Near-term quantum algorithms for many-body physics and material science: a path towards quantum utility

Quantum computing is emerging as a transformative paradigm, offering solutions to problems that are intractable for classical computers. This potential is particularly pronounced in many-body physics, quantum chemistry, and materials sciences, where the exponential complexity of classical methods can be efficiently addressed by quantum computing. Recent advancements in quantum technologies indicate that significant progress in these fields is achievable even with near-term noisy quantum computers. To realize this potential, noise-resilient quantum algorithms and error mitigation strategies have been developed and integrated into hybrid quantum-classical workflows, fostering a productive interplay between quantum and classical computational platforms.

In this talk, I will present recent advancements in quantum algorithms for many-body physics and quantum chemistry, emphasizing their relevance to near-term quantum computing. Key topics include error mitigation strategies critical for achieving accurate, utility-scale results, such as probabilistic error cancellation (PEC) and tensor network-based error mitigation (TEM). Additionally, embedding techniques that integrate quantum electronic structure methods with density functional theory will be discussed and dynamical mean field theory, enabling efficient problem partitioning while maintaining high accuracy.

These methods will be demonstrated through case studies on the computation of ground and excited-state properties in molecules and solids, as well as simulations of quantum dynamics. Finally, I will evaluate the performance of recent hardware calculations using IBM quantum computers and explore the future prospects of quantum computing in materials sciences.