… With AI-powered digital twins, making it faster just requires some more resources. “If you can now change the configuration of this system to a future state and run those training sessions again and again and again, all of a sudden the machine that you're now building virtually and the decoders and…
… The mission remains the same, Kapur says. “We're going to advance the quantum economy, quantum era, by focusing on the top level of the stack and accelerating application development,” he says. “But what we realized is that the process, in order to connect use cases to algorithms, is extraordinaril… …
… Another area is accelerating quantum by optimizing the circuits that run on the system. “There are an infinite number of ways you could enact a certain process, but using AI to optimize those circuits can reduce the time needed on the quantum machine, and it might even accelerate to the point where… …
… Top research labs around the globe are evaluating Maverick-2, as scientific organizations look to ensure that high-precision computing remains viable, efficient, and scalable in the face of AI-driven hardware shifts. …
… Atop the infrastructure is the orchestration layer that includes the Quantum Resource Management Interface QRMI , an open source library that abstracts away hardware-specific details and delivers APIs for quantum resource acquisition, task running, and systems monitoring. …
… There will have to be variants of Google TPUs and Amazon Trainiums aimed specifically at agentic AI inference and presenting a better balance between memory bandwidth and compute while also not sacrificing memory capacity. We will do a deeper dive down deeper into the hardware. …
… This is obviously a big change, and it is being driven mostly by the vast investments in AI training and inference systems, mostly by the hyperscalers, cloud builders, and AI model makers located in the United States and China. …
… This is why SambaNova Systems and the first generation of AI hardware startups are still in the game, despite the hegemony of the Nvidia GPU in the AI datacenter, and it is also why the company was able o raise $350 million in Series E funding to help build and push its new “Cerulean 2” SN50 reconf… …
… With a dataflow engine such as NextSilicon has invented, the hardware literally maps itself to the software rather trying to be a short order cook with too many orders but with 27 arms and eyes in the back of the head, flailing around to make up for the difficulties of getting the right data and th… …