SubsidieMeestersBeyond-classical Machine learning and AI for Quantum Physics
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.
Projectdetails
Introduction
A primary challenge in quantum computing (QC) is finding its ideal application, i.e., an essential problem with the largest advantage of quantum over classical computing. To resolve it, I propose to focus on the notoriously complex area of quantum many-body systems.
Project Objectives
This project will characterize which quantum many-body problems, in various physics domains, allow for significant quantum advantages even over any future machine learning, data-driven methods. By exploiting my pioneering research in this area, I will also develop new quantum machine learning (QML) methods to solve them better than classically possible, using a two-stage approach.
Theoretical Foundations
In the first stage, we will develop the project's theoretical foundations. My recent works on quantum-over-classical learning advantages provide the starting points for the development of new mathematical machinery which facilitates the proving of quantum advantages in selected many-body settings.
In parallel, building on circuit-decomposition methods I recently developed, we will elucidate the role of quantum phenomena in QML in order to design new QML methods which can be better tuned to quantum many-body settings.
Application and Implementation
In the second stage, we will identify suitable concrete quantum many-body problems with substantial real-world interest, apply the newly designed high-performing quantum learners, and formally prove learning advantages using the developed theoretical machinery.
Expected Impact
The positive results of the project will resolve some of the main open problems in QML and will have a major impact on both QC theory and aspects of foundations and applications of QML.
Areas of Focus
In our search for the best application, we will consider many-body problems from diverse areas of physics:
- Condensed matter
- High-energy
- Quantum control
The project will therefore also establish new bridges between quantum many-body physics, machine learning, and quantum computing.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.995.289 |
| Totale projectbegroting | € 1.995.289 |
Tijdlijn
| Startdatum | 1-4-2024 |
| Einddatum | 31-3-2029 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITEIT LEIDENpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
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quantum-enhanced shadows: scalable quantum-to-classical convertersThis 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. | ERC Starting... | € 1.500.000 | 2024 | Details |
Predictive algorithms for simulating quantum materialsThis project aims to develop advanced predictive algorithms for quantum many-body systems by integrating field-theory methods with tensor techniques and machine learning to enhance understanding of quantum materials. | ERC Advanced... | € 3.499.299 | 2025 | Details |
Verifiying Noisy Quantum Devices at ScaleThis project aims to develop scalable, secure methods for characterizing and certifying quantum devices using interactive proofs, facilitating reliable quantum computation and communication. | ERC Consolid... | € 1.997.250 | 2023 | Details |
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.
Artificial agency and learning in quantum environments
This project aims to explore AI's role in quantum physics by developing interpretable learning agents for quantum research, enhancing scientific discovery and understanding of quantum theory.
quantum-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.
Predictive algorithms for simulating quantum materials
This project aims to develop advanced predictive algorithms for quantum many-body systems by integrating field-theory methods with tensor techniques and machine learning to enhance understanding of quantum materials.
Verifiying Noisy Quantum Devices at Scale
This project aims to develop scalable, secure methods for characterizing and certifying quantum devices using interactive proofs, facilitating reliable quantum computation and communication.
Vergelijkbare projecten uit andere regelingen
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
SCALABLE MULTI-CHIP QUANTUM ARCHITECTURES ENABLED BY CRYOGENIC WIRELESS / QUANTUM -COHERENT NETWORK-IN PACKAGEThe 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. | EIC Pathfinder | € 3.420.513 | 2023 | Details |
Accelerating Quantum Technology Development with Machine LearningThe project aims to commercialize QruiseOS for quantum technology by applying optimal control and machine learning, enhancing its maturity and enabling small labs to compete in the market. | EIC Transition | € 2.499.000 | 2023 | Details |
QUantum reservoir cOmputing based on eNgineered DEfect NetworkS in trAnsition meTal dichalcogEnidesThis 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. | EIC Pathfinder | € 2.675.838 | 2024 | Details |
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.
Accelerating Quantum Technology Development with Machine Learning
The project aims to commercialize QruiseOS for quantum technology by applying optimal control and machine learning, enhancing its maturity and enabling small labs to compete in the market.
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.