Demonstration of hardware efficient photonic variational quantum algorithm

Quantum computing has changed the paradigm of computer science, where quantum technologies have promised to outperform classical ones. Such an advantage was only demonstrated for tasks with no application or out of reach for the state-of-the-art quantum technologies. In this context, a promising strategy to find practical use of quantum computers exploits hybrid models, where a quantum device estimates a hard-to-compute quantity, while a classical optimizer trains the parameters. In this work, we demonstrate that single photons and linear optical networks are sufficient for implementing variational quantum algorithms, when the problem specification (ansatz) is tailored to this specific platform. We show this by a proof-of-principle demonstration to tackle an instance of a factorization task, whose solution is encoded in the ground state of a suitable Hamiltonian. This work, which combines Variational Quantum Algorithms with hardware efficient ansätze for linear optics, showcases a promising pathway toward practical applications for photonic quantum platforms.