SubsidieMeestersQuantum Tensor Engine
The Quantum Tensor Engine (QTEngine) aims to provide a user-friendly software framework for efficient implementation of quantum algorithms in simulation, machine learning, and optimization.
Projectdetails
Introduction
Quantum computers harness fundamental aspects of quantum behavior to drive exponential increases in the speed with which certain computations can be performed. They have potentially a tremendous long-term impact in areas such as quantum many-body physics and material science, and further afield in machine learning.
Quantum Many-Body Problems
The quantum many-body problems studied by condensed matter physicists are perhaps the most likely to yield early demonstrations of this potential. However, current and near-term intermediate-scale quantum (NISQ) devices are limited in the number of operations that they can carry out before their performance is degraded by interactions with the environment.
Need for Efficient Algorithms
To take advantage of these platforms and to outperform classical computers, highly efficient and specialized quantum algorithms are required. The implementation and benchmarking of these basic algorithms on different quantum computing platforms is challenging and requires a detailed knowledge of the underlying physics.
Our Approach
Our approach is to produce a ready-to-use, highly innovative software package based upon quantum tensor networks.
Quantum Tensor Engine (QTEngine)
The Quantum Tensor Engine (QTEngine) will provide a unifying framework for both quantum and classical algorithms. The QTEngine will serve as an engine to drive fast and easy implementation of:
- Quantum simulation
- Quantum machine learning
- Optimization algorithms
Target User Base
The anticipated user base includes academic groups as well as commercial research and development groups.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 150.000 |
| Totale projectbegroting | € 150.000 |
Tijdlijn
| Startdatum | 1-7-2024 |
| Einddatum | 31-12-2025 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- TECHNISCHE UNIVERSITAET MUENCHENpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
FIrst NEar-TErm ApplicationS of QUAntum DevicesFINE-TEA-SQUAD aims to create a unifying framework for practical NISQ device applications by developing scalable protocols, certification tools, and a quantum network to enhance performance. | ERC Starting... | € 1.485.042 | 2022 | Details |
Probing Gauge Symmetries and Gauge-Matter Interactions using Tensor NetworksGaMaTeN aims to develop tensor network methods for studying quantum lattice systems with gauge symmetries, enhancing simulations and understanding of complex quantum phenomena. | ERC Consolid... | € 1.997.500 | 2024 | Details |
Delineating the boundary between the computational power of quantum and classical devicesThis 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. | ERC Advanced... | € 1.807.721 | 2024 | Details |
Artificial Scientific Discovery of advanced Quantum Hardware with high-performance SimulatorsARTDISQ aims to leverage AI and high-performance simulators to automate the design of advanced quantum experiments, enhancing discoveries in gravitational wave detection and imaging systems. | ERC Starting... | € 1.499.221 | 2025 | Details |
Quantum Synthetic Models for Entangled Matter Out of EquilibriumThis project aims to identify and characterize new phases of matter exclusive to NISQ devices by studying quantum circuits and cellular automata, enhancing understanding of many-body physics. | ERC Starting... | € 1.405.750 | 2024 | Details |
FIrst NEar-TErm ApplicationS of QUAntum Devices
FINE-TEA-SQUAD aims to create a unifying framework for practical NISQ device applications by developing scalable protocols, certification tools, and a quantum network to enhance performance.
Probing Gauge Symmetries and Gauge-Matter Interactions using Tensor Networks
GaMaTeN aims to develop tensor network methods for studying quantum lattice systems with gauge symmetries, enhancing simulations and understanding of complex quantum phenomena.
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 Scientific Discovery of advanced Quantum Hardware with high-performance Simulators
ARTDISQ aims to leverage AI and high-performance simulators to automate the design of advanced quantum experiments, enhancing discoveries in gravitational wave detection and imaging systems.
Quantum Synthetic Models for Entangled Matter Out of Equilibrium
This project aims to identify and characterize new phases of matter exclusive to NISQ devices by studying quantum circuits and cellular automata, enhancing understanding of many-body physics.
Vergelijkbare projecten uit andere regelingen
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
Quantum-enhanced Machine LearningEqual1 aims to finalize a scalable, sustainable quantum processor chip for AI applications, enhancing machine learning capabilities while reducing carbon footprint. | EIC Accelerator | € 2.500.000 | 2022 | Details |
Q-NETQphoX en Q*BIRD ontwikkelen een quantum connectie tussen QPU's via Transducers en een Central Hub om de toegankelijkheid van quantumtechnologie te vergroten en langeafstandverbindingen mogelijk te maken. | Mkb-innovati... | € 298.970 | 2022 | Details |
Automating quantum control with machine learningQuantrolox develops quantum autopilot software to automatically tune quantum computers, enhancing their uptime and accelerating development cycles for a robust quantum computing industry. | EIC Accelerator | € 2.495.500 | 2023 | Details |
A MILLION QUBIT QUANTUM COMPUTER - HIGHLY SCALABLE SOLID STATE QUANTUM COMPUTING PLATFORM WITH NATIVE OPTICAL NETWORKINGQuantum Transistors aims to develop a highly scalable quantum computing platform with millions of qubits, using efficient cooling methods for broader adoption and reduced costs. | EIC Accelerator | € 2.499.999 | 2024 | Details |
Quantum Next-Generation Trading Platform (QNGTP)Het QNGTP-project onderzoekt de haalbaarheid van kwantumcomputing voor portfolio-optimalisatie om een verbeterde aandelenhandel-applicatie te ontwikkelen en gefractionaliseerde handel te faciliteren. | Mkb-innovati... | € 20.000 | 2023 | Details |
Quantum-enhanced Machine Learning
Equal1 aims to finalize a scalable, sustainable quantum processor chip for AI applications, enhancing machine learning capabilities while reducing carbon footprint.
Q-NET
QphoX en Q*BIRD ontwikkelen een quantum connectie tussen QPU's via Transducers en een Central Hub om de toegankelijkheid van quantumtechnologie te vergroten en langeafstandverbindingen mogelijk te maken.
Automating quantum control with machine learning
Quantrolox develops quantum autopilot software to automatically tune quantum computers, enhancing their uptime and accelerating development cycles for a robust quantum computing industry.
A MILLION QUBIT QUANTUM COMPUTER - HIGHLY SCALABLE SOLID STATE QUANTUM COMPUTING PLATFORM WITH NATIVE OPTICAL NETWORKING
Quantum Transistors aims to develop a highly scalable quantum computing platform with millions of qubits, using efficient cooling methods for broader adoption and reduced costs.
Quantum Next-Generation Trading Platform (QNGTP)
Het QNGTP-project onderzoekt de haalbaarheid van kwantumcomputing voor portfolio-optimalisatie om een verbeterde aandelenhandel-applicatie te ontwikkelen en gefractionaliseerde handel te faciliteren.