Batch quantization is a process where multiple data points or transactions are grouped together and then processed or converted into a more compact, often lower-precision format. This technique reduces the overall data size and computational requirements for storage and transmission. It can involve mapping a range of input values to a smaller set of output values. This method improves efficiency in various digital systems.
Context
In the context of digital assets and blockchain technology, batch quantization is relevant for optimizing data storage and processing within zero-knowledge proofs (ZKPs) or other cryptographic protocols. By quantizing data, the size of proofs can be significantly reduced, leading to lower on-chain transaction costs and faster verification times. This advancement is a key area of research for improving the scalability and privacy of decentralized applications.
Batch Zero-Knowledge Proofs are integrated into BFT consensus, cutting communication complexity to O(n) and enabling scalable, private decentralized AI.
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