Neural-network-enabled hybrid classical-quantum computing

Using quantum and classical computational techniques in a unified framework is key to solving problems that cannot be easily addressed by quantum computations alone.

A prototypical task where this manifests is the task of preparing approximate ground states of quantum many-body systems. In this seminar, I will discuss several strategies to efficiently couple classical methods based on neural quantum states [1] with the power of parameterized quantum circuits. I will describe hybrid quantum-classical variational ansatzes that forge entanglement to lower quantum resources overhead [2]. I will then describe a new hybrid classical-quantum ansatz to treat multi-component systems [3] efficiently.

[1] Carleo and Troyer, Science 355, 602 (2017)

[2] Huembeli, Carleo, and Mezzacapo, arXiv:2205.00933 (2022)

[3] Barison, Vicentini, and Carleo, in preparation