HyNNet NISQ · Hybrid quantum-classical neural networks for the characterization of noisy intermediate scale quantum computers
Horizon Europe — Marie Skłodowska-Curie Actions
- Duration
- 2023-09-01 → 2025-11-30
- EU contribution
- €218,667
- Participants
- 2
- Scheme
- HORIZON-TMA-MSCA-PF-GF
Lines connect the coordinator with its partners.
Results in brief
Hybrid quantum-classical neural networks for the characterization of noisy intermediate scale quantum computers
Existing noisy intermediate-scale quantum computers can perform computations that are challenging for classical computers. However, quantum computing hardware and quantum algorithms need to be further developed to enable the exploitation of quantum computers in areas such as the simulation of many-body systems and machine learning. One of the major challenges in developing scalable quantum computers is characterizing the noisy quantum data produced by near-term quantum hardware. With increasing system size, standard characterization techniques using direct measurements and classical post-processing become prohibitively demanding due to large measurement counts and computational efforts. Directly processing quantum data on quantum processors can substantially reduce measurement costs. Quantum neural networks based on parametrized quantum circuits, measurements and feed-forward can process large amounts of quantum data, to detect non-local quantum correlations with reduced measurement and computational efforts. Characterizing non-local correlations is crucial in condensed matter physics for classifying quantum phases of matter and understanding new strongly correlated materials such as topological quantum matter. However, such quantum neural networks often require deep quantum circuits that cannot be implemented on existing devices. The objective of HyNNet NISQ was to overcome these challenges by integrating short-depth parametrized quantum circuits with classical artificial neural networks to design hybrid quantum-classical neural networks. Thanks to their short-depth quantum circuits, these hybrid neural networks can be readily implemented on existing quantum computers and thus open the way for the efficient characterization of quantum states. Employing machine learning techniques, we trained the hybrid neural networks to identify underlying characteristics of quantum phases in strongly correlated materials such as topological quantum matter, facilitating their simulation on quantum computers.
Data: CORDIS, © European Union
Project objective
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).
Original text from CORDIS.
Participants
- FRIEDRICH-ALEXANDER-UNIVERSITAET ERLANGEN-NUERNBERG · ErlangenCoordinatorGermany
- UNIVERSITAT BASEL · BaselSwitzerland
Links
- View on CORDIS
- DOI: 10.3030/101108476
- https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e509312eeb&appId=PPGMS
- https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e52615a632&appId=PPGMS
Data: CORDIS, © European Union