What do you get when you combine the deep learning AI field with quantum computing? You get into the new realm of increasing computational power exponentially that might one day solve the current unsolvable problems.
As of today, cell phones, computers, and supercomputers operate in a binary system where the classical bit stores information as zero or one. In quantum computing, a quantum bit (or qubit) can store both zero and one at the same time, a property called superposition. This special property can process many calculations simultaneously, which allows quantum computing to solve complex problems more efficiently.
qAIntum.ai is a company at the forefront of building large language models (LLMs) with quantum computing. The company’s cutting-edge innovation is the Quantum Transformer Architecture where quantum neural networks (QNNs) are integrated with traditional transformer models. The company’s objective is to build Quantum Large Language Models (QLLMs) with a potential creation of more sophisticated and accurate language models.
Then, there’s the Quantum AI group at Google that has been working on quantum computing for almost two decades. This group has achieved quantum supremacy in 2019 where computation took just 200 seconds that would have taken the most powerful supercomputers thousands of years to calculate. In 2023, the group achieved another milestone quantum error correction by showing that it’s possible to reduce errors by increasing the number of qubits.
Whether quantum computing AI is hype or reality, I have to give respect to the brilliant scientists and researchers who dedicate their professional lives to work on these high risk theoretical concepts. These pioneering researches might not have clear real-world business applications initially, but if succeeded could advance the technological progress for humanity.
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