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QUANTIFI - QUANTum computIng For heterogeneous catalytIc materials solutions.

QUANTIFI - QUANTum computIng For heterogeneous catalytIc materials solutions.
QUANTIFI - 量子计算 用于异质催化材料解决方案。
批准号:
133986
负责人:
金额:
$43.52万
依托单位:
依托单位国家:
英国
项目类别:
CR&D Bilateral
财政年份:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --

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中文摘要
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英文摘要
QUANTIFI aims to develop a world-leading Quantum Computing Dynamical Mean Field Theory (DMFT) solution for strongly correlated catalytic materials. DMFT is needed to properly describe a large number of important transition metal oxides used as catalytic materials for emissions reductions as well as oxides for batteries and other applications. On conventional computers DMFT is restricted to very small systems due to the prohibitive computational cost. Quantum computers are expected to lead to exponentially large speedups, making currently unfeasible calculations feasible. We will bring the resulting quantum software product to the market and integrate it in cloud services. This will enable the UK to maintain its world leading position in the quantum materials software market with the advent of quantum computers (QCs).This will be achieved through the development of a framework based on quantum algorithms that interfaces with a QC to solve the electronic structure problem using DMFT. The vision directly relates to the overall need of the chemicals/materials sector for accurate, rapid modelling solutions, overcoming existing limitations that prevent accurate modelling of materials, reducing the need for lengthy, expensive lab trials. Application of the solution to the materials sector will enable faster discovery of new materials, new economies and new (patentable) discoveries.The technology will be innovative in a number of clear ways, in particular this will demonstrate the feasibility of using quantum computing to accelerate materials modelling and discovery, including:\* Use of a Variational Quantum Eigensolver (VQE) for ground and excited states within an exact diagonalization (ED) DMFT approach.\* Quantum Machine Learning algorithms for noise reduction and error mitigation.\* Use of quantum DMFT solvers on currently available and near-term ('NISQ') QCs for real materials of industrial relevance. These are expected to be able to solve systems, where state-of-the-art classical methods fail due to the exponential growth of computational times.QUANTIFI is innovative in that we use a Variational Quantum Eigensolver (VQE) for ground and excited states within an exact diagonalization (ED) DMFT approach to demonstrate the feasibility of quantum DMFT solvers on currently available and near-term ('NISQ') QCs for industrially relevant materials. The work is supported by NPL and KCL, world-leading experts in DMFT.QUANTIFI, therefore, has potential high impact in catalysis and hence a large product relevance for many of the UKs chemistry manufacturers, materials designers, and pharmaceutical companies.By achieving this, it is estimated that the consortium and wider supply chain will achieve significant benefits.
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Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Simulation and certification of the ground state of many-body systems on quantum simulators
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Abolfazl Bayat
  • 依托单位:
Mapping Quantum Chromodynamics by Nuclear Collisions at High and Moderate Energies
  • 批准号:
    11875153
  • 项目类别:
    面上项目
  • 资助金额:
    60.0万元
  • 批准年份:
    2018
  • 负责人:
    MARCO RUGGIERI
  • 依托单位: