Recent advancements in artificial intelligence (AI) are challenging the traditional dominance of quantum computing in fields such as fundamental physics, chemistry, and materials science.

Giuseppe Carleo, a computational physics professor at EPFL, coauthored a recent paper published in Science, which highlights neural networks as the leading technique for modeling materials with strong quantum properties.

While the largest quantum devices have recently surpassed the thousand-qubit mark, practical applications may require tens of thousands to millions of qubits to realize their full potential.

Despite the promise of AI, some researchers caution that the effectiveness of neural networks can vary, making it difficult to predict their limitations in solving complex problems.

Ongoing research in quantum computing is considered vital for future scientific breakthroughs, even though there are currently no commercial applications.

While quantum computing and AI are often viewed as competitors in quantum chemistry, some experts believe they will ultimately complement each other in solving complex problems.

For strongly correlated systems, where interactions significantly affect behavior, traditional methods like density functional theory (DFT) struggle, but AI is making notable progress.

The rapid progress in AI applications has led researchers to speculate whether AI could address significant challenges in chemistry and materials science before quantum computers are fully developed.

DeepMind has demonstrated AI's capability to model excited states in quantum systems, which could have implications for advancements in solar cells and sensors.

Tech companies have invested billions in quantum computing, anticipating breakthroughs in various fields, including finance, drug discovery, and logistics.

Data availability remains a significant barrier to fully exploiting AI's potential in these fields, exemplified by Meta's dataset of 118 million DFT calculations.

Experts agree that quantum computers are likely to play a future role in simulating complex quantum system evolutions, although practical applications may not emerge soon.