Abstract: We propose an efficient quantum subroutine for matrix multiplication that computes a state vector encoding the entries of the product of two matrices in superposition. The subroutine ...
Google DeepMind’s AI systems have taken big scientific strides in recent years — from predicting the 3D structures of almost every known protein in the universe to forecasting weather more accurately ...
There are central processing units (CPUs), graphics processing units (GPUs) and even data processing units (DPUs) – all of which are well-known and commonplace now. GPUs in particular have seen a ...
Matrix Composite Materials (Bristol, UK) will be exhibiting at the International Composites Summit (ICS) in Milton Keynes on 4-5th September. Their primary exhibit will be QUANTUM-ESC® carbon SMC ...
Since homomorphic encryption enables SIMD operations by packing multiple values into a vector of operations and enabling pairwise addition or multiplication operations, one (old) conventional method ...
A new technical paper titled “Scalable MatMul-free Language Modeling” was published by UC Santa Cruz, Soochow University, UC Davis, and LuxiTech. “Matrix multiplication (MatMul) typically dominates ...
Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...
Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...
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