I’ll just say it: What the hell is going on with AI “reasoning”? Sorry for the air quotes. That punctuational side-eye was more common in 2024, when the specially trained cousins of LLMs now known as “large reasoning models,” or LRMs, were still new. Nowadays it may seem downright churlish, though, given that a “general-purpose reasoning model” from OpenAI solved a famous open mathematical…
There are many algorithms for which it has been mathematically proven that a quantum computer can generate results that would take a classical computer an unreasonable amount of time to generate. Unfortunately, today's quantum computers either can't run those algorithms or can only run simplified versions that classical computers can also handle. This has left the field facing a challenging question: Can we demonstrate the promise of quantum computers on today's noisy, limited hardware?
That's a more difficult question than it may first appear. If you generate a result that's out of reach of today's regular computers, it may not be possible to verify that you got the right result. And given that today's quantum computers are somewhat error-prone, getting the wrong result is a distinct possibility. Further, in the absence of a mathematical proof of the capabilities of quantum hardware, it's possible that a better classical algorithm could outperform the quantum hardware.
These issues inspired IBM to launch a quantum advantage tracker. On Thursday, the company announced three new entries that it says clearly show a quantum advantage, each using a different approach to overcoming errors and validating quantum results. "Trusted computing when you can do classical simulations is irrelevant," IBM's Jay Gambetta told Ars. "Trusted computing when you can't do classical simulations is a big deal."
© IBM
If you follow quantum computing news for long enough, it can start to seem like any quantum system that can alternate between two well-separated energy states can be used as a qubit. Atoms, ions, photons, electrons, and manufactured devices all have their backers. One of the key things that attracts backers is the tech's ability to scale. We'll need a lot of high-quality qubits to start doing any complex computations, and the ability of any technology to get us there is the subject of debate.
So even as some technologies can now support thousands of qubits, some competitors are still working on a small handful of qubits—the companies behind them are convinced that they have the potential to scale more effectively.
One of those technologies involves quantum dots that hold a single electron. Their advantage is that we can manufacture them using the same tech we use to build traditional processors, an approach that has proven to be scalable. This week saw two new papers describing different ways of using quantum dots, one of which was appealing enough that IBM bought the company that developed it. Separately, another company has released a processor showing 100 individual electrons being held in diamond defects, technology that wasn't obvious could scale.
© HRL Laboratories
Many of today’s electronic devices — from the semiconductors in your cell phone to the photovoltaic cells in your solar panels — are built on thin-film substrates. The thin film is an electronically conductive material while the substrate is an inert material. Or is it?
Physicists and materials scientists have long assumed substrates do not react to electrical stimulus, but new research from the University of California San Diego Alex Frañó’s lab and a team of collaborators has shown that substrates are not inert after all. The discovery 1 has the potential to help engineers build the dense, three-dimensional, brain-inspired computer chips needed for more energy-efficient computing.
In a human brain, a vast network of neurons works across different regions, enabling every thought and action we take. Yet thoughts that are quick and simple for humans — recognizing faces, differentiating a cat from a dog or solving a captcha — can be devilishly hard and energy-intensive for computers. Quantum materials may provide a solution.
It was this quest to develop new materials that led Frañó’s lab to study vanadium dioxide thin-film devices. When a voltage is applied to the thin film, an electric filament forms — something similar happens when the Earth and atmosphere create a voltage strong enough to result in lightning. The filament in the thin film drives the device’s electrical spiking, much like the signals in our neurons.
This reaction has been studied countless times in labs around the world, but Frañó’s lab was implementing a new technique, developed by graduate student Elliot Kisiel, called dark-field X-ray microscopy. Kisiel, who spent most of his graduate studies at Argonne National Laboratory and is now a Mayer Postdoctoral Fellow there, developed the tool to combine the best of two worlds: electron microscopy and X-ray diffraction.
“Dark-field X-ray microscopy gives us the ability to look at an entire device in one image, which not only greatly improves how fast we can perform these measurements, but also allows us to study the areas surrounding the devices with high fidelity,” he stated.
Since the lenses for X-rays are often thick and, therefore, absorb most of the X-rays that pass through them, the team needed to look at something that provided more signal, which is why they decided to look at the substrate during some of their preliminary testing with this new technique.
What they saw was completely unexpected: not only was there a change in the thin film, but there was also a change in the substrate, showing for the first time that the two parts were sharing energy or “coupled.” This discovery went against everything scientists had assumed about substrates for decades.
Thin films are so called because they are vanishingly thin — around 100 nanometers — while the substrates they sit on can be 10,000 times thicker. Despite their lopsided sizes, in this experiment, the thin film was able to push and pull on the substrate. Frañó likened the discovery to a tree on a mountaintop being able to move the entire mountain. Furthermore, the substrate also acted on the thin film, each pushing and pulling on the other.
They spent the next four years making sure what they saw wasn’t a fluke. They not only reproduced the results doing the research exactly as they had done it the first time, but they also reproduced the results when they changed certain aspects of the sample, such as the thickness and material of the substrate. They also tested their results using different instrumentation. Much of that work was done at Argonne, which has a synchrotron facility that produces high brilliance X-rays, and at Brookhaven National Laboratory, home to the world’s only all-electric ultrafast electron microscope, which is able to capture exactly how a working device behaves under realistic operating conditions.
It wasn’t a fluke.
“The assumption that substrates are inert needs to be rethought,” Frañó stated. “Going forward, we have to assume that the substrate is undergoing changes when the film is. This is a transformational notion that counters decades of previous supposition.”
This paper is a companion to a paper that was published last year in ACS Nano 2. In that paper, they used dark-field X-ray microscopy to study the thin film, while this paper focuses on the substrate. Together they illustrate how one research result leads to the next research inquiry.
In Frañó’s case, the question he now asks himself is, “What can we do with this new substrate discovery? If a tree is moving the whole mountain, let’s find a way to capitalize on that.”
One idea is that the substrate can act as a medium that can couple materials on either end of the substrate. Currently, thin film devices are connected two-dimensionally, but if researchers could build on both sides of a substrate, it would allow for three-dimensionality, opening up the possibility of denser, more interconnected computer chips, and bringing us one step closer to the next generation of computing.
The post Substrate dynamics could power brain-inspired chips appeared first on Mapping Ignorance.
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