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Quantum-scale thermodynamics offers a tighter definition of entropy

A new, microscopic formulation of the second law of thermodynamics for coherently driven quantum systems has been proposed by researchers in Switzerland and Germany. The researchers applied their formulation to several canonical quantum systems, such as a three-level maser. They believe the result provides a tighter definition of entropy in such systems, and could form a basis for further exploration.

In any physical process, the first law of thermodynamics says that the total energy must always be conserved, with some converted to useful work and the remainder dissipated as heat. The second law of thermodynamics says that, in any allowed process, the total amount of heat (the entropy) must always increase.

“I like to think of work being mediated by degrees of freedom that we control and heat being mediated by degrees of freedom that we cannot control,” explains theoretical physicist Patrick Potts of the University of Basel in Switzerland. “In the macroscopic scenario, for example, work would be performed by some piston – we can move it.” The heat, meanwhile, goes into modes such as phonons generated by friction.

Murky at small scales

This distinction, however, becomes murky at small scales: “Once you go microscopic everything’s microscopic, so it becomes much more difficult to say ‘what is it that that you control – where is the work mediated – and what is it that you cannot control?’,” says Potts.

Potts and colleagues in Basel and at RWTH Aachen University in Germany examined the case of optical cavities driven by laser light, systems that can do work: “If you think of a laser as being able to promote a system from a ground state to an excited state, that’s very important to what’s being done in quantum computers, for example,” says Potts. “If you rotate a qubit, you’re doing exactly that.”

The light interacts with the cavity and makes an arbitrary number of bounces before leaking out. This emergent light is traditionally treated as heat in quantum simulations. However, it can still be partially coherent – if the cavity is empty, it can be just as coherent as the incoming light and can do just as much work.

In 2020, quantum optician Alexia Auffèves of Université Grenoble Alpes in France and colleagues noted that the coherent component of the light exiting a cavity could potentially do work. In the new study, the researchers embedded this in a consistent thermodynamic framework. They studied several examples and formulated physically consistent laws of thermodynamics.

In particular, they looked at the three-level maser, which is a canonical example of a quantum heat engine. However, it has generally been modelled semi-classically by assuming that the cavity contains a macroscopic electromagnetic field.

Work vanishes

“The old description will tell you that you put energy into this macroscopic field and that is work,” says Potts, “But once you describe the cavity quantum mechanically using the old framework then – poof! – the work is gone…Putting energy into the light field is no longer considered work, and whatever leaves the cavity is considered heat.”

The researchers new thermodynamic treatment allows them to treat the cavity quantum mechanically and to parametrize the minimum degree of entropy in the radiation that emerges – how much radiation must be converted to uncontrolled degrees of freedom that can do no useful work and how much can remain coherent.

The researchers are now applying their formalism to study thermodynamic uncertainty relations as an extension of the traditional second law of thermodynamics. “It’s actually a trade-off between three things – not just efficiency and power, but fluctuations also play a role,” says Potts. “So the more fluctuations you allow for, the higher you can get the efficiency and the power at the same time. These three things are very interesting to look at with this new formalism because these thermodynamic uncertainty relations hold for classical systems, but not for quantum systems.”

“This [work] fits very well into a question that has been heavily discussed for a long time in the quantum thermodynamics community, which is how to properly define work and how to  properly define useful resources,” says quantum theorist Federico Cerisola of the UK’s University of Exeter. “In particular, they very convincingly argue that, in the particular family of experiments they’re describing, there are resources that have been ignored in the past when using more standard approaches that can still be used for something useful.”

Cerisola says that, in his view, the logical next step is to propose a system – ideally one that can be implemented experimentally – in which radiation that would traditionally have been considered waste actually does useful work.

The research is described in Physical Review Letters.  

The post Quantum-scale thermodynamics offers a tighter definition of entropy appeared first on Physics World.

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The Hidden Math of Ocean Waves

The math behind even the simplest ocean waves is notoriously uncooperative. A team of Italian mathematicians has made major advances toward understanding it.

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Motion blur brings a counterintuitive advantage for high-resolution imaging

Three pairs of greyscale images, showing text, a pattern of lines, and an image. The left images are blurred, the right images are clearer
Blur benefit: Images on the left were taken by a camera that was moving during exposure. Images on the right used the researchers’ algorithm to increase their resolution with information captured by the camera’s motion. (Courtesy: Pedro Felzenszwalb/Brown University)

Images captured by moving cameras are usually blurred, but researchers at Brown University in the US have found a way to sharpen them up using a new deconvolution algorithm. The technique could allow ordinary cameras to produce gigapixel-quality photos, with applications in biological imaging and archival/preservation work.

“We were interested in the limits of computational photography,” says team co-leader Rashid Zia, “and we recognized that there should be a way to decode the higher-resolution information that motion encodes onto a camera image.”

Conventional techniques to reconstruct high-resolution images from low-resolution ones involve relating low-res to high-res via a mathematical model of the imaging process. These effectiveness of these techniques is limited, however, as they produce only relatively small increases in resolution. If the initial image is blurred due to camera motion, this also limits the maximum resolution possible.

Exploiting the “tracks” left by small points of light

Together with Pedro Felzenszwalb of Brown’s computer science department, Zia and colleagues overcame these problems, successfully reconstructing a high-resolution image from one or several low-resolution images produced by a moving camera. The algorithm they developed to do this takes the “tracks” left by light sources as the camera moves and uses them to pinpoint precisely where the fine details must have been located. It then reconstructs these details on a finer, sub-pixel grid.

“There was some prior theoretical work that suggested this shouldn’t be possible,” says Felzenszwalb. “But we show that there were a few assumptions in those earlier theories that turned out not to be true. And so this is a proof of concept that we really can recover more information by using motion.”

