Quantum is not a magic speed‑button for AI. The pragmatic path today is hybrid: classical AI for data prep, feature learning, and orchestration; quantum subroutines for hard combinatorial search, sampling, and certain linear‑algebra kernels where devices permit. A reliable operating model is retrieve → reason → simulate → apply → observe: ground problems and constraints; … Read more
Quantum computing won’t replace AI SaaS; it will augment specific bottlenecks where quantum‑accelerated subroutines deliver better optimization, simulation, or security. Expect a hybrid stack: classical CPUs/GPUs handle training and inference, while quantum services are invoked selectively for tasks like combinatorial optimization, Monte‑Carlo acceleration, cryptography transitions, and high‑fidelity simulations that inform AI decisions. The near‑term impact … Read more