The Hidden Bottleneck in Quantum Machine Learning: Getting Data into a Quantum Computer
中文摘要
量子机器学习面临瓶颈：如何高效地将经典数据输入量子计算机是实现其强大算力的关键挑战。
English Summary
The primary bottleneck in quantum machine learning is the efficient encoding of classical data into quantum systems, a prerequisite for leveraging their exponential computational potential.
原文节选
Quantum Machine Learning promises access to exponentially large representational spaces, but before any computation can happen, classical data must first be embedded into quantum systems. This article explores one of the most overlooked bottlenecks in QML: getting data into a quantum computer efficiently. The post The Hidden Bottleneck in Quantum Machine Learning: Getting Data into a Quantum Computer appeared first on Towards Data Science.