Intel Innovations 2022 introduces the results of years of study in neural computing and quantum computing.
use qubits, instead of binary, on-off switches called bits we use in digital computers. The Quantum computer sees in simultaneous actions, whereas digital computers see only two variables. For example, a digital computer sees a coin as either heads or tails, one or the other, while a quantum computer sees both heads and tails at the same time, much like we would.
For this type of computer to work there needs to specialized software. Since there are relatively few quantum computers, the quantum SDK is designed as a platform to be simulated on a digital computer. The SDK uses a quantum runtime environment optimized for executing hybrid quantum-classical algorithms. That would mean that quantum simultaneous computing can be simulated in real-time for algorithms for something like financial modeling.
The goal of the Quantum SDK in its beta release is to advance the science behind Quantum computing, create new instances of software that can use the quantum process, and eventually create devices that use quantum computing software. This is a giant leap forward to computers that can see the world in holistic fashion rather than linear.Mike Davies is heading up the efforts to create a Neuromorphic computing intuitive system with the Loihi 2 second generation research chip.
The Loihi 2 is able to work in multiple instances, where four Loihi 2 chips are used in a series on one module. The modules can then be set up in series that can carry dozens of interwoven systems, creating an ecosystem that would mimic the intuitive structures of thousands of algorithms inferences per second. The development board that can be installed in series is the Kapoho Point, an eight chip, two sided Loihi 2 system.
The boards were being sent out to the member community involved in the research, Air Force Research Laboratory was the first to receive the Kapoho Point boards and have startling results in solving optimization problems. The boards can solve problems with up to eight million variables, while 10 times the speed of other neural systems, and are running on 1,000 times lower energy. This could mean that an AI system could run inference choices in computing at speeds that mimic brainwaves.
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