Speakers
Description
Sampling-based quantum advantage schemes currently serve as the leading experimental demonstrations of computational tasks deemed intractable for classical devices. However, their practical significance remains a subject of intense debate as these tasks are synthetically designed to maximize classical hardness rather than to solve meaningful real-world problems. In this contribution, we propose a generative learning application of two such schemes: Fermion Sampling and Gaussian Boson Sampling. We show that, despite the classical hardness of sampling from their output distributions, the generative models built on these schemes can nevertheless be trained efficiently by classical means. We numerically demonstrate their performance and scalability, training fermionic and bosonic models with up to 160 qubits and 805 photonic modes, respectively.
| Topic | Hybrid quantum-classical approaches for learning and optimization |
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| Additional Topic | Quantum variational circuits and their applications |