Google’s Quantum artificial intelligence lab recently revealed a new quantum processor that may pave the way for quantum computing to go mainstream. The new chip features 72 qubits according to Google and offers a compelling proof-of-concept for future, large-scale quantum computers.
“Our strategy is to explore near-term applications using systems that are forward compatible to a large-scale universal error-corrected quantum computer,” Google’s announcement reads.
All right, quantum computing is complicated, bizarre, and tough to explain but Google’s new Bristlecone chip is important for a few reasons. First of which, it’s designed to address one of the key problems facing quantum computing today: Error correction. Because traditional chips, like the one in your phone or your computer, or any other electronic device, all use Boolean logic — simple yes or no, on or off operations — error correction is fairly easy to implement. All of these traditional processors use redundancy to check for errors. Just copy a single bit of data three times, if one changes due to an error, your processor can just check the other two bits and make the correction.
That is an oversimplification but that is essentially how error correction works on regular processors. Now quantum processors use a different kind of logic which makes traditional error correction tricky. Instead of each bit having two potential states — on or off — a quantum bit or qubit has three. It can be on, off, or both, and you’ll only know which one it is once you look at it. This makes error-correction a huge problem for quantum computers. How can you tell if a bit of data is correct if looking at it might change its state?
“We chose a device of this size to be able to demonstrate quantum supremacy in the future, investigate first and second order error-correction using the surface code, and to facilitate quantum algorithm development on actual hardware,” Google’s announcement said.
Chips like Bristlecone aim to solve that problem by offering a testbed for quantum error correction on a chip with enough qubits to potentially achieve quantum supremacy — that is just a term for the first time a quantum computer can actually outperform a traditional supercomputer on a well-known computer science problem. According to Google, this new chip could be the one that does it — or at least some future version of Bristlecone.
After spending years with touchscreen Windows laptops, I've become rather accustomed to reaching out and tapping the screen if I just want to interact with something quickly. It's a small convenience, one that feels natural as soon as you get used to it. But whenever I use a MacBook for a bit, I inevitably find myself reaching for the screen before realizing that Apple has never offered this feature for its notebooks.
Apple has resisted touchscreen Macs for years, but that might finally change. The company is reportedly preparing to introduce its first touchscreen MacBook Pro, complete with an OLED display and a redesigned interface that accommodates touch input. Not to be confused with the new $2,000 M6 MacBook Pro, which just launched with minimal updates over its predecessor.
Acer’s Predator Atlas 8 finally has a US release date and price, several months after the company first showed off the handheld in May. The highlight is the Intel Arc G3 Extreme chip inside, which has been adopted by only a couple of other consoles besides the Atlas 8. This version will go on sale exclusively at Best Buy on November 1 for $1,299.99.
Acer originally said the Atlas 8 would arrive in North America in October, but left pricing open at the time. The launch came amid the ongoing DRAM crunch, which has made memory more expensive across the PC industry and complicated pricing for hardware makers.
Google DeepMind's AI world model Genie 4 can now build interactive worlds that you can explore and reshape as they run. It also generates ambient sounds, music, and dialogue that respond to your actions.
The new world model builds on Genie 3 with improved realism, more natural human movement, and better simulations of real-world physics. It could eventually help with everything from game development to training robots.