IBM Research Award - 2025 - Julien Gacon

For his pioneering work on resource-efficient quantum algorithms, advancing our ability to simulate complex many-body systems and broadening the horizons of quantum computation.

Quantum computing presents a new computational paradigm, that holds the potential to fundamentally advance fields such as physics, chemistry, and materials science. However, the noise and limited scale of today's quantum computers render many prominent quantum algorithms impractical for real-world applications.

This doctoral thesis works towards bridging the gap between algorithms and hardware capabilities, by developing two techniques to reduce quantum computational resource requirements. The first approach is based on stochastic approximations of costly quantities, such as the quantum geometric tensor. The second method avoids its explicit calculation by proposing a novel, optimization-based formulation of quantum time evolution.

The proposed algorithms are benchmarked on representative spin models, both in numerical simulations and hardware experiments. In combination with error mitigation, the latter is scaled up to 27 qubits--a regime challenging to reach using variational algorithms without our techniques.

While primarily focused on the simulation of quantum systems, the developed subroutines are broadly applicable to domains such as classical optimization and machine learning. These contributions represent a step toward making quantum algorithms more practical and scalable on today's quantum hardware.