Tim Ferriss & Eric Schmidt
Quantum AI uses quantum computing to compute machine learning algorithms. Quantum AI is able to produce results not possible with traditional computers due to the computational advantages provided by quantum computing.
Quantum data is quantified data that can be viewed as qubits. The superposition and the entanglement of quantum data make it valuable. Observing and storing this data can be challenging. Quantum data is also noisy. It is important to use machine learning at the stage of analyzing, interpreting and understanding these data.
Quantum algorithms are a series of steps leading to a solution. To execute these steps, you must use the specific instructions that are designed for the device.
Quantum computing uses different instructions that have a different execution concept than classical computing. Quantum algorithms use quantum effects such as superposition and entanglement in order to find the solution more quickly.
Why is it important to you?
AI has advanced rapidly over the last decade but has yet to overcome its technological limitations. Quantum computing’s unique properties can help to eliminate obstacles in the way of AGI (Artificial General Intelligence).
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