Quantum Computing Tools and APIs
Several quantum machine learning algorithms have already been implemented as testbeds or early adopter enterprise platforms. We are at the stage where quantum implementation is benchmarked against "traditional" mainstream ML algorithms. Today we can characterize quantum computing platforms as fairly reliable small-scale quantum devices.
The technical complexity and energy expenditure required for running present-day adiabatic quantum computers are substantial. Moreover, replacing existing computing systems with quantum might not be easy due to the environmental impact. However, assuming a rapid pace of innovation, advanced algorithms, i.e., machine learning on reliable quantum computers, will soon become mainstream. Future benefits from quantum speedup probably outweigh the problems, but new severe risks regarding reliability, trustworthiness, safety, and security will also surface.
Today high-level application programming interfaces for quantum computing are increasingly available (QISKIT, CIRQ, etc.). #algorithms #machinelearning #complexity #future #quantumcomputing #quantumcomputer #api
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