The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

Computing the ground state of interacting quantum matter is a long-standing challenge,
especially for complex two-dimensional systems. Recent developments have highlighted the …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Given access to accurate solutions of the many-electron Schrödinger equation, nearly all
chemistry could be derived from first principles. Exact wave functions of interesting chemical …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …