Lifeboat Foundation

Managing the heat generated in electronics is a huge problem, especially with the constant push to reduce the size and pack as many transistors as possible in the same chip. The whole problem is how to manage such high heat fluxes efficiently. Usually, electronic technologies, designed by electrical engineers, and cooling systems, designed by mechanical engineers, are done independently and separately. But now, EPFL researchers have quietly revolutionized the process by combining these two design steps into one: They’ve developed an integrated microfluidic cooling technology together with the electronics that can efficiently manage the large heat fluxes generated by transistors. Their research, which has been published in Nature, will lead to even more compact electronic devices and enable the integration of power converters, with several high-voltage devices, into a single chip.

The best of both worlds

In this ERC-funded project, Professor Elison Matioli, his doctoral student Remco Van Erp, and their team from the School of Engineering’s Power and Wide-band-gap Electronics Research Laboratory (POWERLAB), began working to bring about a real change in designing electronic devices by conceiving the electronics and cooling together, right from the beginning. The group sought to extract the heat very near the regions that heat up the most in the device. “We wanted to combine skills in electrical and mechanical engineering in order to create a new kind of device,” says Van Erp.

The research team of Assistant Professor Masahiko Sato and Professor Yasushi Todo of the National Institutes of Natural Sciences (NINS) National Institute for Fusion Science (NIFS) has succeeded using computer simulation in reproducing the high-pressure plasma confinement observed in the Large Helical Device (LHD). This result has enabled highly accurate predictions of plasma behavior aimed at realizing an economical helical fusion reactor.

In order to realize fusion energy, we must confine high pressure plasma using the magnetic field for a long duration. Although higher pressure plasma can be confined by a stronger magnetic field, it costs more to generate a stronger magnetic field using electromagnetic coils. Therefore, if the magnetic field strength is the same, a device that can confine higher pressure plasma is economically desirable. Because the LHD has succeeded in maintaining high-pressure plasma, there is great expectation in realizing a helical fusion reactor.

Design research for a future fusion reactor is performed based on computer simulations predicting the behavior of magnetically confined plasma. We require highly accurate simulations. To confirm the accuracy, the simulations are required to reproduce the experimental results obtained by the existing devices. However, the simulations had not reproduced the experimental results obtained by the LHD showing that high-pressure plasma is maintained. This has been a serious problem for the design research for an economical helical fusion reactor.

A pair of Danish computer scientists have solved a longstanding mathematics puzzle that lay dormant for decades, after researchers failed to make substantial progress on it since the 1990s.

The abstract problem in question is part of what’s called graph theory, and specifically concerns the challenge of finding an algorithm to resolve the planarity of a dynamic graph. That might sound a bit daunting, so if your graph theory is a little rusty, there’s a much more fun and accessible way of thinking about the same inherent ideas.

Going as far back as 1913 – although the mathematical concepts can probably be traced back much further – a puzzle called the three utilities problem was published.

A reputable leaker posted feasible specifications for an Nvidia GeForce RTX 3060 Ti or GeForce RTX 3060 Super graphics card.

To maintain its leadership in quantum computing technology, the US must embrace a culture shift that fuses three important elements: science, a roadmap, and agility.

Amazon’s quantum computing service is currently good for learning about quantum computing and developing NISQ-regime quantum algorithms, but stay tuned.

Circa 2014 o,.o.

The Defense Advanced Research Projects Agency, or DARPA, has announced the start of a five-year, $26 million effort to develop brain implants that can treat mental disease with deep-brain stimulation.

The hope is to implant electrodes in different regions of the brain along with a tiny chip placed between the brain and the skull. The chip would monitor electrical signals in the brain and send data wirelessly back to scientists in order to gain a better understanding of psychological diseases like Post-Traumatic Stress Disorder (PTSD). The implant would also be used to trigger electrical impulses in order to relieve symptoms.

Continue reading “DARPA teams begin work on tiny brain implant to treat PTSD” »

He was also scared because the experiment showed, in a concrete way, that humanity was at the dawn of a new era, one in which our thoughts could theoretically be snatched from our heads. What was going to happen, Dr. Gallant wondered, when you could read thoughts the thinker might not even be consciously aware of, when you could see people’s memories?

Opinion

Continue reading “The Brain Implants That Could Change Humanity” »

Simulating chemical processes is one of the most promising applications of quantum computers, but problems with noise have prevented nascent quantum systems from outperforming conventional computers on such tasks. Now, researchers at Google have taken a major step towards this goal by using the most powerful quantum computer yet built to successfully implement a protocol for calculating the electronic structure of a molecule. The results may form a blueprint for complex, useful calculations on quantum computers affected by noise.

In October 2019, Google announced to great fanfare that its 53-qubit Sycamore computer had achieved quantum advantage. This means that a quantum computer can solve at least one problem much faster than any conventional supercomputer. However, Google researchers openly acknowledged that the problem Sycamore solved (sampling the outcome of a random quantum circuit) is easy for a quantum computer but difficult for a conventional supercomputer — and had little practical use.

What researchers would really like to do is use quantum computers to solve useful problems more effectively than possible with conventional computers: “Sycamore is extremely programmable and, in principle, you really can run any algorithm on it…In this sense, it’s a universal quantum computer,” explains team member Ryan Babbush of Google Research, “However, there’s a heavy caveat: there’s still noise affecting the device and as a result we’re still limited in the size of circuit we can implement.” Such noise, which results from classical sources such as thermal interference, can destroy the fragile superpositions crucial to quantum computation: “We can implement a completely universal circuit before the noise catches up and eventually destroys the computation,” says Babbush.