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市场调查报告书
商品编码
1368215
汽车产业量子运算用途策略概述Strategic Overview of Quantum Computing Applications in the Automotive Industry |
新产品开发计划专注于製程最佳化和先进材料研究
该分析重点关注汽车行业中的量子计算创新,以及将该技术整合到供应链、材料研究、车辆设计、车辆测试、组装、製造、零售、售后和车辆行驶中的重要性。我猜。与量子运算处理器整合的模拟可以帮助汽车更快地分析多个预生产场景,并具有更高的准确性和更快的交付时间,从而获得竞争优势。量子运算还可以帮助在量子层级上模拟电池材料的复杂分子特性以及反应和行为,使OEM能够使用新型永续材料设计低成本电池。
该技术还可以帮助最佳化交通管理和车辆路线。 BMW、福斯、丰田、现代、戴姆勒和福特正在针对某些使用案例试点量子计算。儘管具有较小变数集的概念验证(POC)看起来很有希望,但未来的计划将需要扩大基础设施、量子位元品质和复杂参数集的使用。在投资量子研究之前,确定正确的使用案例非常重要。 OEM应该与专业服务专家合作,帮助他们发现问题、开发概念验证,并最终整合到日常生产流程中。然而,高昂的投资成本和现有的合适技术(用于数位化汽车价值链)目前阻碍了OEM实施动态。透过确定正确的用例并采用量子和经典计算的混合模型,OEM可以两全其美。该分析整体情况了量子计算以及当前阻碍汽车行业量子发展势头的挑战。我们分析OEM合作伙伴关係和关键使用案例。
New Product Development Initiatives to Focus on Process Optimization and Advanced Materials Research
This analytics highlights quantum computing innovation in the automotive industry and the significance of integrating this technology in supply chain, materials research, vehicle design, vehicle testing, assembly, manufacturing, retail, after-sales, and vehicle-in-motion. Simulations integrated with quantum computing processors analyze multiple pre-production scenarios substantially faster; they are more accurate and have a shorter turnaround time, helping automakers stay ahead of the competition. Quantum computing also helps simulate complex molecular properties and battery material reactions and behaviors at the quantum level and can enable OEMs to design low-cost batteries with new, sustainable materials.
The technology can help optimize traffic management and vehicle routing. BMW, VW, Toyota, Hyundai, Daimler, and Ford are piloting (in partnership) quantum computing for select use cases. Though the proof-of-concept (Poc) for a smaller set of variables looked promising, the future plan will involve scaling up the infrastructure, qubits quality, and using complex sets of parameters. Identifying the right use case is critical before investing in quantum research. OEMs should partner with professional services experts that can help with problem identification through proof-of-concept development and eventually integration into day-to-day production processes. However, huge investment costs and existing pertinent technologies (to digitize the automotive value chain) are currently hindering quantum adoption among OEMs. Right use case identification, coupled with a hybrid quantum-classical computing model, will enable OEMs to achieve the best of both worlds. This analytics presents the overall scope of quantum computing and the current challenges hindering quantum momentum in the automotive industry. It analyzes OEM partnerships and key use cases.