Quantum computers have long been viewed as the next big revolution that could transform various industries, including finance and drug development. Many have pinned their hopes on the use of quantum computers in physics and chemistry, expecting them to offer significant advantages over traditional computing technologies.
While the industry grapples with the technical challenges of developing efficient quantum systems, artificial intelligence based on neural networks is emerging as a strong contender in solving key problems in physics, chemistry, and material science. Although currently limited to modeling and data analysis, AI is making strides in these areas.
Giuseppe Carlo, a professor of computing physics at the Swiss Federal Technological Institute, highlights the impressive progress in modeling quantum systems using AI. In a recent article published in the journal Science, Carlo and his co-authors demonstrate that neuralite approaches are becoming a leading technique for modeling materials with distinct quantum properties.
Furthermore, Meta has presented a model trained on a vast dataset that ranked first in the neural network competition for discovering new materials. With such rapid progress, researchers are beginning to question whether most challenging tasks can be solved even before powerful quantum computers become a reality.
Carlo suggests that companies investing billions in quantum technologies may find their investments unjustified in the long run.
One of the primary advantages of quantum computers is their ability to perform certain calculations much quicker than traditional machines. However, to fully leverage this potential, quantum processors need to be significantly more powerful than current ones. While the largest quantum devices have surpassed a thousand qubits, overcoming traditional systems may require dozens or even millions of qubits.
Additionally, for many quantum algorithms with practical applications such as database search or optimization, the speed advantage is not as significant. A study involving Microsoft’s quantum computing lead revealed that theoretical benefits diminish when considering the slower performance of quantum hardware compared to modern chips.