Application scenarios

When they tried the algorithm out, they found that it could indeed exploit the camera motion to produce images with much higher resolution than those without the motion. In one experiment, they used a standard camera to capture a series of images in a grid of high-resolution (sub-pixel) locations. In another, they took one or more images while the sensor was moving. They also simulated recording single images or sequences of pictures while vibrating the sensor and while moving it along a linear path. These scenarios, they note, could be applicable to aerial or satellite imaging. In both, they used their algorithm to construct a single high-resolution image from the shots captured by the camera.

“Our results are especially interesting for applications where one wants high resolution over a relatively large field of view,” Zia says. “This is important at many scales from microscopy to satellite imaging. Other areas that could benefit are super-resolution archival photography of artworks or artifacts and photography from moving aircraft.”

The researchers say they are now looking into the mathematical limits of this approach as well as practical demonstrations. “In particular, we hope to soon share results from consumer camera and mobile phone experiments as well as lab-specific setups using scientific-grade CCDs and thermal focal plane arrays,” Zia tells Physics World.

“While there are existing systems that cameras use to take motion blur out of photos, no one has tried to use that to actually increase resolution,” says Felzenszwalb. “We’ve shown that’s something you could definitely do.”

The researchers presented their study at the International Conference on Computational Photography and their work is also available on the arXiv pre-print server.

The post Motion blur brings a counterintuitive advantage for high-resolution imaging appeared first on Physics World.

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Bayes’ rule goes quantum

How would Bayes’ rule – a technique to calculate probabilities – work in the quantum world? Physicists at the National University of Singapore, Japan’s University of Nagoya, and the Hong Kong University of Science and Technology in Guangzhou have now put forward a possible explanation. Their work could help improve quantum machine learning and quantum error correction in quantum computing.

Bayes’ rule is named after Thomas Bayes who first defined it for conditional probabilities in “An Essay Towards Solving a Problem in the Doctrine of Chances” in 1763.  It describes the probability of an event based on prior knowledge of conditions that might be related to the event. One area in which it is routinely used is to update beliefs based on new evidence (data). In classical statistics, the rule can be derived from the principle of minimum change, meaning that the updated beliefs must be consistent with the new data while only minimally deviating from the previous belief.

In mathematical terms, the principle of minimum change minimizes the distance between the joint probability distributions of the initial and updated belief. Simply put, this is the idea that for any new piece of information, beliefs are updated in the smallest possible way that is compatible with the new facts. For example, when a person tests positive for Covid-19, they may have suspected that they were ill, but the new information confirms this. Bayes’ rule is a therefore way to calculate the probability of having contracted Covid-19 based not only on the test result, and the chance of the test yielding a false negative, but also on the patient’s initial suspicions.

Quantum analogue

Quantum versions of Bayes’ rule have been around for decades, but the approach through the minimum change principle had not been tried before. In the new work, a team led by Ge Bai, Francesco Buscemi and Valerio Scarani set out to do just that.

“We found which quantum Bayes’ rule is singled out when one maximizes the fidelity (which is equivalent to minimizing the change) between two processes,” explains Bai. “In many cases, the solution is the ‘Petz recovery map’, proposed by Dénes Petz in the 1980s and which was already considered as being one of the best candidates for the quantum Bayes’ rule. It is based on the rules of information processing, crucial not only for human reasoning, but also for machine learning models that update their parameters with new data.”

Quantum theory is counter-intuitive, and the mathematics is hard, says Bai. “Our work provides a mathematically sound way to update knowledge about a quantum system, rigorously derived from simple principles of reasoning, he tells Physics World. “It demonstrates that the mathematical description of a quantum system—the density matrix—is not just a predictive tool, but is genuinely useful for representing our understanding of an underlying system. “It effectively extends the concept of gaining knowledge, which mathematically corresponds to a change in probabilities, into the quantum realm.”

A conservative stance

The “simple principles of reasoning” encompass the minimum change principle, adds Buscemi. “The idea is that while new data should lead us to update our opinion or belief about something, the change should be as small as possible, given the data received.

“It’s a conservative stance of sorts: I’m willing to change my mind, but only by the amount necessary to accept the hard facts presented to me, no more.”

“This is the simple (yet powerful) principle that Ge mentioned,” he says, “and it guides scientific inference by preventing unwanted biases from entering the reasoning process.”

An axiomatic approach to the Petz recovery map

While several quantum versions of the Bayes’ rule have been put forward before now, these were mostly based on the fact of having analogous properties to their classical counterpart, adds Scarani. “Recently, Francesco and one co-author proposed an axiomatic approach to the most frequently-used quantum Bayes rule, the one using the Petz recovery map. Our work is the first to derive a quantum Bayes rule from an optimization principle, which works very generally for classical information, but which has been used here for the first time in quantum information.

The result is very intriguing, he says: “we recover the Petz map in many cases, but not all. If we take that our new approach is the correct way to define a quantum Bayes rule, then previous constructions based on analogies were correct very often, but not quite always; and one or more of the axioms are not to be enforced after all. Our work is therefore is a major advance, but it is not the end of the road – and this is nice.”

Indeed, the researchers say they are now busy further refining their quantum Bayes’ rule. They are also looking into applications for it. “Beyond machine learning, this rule could be powerful for inference—not just for predicting the future but also retrodicting the past,” says Bai. “This is directly applicable to problems in quantum communication, where one must recover encoded messages, and in quantum tomography, where the goal is to infer a system’s internal state from observations.

“We will be using our results to develop new, hopefully more efficient, and mathematically well-founded methods for these tasks,” he concludes.

The present study is detailed in Physical Review Letters.

The post Bayes’ rule goes quantum appeared first on Physics World.

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