Due to several physical limitations in the realisation of quantum hardware, today’s quantum computers are qualified as Noisy Intermediate-Scale Quantum (NISQ) hardware. NISQ hardware is defined by a small number of qubits (50 to a few hundred) and noisy operations. Moreover, current realisations of quantum chips do not have the ideal all-to-all connectivity between qubits but rather at most a nearest-neighbour connectivity. All these hardware restrictions add supplementary low-level requirements. In order to leverage the computational power hardware, efficient quantum circuit simulation, compilation and verification are required and optimized for quantum technology. In this work, we develop computer-aided design methods for quantum computing.
Quantum computing is herald as the next revolution in scientific computing. This research focuses on applying quantum algorithms to scientific computing problems in order to circumvent the bottlenecks imposed by classical computing. The first target will be to solve linear systems that arise from scientific computing problems on a NISQ quantum computer and analyze the potential gain with respect to classical architectures and the bottlenecks that might appear with the quantum approach. The tools and algorithms developed will then be used to solve more complex problems such as partial differential equations or quantum machine learning.