SubsidieMeestersquantum-enhanced shadows: scalable quantum-to-classical converters
This project aims to enhance quantum experiments by developing quantum-to-classical converters, enabling efficient data processing and learning through a unified framework that addresses scalability issues.
Projectdetails
Introduction
Large-scale quantum experiments do not work in isolation. Substantial classical computing power is required to control the experiment and process the results. This necessarily creates information-transmission bottlenecks at the interface between quantum and classical realms.
Scalability Issues
These bottlenecks create scalability issues that prevent us from using existing architectures to the best of their capabilities and may even impair our ability to further scale up system sizes.
Project Overview
In this project, we adopt a unifying framework that takes into account all computing resources (quantum and classical). We develop quantum-to-classical converters to overcome information-transmission bottlenecks.
Shadows
Dubbed shadows, they leverage randomization, as well as quantum-enhanced readout strategies to obtain a succinct classical description of an underlying quantum system that can then be used to efficiently predict many features at once. The shadow paradigm is compatible with near-term quantum hardware and utilizes genuine quantum effects that do not have a classical counterpart.
Synergies with Machine Learning
Building on these ideas, we also establish rigorous synergies between quantum experiments and classical machine learning. Shadow learning protocols use shadows to succinctly represent training data obtained from actual quantum experiments. A classical training stage then enables data-driven learning of genuine quantum phenomena.
Reliable Execution
Finally, we develop new tools to ensure reliable execution on current quantum hardware, thus bridging the gap between theory and experiment.
Interdisciplinary Skill Set
My interdisciplinary skill set combines methods from modern computer science with quantum information and has already led to numerous high-impact contributions (e.g. 1 Nature Physics with more than 350 citations and 2 Science publications).
Future Directions
These insights form the basis for this larger project, where we lay the foundation for scalable and practical quantum data processing and learning that can keep up and grow with future improvements in quantum technology.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.500.000 |
| Totale projectbegroting | € 1.500.000 |
Tijdlijn
| Startdatum | 1-7-2024 |
| Einddatum | 30-6-2029 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITAT LINZpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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|---|---|---|---|---|
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Delineating the boundary between the computational power of quantum and classical devices
This project aims to assess and leverage the computational power of quantum devices, identifying their advantages over classical supercomputers through interdisciplinary methods in quantum information and machine learning.
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This project aims to identify quantum many-body problems with significant advantages over classical methods and develop new quantum machine learning techniques to solve them effectively.
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Sublinear Quantum Computation
This project aims to develop innovative sublinear quantum algorithms to address open problems in quantum computation, enhancing efficiency and linking quantum computing with advanced mathematics.
Algorithms, Security and Complexity for Quantum Computers
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Vergelijkbare projecten uit andere regelingen
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Quantum Generative Adversarial Networks with phoTonic Integrated Circuits (QuGANTIC)QuGANTIC aims to develop a scalable quantum computer using quDits on a photonic integrated chip to enhance data processing for critical global challenges, outperforming classical systems. | EIC Pathfinder | € 3.194.262 | 2023 | Details |
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SCALABLE MULTI-CHIP QUANTUM ARCHITECTURES ENABLED BY CRYOGENIC WIRELESS / QUANTUM -COHERENT NETWORK-IN PACKAGE
The QUADRATURE project aims to develop scalable quantum computing architectures with distributed quantum cores and integrated wireless links to enhance performance and support diverse quantum algorithms.
Scalable Hardware for Large-Scale Quantum Computing
Developing a scalable, fault-tolerant quantum computer using advanced cryo-CMOS technology to enhance precision and efficiency in processing complex data across various fields.
Quantum Generative Adversarial Networks with phoTonic Integrated Circuits (QuGANTIC)
QuGANTIC aims to develop a scalable quantum computer using quDits on a photonic integrated chip to enhance data processing for critical global challenges, outperforming classical systems.
QUantum reservoir cOmputing based on eNgineered DEfect NetworkS in trAnsition meTal dichalcogEnides
This project aims to develop a proof-of-concept for Quantum Reservoir Computing using Quantum Materials defects to create advanced computing devices and enhance Quantum Technologies.