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
• 金额: $ 43.52万
• 依托单位: RAHKO LIMITED
• 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
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=133986
• 关键词: QUANTIFI; QUANTum; computIng; heterogeneous; catalytIc

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.

QUANTIFI旨在为强相关催化材料开发世界领先的量子计算动态平均场理论（DMFT）解决方案。需要DMFT来正确描述大量用作减排催化材料的重要过渡金属氧化物以及用于电池和其他应用的氧化物。在传统计算机上，由于计算成本过高，DMFT被限制在非常小的系统中。量子计算机有望带来指数级的加速，使目前不可行的计算成为可能。我们将由此产生的量子软件产品推向市场，并将其整合到云服务中。随着量子计算机（qc）的出现，这将使英国在量子材料软件市场保持世界领先地位。这将通过开发基于量子算法的框架来实现，该框架与QC接口，使用DMFT解决电子结构问题。这一愿景直接关系到化学品/材料行业对准确、快速建模解决方案的总体需求，克服现有的阻碍材料准确建模的限制，减少对漫长、昂贵的实验室试验的需求。将该解决方案应用于材料行业将能够更快地发现新材料、新经济和新的（可申请专利的）发现。该技术将以许多明确的方式进行创新，特别是这将证明使用量子计算加速材料建模和发现的可行性，包括：*在精确对角化（ED） DMFT方法中使用变分量子特征解算器（VQE）来处理基态和激发态。*量子机器学习算法用于降噪和减少错误。\*使用量子DMFT解算器在当前可用和近期（“NISQ”）qc上用于工业相关的实际材料。这些有望能够解决系统，其中最先进的经典方法由于计算时间的指数增长而失败。QUANTIFI的创新之处在于，我们在精确对角化（ED） DMFT方法中使用变分量子特征解算器（VQE）来处理基态和激发态，以证明量子DMFT解算器在工业相关材料的当前可用和近期（“NISQ”）qc上的可行性。这项工作得到了世界领先的DMFT专家NPL和KCL的支持。因此，QUANTIFI在催化方面具有潜在的高影响力，因此对许多英国化学制造商，材料设计师和制药公司具有很大的产品相关性。通过实现这一点，估计财团和更广泛的供应链将获得显着的利益。