Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

We present a self-consistent field (SCF) approach within the adaptive derivative-assembled
problem-tailored ansatz variational quantum eigensolver (ADAPT-VQE) framework for …

We developed a general framework for hybrid quantum-classical computing of molecular
and periodic embedding approaches based on an orbital space separation of the fragment …

In order to characterize and benchmark computational hardware, software, and algorithms, it
is essential to have many problem instances on-hand. This is no less true for quantum …

Nuclear quantum effects such as zero-point energy and hydrogen tunneling play a central
role in many biological and chemical processes. The nuclear-electronic orbital (NEO) …

ADAPT-VQE stands out as a robust algorithm for constructing compact ans\" atze for
molecular simulation. It enables to significantly reduce the circuit depth with respect to other …

In ab initio electronic structure simulations, fermion-to-qubit mappings represent the initial
encoding step from the problem of fermions into a problem of qubits. This work introduces a …

Until fault-tolerance becomes implementable at scale, quantum computing will heavily rely
on noise mitigation techniques. While methods such as zero noise extrapolation with …

The rise of modern computer science enabled physical chemistry to make enormous
progresses in understanding and harnessing natural and artificial phenomena …

We develop an energy calculation algorithm leveraging quantum phase difference
estimation (QPDE) scheme and a tensor-network-based unitary compression method in the …