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Hybrid quantum-classical neural networks for the characterization of noisy intermediate scale quantum computers

CORDIS · observation · Publication date unknown

stem size and complexity of quantum devices. Here I will integrate adaptive quantum algorithms with classical artificial neutral networks to design hybrid quantum-classical neural networks. Employing machine learning techniques, I will train the hybrid neural networks to identify underlying characteristics of quantum states. I will develop characterization and measurement tools required for the simulation of condensed matter physics and quantum chemistry on near-term quantum computers. First, I will investigate how to design and train hybrid neural networks to recognize quantum phases of matter, focusing on strongly correlated systems and topological order. Second, I will study how to exploit hybrid neural networks to reconstruct the full quantum state describing all properties of a quantum system. I will use this technique to efficiently measure quantities required for condensed matter physics and quantum chemistry simulations. The hybrid neural networks developed here can be readily realized on near-term quantum computers. Therefore, they will provide key tools for the development

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recordType

award

status

SIGNED

region

EU

value

218666.88

unit

EUR