What's curious about quantum systems is the sole focus by the press on said tech being the end-all for breaking encryption instead of discussing why QC would also be perfect for AI-driven neural nets and ray tracing due to the inherent parallelism of qubits being in superposition to enable quantum computers to escape the limitations of serial processing forever.
For Valeria Saggio to boot up the computer in her former Vienna lab, she needed a special crystal, only as big as her fingernail. Saggio would place it gently into a small copper box, a tiny electric oven, which would heat the crystal to 77 degrees Fahrenheit. Then she would switch on a laser to bombard the crystal with a beam of photons.
This crystal, at this precise temperature, would split some of those photons into two photons. One of these would go straight to a light detector, its journey finished; the other would travel into a tiny silicon chip — a quantum computing processor. Miniature instruments on the chip could drive the photon down different paths, but ultimately there were only two outcomes: the right way, and the many wrong ways. Based on the result, her processor could choose another path and try again.
The sequence feels more Rube Goldberg than Windows, but the goal was to have a quantum computer teach itself a task: Find the right way out. For Saggio, a quantum physicist who moved to the Massachusetts Institute of Technology a few weeks ago, the project was akin to sticking a robot in a maze. The computer must learn the right path without any prior knowledge of where to turn along the way. It’s not too hard a chore — a normal classical computer could brute-force its way through dead ends and lucky guesses. But Saggio wondered, “Can quantum mechanics help?” She and her collaborators showed last year that it can.
Crucially, the chip is not just moving through faster cycles of trial-and-error, said Lucas Lamata, a quantum machine learning expert at the University of Seville. “The novelty in this paper is that they show a speedup in learning. [It’s] an important breakthrough.” Quantum mechanics makes the system learn in fewer steps. In that sense, it shows in an experiment what Temme’s theoretical speedup promised: Quantum physics can outwit — not just outrun — classical computing.
It's all about kernels.
The story of MAGI Mathematical Applications Group Incorporated begins with the “simple” question, “What paths would radiation take when a 20 megaton nuke goes off? Hypothetical target? NYC."
MAGI was tasked to answer the question by inventing
ray tracing to track the radiation paths of the nuke’s blast to the environment but also to apply the same technique to imaging as a nuke is a single energy source like a light bulb and blast and light rays propagate exactly the same but with the difference being light rays nondestructively intersect with objects and react according to the kind of characteristics the objects in question may possess like color, texture, transparency and reflectivity, using the laws of physics to generate an accurate 3D rendering of the scene in question. Computationally intense, ray tracing, and it's even more complex sibling, radiosity, are the most accurate ways to render high-resolution 3D scenes known to science.
Note, in reality, rays propagate from light sources to illuminate the scene in question.
Which means QC would be perfect for this kind of intense image creation without question.
It's only the beginning. :)
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