Quantum-powered generative artificial intelligence architecture for performing synthetic data augmentation with advanced quantum circuits, has quantum gate for applying adaptive parameterized rotations on quits, and hybrid quantum-classical optimization circuit for employing quantum processors
	SAI S T P H V; REDDY V S K; SUJITH A V L N; CHARI D V
	2025-03-29
专利权人	UNIV REDDY MALLA (UYRE-Non-standard)
申请日期	2025-03-29
专利号	IN202541032192-A
成果简介	NOVELTY - The architecture has a quantum gate for applying adaptive parameterized rotations on qubits. An optimized controlled phase shift and entanglement based quantum Fourier transform circuit facilitates exponential speedup in a signal processing. Variational quantum algorithm (VQA) circuit applies layered parameterized gates structure as a unit of dynamically optimizing quantum state transformations with an iterative feedback mechanism for improving error resilience and scalability in quantum chemistry simulations, portfolio optimization and combinatorial problem solving. A hybrid quantum-classical optimization circuit employs quantum processors for state space exploration and function evaluation. USE - Quantum-powered generative artificial intelligence (AI) architecture for performing synthetic data augmentation with advanced quantum circuits. ADVANTAGE - The architecture improves ability to recognize patterns and generate data and content, and performance of AI generated outputs. DESCRIPTION OF DRAWING(S) - The drawing shows a circuit diagram of a parameterized quantum circuit.
IPC 分类号	G06N-010/00 ; G06N-010/60 ; G06N-003/045 ; G06N-003/047 ; G06N-003/08
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/13349
专题	中国科学院新疆生态与地理研究所
作者单位	UNIV REDDY MALLA (UYRE-Non-standard)
推荐引用方式 GB/T 7714	SAI S T P H V,REDDY V S K,SUJITH A V L N,et al. Quantum-powered generative artificial intelligence architecture for performing synthetic data augmentation with advanced quantum circuits, has quantum gate for applying adaptive parameterized rotations on quits, and hybrid quantum-classical optimization circuit for employing quantum processors. IN202541032192-A[P]. 2025.

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