Quantum computing has some potential to reduce energy consumption in certain computational tasks, but it may not be the overall best course for reducing energy consumption of neural networks.

While it has the potential to solve certain computational problems more efficiently than classical computers, it is not clear that quantum computing would help close the performance gap between the energy efficiency of neural networks and the human brain. In fact, there are many factors that contribute to the energy consumption of neural networks, including the size and complexity of the network, the precision of the computations, and the data processing methods. Therefore, reducing energy consumption in AI systems will likely require a combination of approaches, including algorithmic improvements, hardware optimization, and energy-efficient computing techniques.
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