Summary
The objective of HyNNet NISQ is to develop tools based on hybrid quantum-classical algorithms for the characterization and measurement of quantum states prepared on near-term quantum computers.
Currently available quantum devices can perform computations that are challenging for classical computers. However, applications of quantum computers in science and economy require a further development of quantum hardware and algorithms. One of the major challenges is the measurement and characterization of quantum states produced as an output of quantum algorithms. Standard diagnostic techniques have become limited due to the quickly increasing system 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 of quantum algorithms and next-generation quantum hardware.
I (Dr. Petr Zapletal) will carry out the proposed research with the input and advice from Prof. Christoph Bruder (University of Basel) and Prof. Michael J. Hartmann (FAU Erlangen-Nuremberg).
Currently available quantum devices can perform computations that are challenging for classical computers. However, applications of quantum computers in science and economy require a further development of quantum hardware and algorithms. One of the major challenges is the measurement and characterization of quantum states produced as an output of quantum algorithms. Standard diagnostic techniques have become limited due to the quickly increasing system 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 of quantum algorithms and next-generation quantum hardware.
I (Dr. Petr Zapletal) will carry out the proposed research with the input and advice from Prof. Christoph Bruder (University of Basel) and Prof. Michael J. Hartmann (FAU Erlangen-Nuremberg).
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101108476 |
| Start date: | 01-09-2023 |
| End date: | 30-11-2025 |
| Total budget - Public funding: | - 218 666,00 Euro |
Cordis data
Original description
The objective of HyNNet NISQ is to develop tools based on hybrid quantum-classical algorithms for the characterization and measurement of quantum states prepared on near-term quantum computers.Currently available quantum devices can perform computations that are challenging for classical computers. However, applications of quantum computers in science and economy require a further development of quantum hardware and algorithms. One of the major challenges is the measurement and characterization of quantum states produced as an output of quantum algorithms. Standard diagnostic techniques have become limited due to the quickly increasing system 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 of quantum algorithms and next-generation quantum hardware.
I (Dr. Petr Zapletal) will carry out the proposed research with the input and advice from Prof. Christoph Bruder (University of Basel) and Prof. Michael J. Hartmann (FAU Erlangen-Nuremberg).
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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