Dartmouth Events

Abstract: Recent advancements in quantum information have led to significant progress in quantum technologies, bringing us to a new Information Age. In the near future, we expect to have access to Noisy Intermediate-Scale Quantum (NISQ) computers, which contain between fifty and a few hundred qubits and operate with limited quantum circuit depths. Variational quantum algorithms (VQAs), which rely on a quantum-classical optimization loop, are currently considered one of the most promising approaches for utilizing these devices. However, important theoretical and practical challenges remain, such as understanding the trainability of VQAs and exploring their potential interdisciplinary applications. In this colloquium, I will first discuss how measurement locality and circuit depth influence the trainability of VQAs, providing physical insights through Hamiltonian-based algorithms. Then, I will present applications of VQAs in the development of optimal quantum sensors for single-parameter estimation. These studies are expected to open new research directions, including the realization of precise multi-parameter estimation using near-term quantum devices, with potential applications in fields such as chemistry, materials science, and biology.

Hosted by Professor Lorenza Viola