Quantum Algorithms for Chemistry and Material Science Simulation: Bridging the Gap Between Classical and Quantum Approaches
CECAM-HQ-EPFL, Lausanne, Switzerland
This event is co-organised by the Center for Quantum Science and Engineering (QSE Center) and CECAM.
Quantum computing possesses immense potential for scientific discovery, offering controlled access to unexplored computational realms previously unattainable with classical computing . As quantum hardware development progresses rapidly, hybrid classical-quantum computing approaches in the Noisy Intermediate-Scale Quantum (NISQ) regime — and soon in the fault-tolerant regime — are poised to play a crucial role in near- and mid-term applications for scientific and technological advancements. Electronic structure applications have been identified as ideal candidates for classical-quantum computing, as demonstrated by seminal works from IBM  and Google . However, the most pressing challenges in the NISQ regime [4-6] lie in realizing significantly larger electronic structure calculations, moving beyond initial proof-of-principle realizations. Furthermore, other application domains are arising, including for instance, statistical mechanics, where quantum computing is expected to play an emerging role.
Atomistic simulations based on quantum mechanics have increasingly been used to predict the properties of functional materials, including catalysts, energy storage systems, and quantum information science materials . Density functional theory (DFT) is the predominant method for first-principles simulations, but it struggles to describe strongly-correlated electronic states. Alternative methods, such as dynamical mean-field theory, quantum Monte-Carlo, ab initio quantum chemistry, and variational methods relying on tensor-network or neural-network ansätze have been developed to address these limitations. Still, they remain computationally demanding for complex materials containing defects and interfaces.
Quantum computers offer the potential to efficiently encode and simulate quantum states of weakly and strongly-correlated molecules and materials, providing an advantage over conventional computers in computational time and storage size. As we enter the NISQ era, there remains a need to optimally cast the problem into a form that takes most advantage of the available quantum computing resources, while still relying on conventional computing. This hybrid strategy may be realized in several ways. The quantum state itself may be cast into a hybrid representation, like in the recently proposed entanglement forging schemes [7-8] that are currently driving the development of hybrid quantum processing units . One may also simplify the many-body problems by defining active regions with strongly-correlated electronic states, embedded in an environment described within approximate theory [10-12]. More generally, new algorithmic solutions may lead to better quantum variational representations, and more efficient sampling for a faster and more accurate execution of variational approaches.
This Workshop aims to gather the leading experts in hybrid quantum classical algorithms for the simulation of many-electron systems, and explore innovative algorithmic solutions for chemistry and material science simulations, focusing on bridging the gap between classical and quantum approaches.
Sara Bonella (CECAM HQ) - Organiser
Philippe Caroff (EPFL) - Organiser
Andrea Cavalli (CECAM HQ) - Organiser
Vincenzo Savona (QSE, EPFL) - Organiser