movie history. Brilliant!

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Keep rolling, rolling, rolling...

Lifelike Sisyphus model in LEGO by Jason Allemann. A simple, but inventive gearing brings him to life, rolling his boulder forever on top of a pedestal with beautiful reliefs from his life.

Be sure to watch the video:

http://jkbrickworks.com/sisyphus-kinetic-sculpture/

http://lego.gizmodo.com/watching-a-lego-sisyphus-perpetually-push-a-boulder-is-1745614147

Lifelike Sisyphus model in LEGO by Jason Allemann. A simple, but inventive gearing brings him to life, rolling his boulder forever on top of a pedestal with beautiful reliefs from his life.

Be sure to watch the video:

http://jkbrickworks.com/sisyphus-kinetic-sculpture/

*via*Leg Godt, Gizmodohttp://lego.gizmodo.com/watching-a-lego-sisyphus-perpetually-push-a-boulder-is-1745614147

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