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Resources for Flexible Quantum Information Processing (REFLEQIP)
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Over the past two decades, quantum technologies for computation, communication and sensing have progressed from theoretical proposals to experimental reality. Although these technologies are widely expected to outperform their classical counterparts in the future, present day state-of-the-art setups operating in the quantum domain cannot compete with currently available classical devices such as smart phones or laptop computers. This is, in part, due to the difficulties in controlling and precisely manipulating the required quantum systems such as trapped atoms or quanta of light (so-called photons) in the presence of disturbance from their environment. Consequently, modern prototypes for quantum computers operate with small numbers of information carrying systems such as so-called “qubits”, the quantum equivalents of classical bits, yet require complex control mechanism, energy for cooling and other resources for their operation.
In this research project entitled we have therefore followed a dual approach: On the one hand, we have investigated how the available scarce quantum resources can be used most efficiently for tasks such as running computations on a quantum computer or distributing quantum systems for quantum communication. Among the key results of this approach were the development of a machine learning algorithm that can protect quantum computations by dynamically adapting its error correction routine to changing environmental conditions [Poulsen Nautrup et al., Quantum 3, 215 (2019)], as well as the design of procedures for improving the distribution of quantum states among multiple users by tapping into information about noise that is encoded in otherwise discarded systems [Morelli et al., Quantum 6, 722 (2022) ].
On the other hand, the project has aimed to provide fundamental theoretical insight into the resources needed to establish these quantum resources to begin with. In this second approach we have addressed the question of how time, energy, and precise control over the internal structure and interaction of complex quantum systems allow us to perform measurements [Guryanova, Friis, Huber, Quantum 4, 222 (2020)], cool quantum systems to the required temperatures [Taranto et al., PRX Quantum 4, 010332 (2023)], store [Bakhshinezhad et al., arXiv:2303.16676] and estimate [Debarba et al., New J. Phys. 21, 113002 (2019)] useable energy, and create correlations [Bakhshinezhad et al., J. Phys. A Math. Theor. 52, 465303 (2019)].
The project has thus connected various aspects of my research in quantum thermodynamics, quantum metrology, quantum computation and entanglement detection.
In this research project entitled we have therefore followed a dual approach: On the one hand, we have investigated how the available scarce quantum resources can be used most efficiently for tasks such as running computations on a quantum computer or distributing quantum systems for quantum communication. Among the key results of this approach were the development of a machine learning algorithm that can protect quantum computations by dynamically adapting its error correction routine to changing environmental conditions [Poulsen Nautrup et al., Quantum 3, 215 (2019)], as well as the design of procedures for improving the distribution of quantum states among multiple users by tapping into information about noise that is encoded in otherwise discarded systems [Morelli et al., Quantum 6, 722 (2022) ].
On the other hand, the project has aimed to provide fundamental theoretical insight into the resources needed to establish these quantum resources to begin with. In this second approach we have addressed the question of how time, energy, and precise control over the internal structure and interaction of complex quantum systems allow us to perform measurements [Guryanova, Friis, Huber, Quantum 4, 222 (2020)], cool quantum systems to the required temperatures [Taranto et al., PRX Quantum 4, 010332 (2023)], store [Bakhshinezhad et al., arXiv:2303.16676] and estimate [Debarba et al., New J. Phys. 21, 113002 (2019)] useable energy, and create correlations [Bakhshinezhad et al., J. Phys. A Math. Theor. 52, 465303 (2019)].
The project has thus connected various aspects of my research in quantum thermodynamics, quantum metrology, quantum computation and entanglement detection.
This project ran from August 2018 until January 2023.
Team
- Nicolai Friis (PI/Project Lead)
- Yelena Guryanova (Postdoc, on the project from November 2018 to March 2019, now YIRG group leader at IQOQI Vienna)
- Tiago Debarba (Visiting Fellow, on the project from January to July 2019, Associate Professor at Universidade Tecnológica Federal do Paraná, Brazil)
- Simon Morelli (PhD student from 2019 to 2022, now postdoc at BCAM Bilbao, Spain)
- Pharnam Bakhshinezhad (Visiting PhD student, on the project from March to September 2019, Sharif University of Technology, Teheran)
- Alexandra Bergmayr (PhD student on the project from July to September 2023, TU Wien)
