PUBLISHER: Grand View Research | PRODUCT CODE: 1888839
PUBLISHER: Grand View Research | PRODUCT CODE: 1888839
The global AI-optimization for quantum computing market size was valued at USD 112.3 million in 2024 and is projected to reach USD 541.4 million by 2033, growing at a CAGR of 19.3% from 2025 to 2033. The rapid advancement of quantum hardware is creating a strong need for AI-driven optimization to improve system accuracy and efficiency.
As quantum processors scale to higher qubit counts, error rates, qubit decoherence, and noise challenges intensify, requiring intelligent algorithms to stabilize operations. Quantum algorithms are becoming more complex as enterprises explore use cases in chemistry, materials science, finance, and logistics. These workloads demand precise optimization of circuits, resource allocation, and noise-aware execution strategies. AI helps automate circuit optimization by shortening gate depth, lowering noise exposure, and selecting the most efficient qubit pathways. Organizations recognize that algorithm optimization is essential to approach quantum advantage in the near term. AI-driven optimization platforms provide automated tools that would otherwise require extensive human expertise. This growing complexity of workloads directly fuels demand for AI-enabled optimization solutions.
Additionally, the rise of trapped ion computing, where classical AI systems work alongside quantum processors. These trapped ions workflows rely on AI to orchestrate task allocation, optimize circuit execution, and manage data transfer across environments. AI models enable adaptive learning loops where classical systems train and refine quantum models iteratively. This approach significantly increases quantum algorithm performance and accelerates convergence times for optimization problems. Enterprises adopting trapped ions architectures rely on AI tools to streamline interactions between classical GPUs and quantum devices. As trapped ions computing becomes the standard for near-term quantum use cases, demand for AI-driven orchestration tools accelerates.
Industries such as pharmaceuticals, automotive, Machine Learning Model Optimization, logistics, and energy are rapidly exploring quantum applications that require optimization support. These sectors face complex computational problems-such as molecular modeling, portfolio optimization, and route planning-that benefit from AI-enhanced quantum workflows. AI improves solution accuracy by fine-tuning quantum circuits for domain-specific challenges. Vendors are building an industry focused on optimization models that simplify integration into existing enterprise applications. As quantum cloud platforms expand their service offerings, AI-assisted optimization tools become more accessible to commercial users. This rising adoption aligns with growing enterprise awareness of quantum computing's long-term value.
Global AI-optimization For Quantum Computing Market Report Segmentation
This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2021 to 2033. For this study, Grand View Research has segmented the global AI-optimization for quantum computing market report based on component, technology, application, end use, and region.