Prediction Failed? Yes, Because Quantum Computing Works
The Present And Future Of Quantum Computing Expansion
+ One thing that we do know about the nature of quantum computing is that algorithms that normally have exponentially evolving complexity only have linear growth in complexity when viewed in the eyes of a quantum computer.
+ This is where the potential of quantum computers becomes interesting. As an example, the pharmaceutical industry relies a lot on the skills of designing new proteins. To do that, you need to simulate how they will fold in 3D space. As you try out possible outcomes, the complexity grows exponentially. Except, as I said, to a quantum computer, it doesn’t. By reducing the complexity from exponential to linear behavior, you may reduce years of work to a few minutes.
Other industries with similar potential for quantum computing shortcuts include everything about creating new materials, nano cover, paint, food industry, all kinds of chemicals and much more.
+ There is, however, one caveat. To do such simulations at a useful scale, we need both a quantum computer with a decent number of qubits, as its digital units are called, and a quantum computer that can run long enough to actually do the calculations. The qubit operates at the atomic/electronic scale, and noise from the surroundings is the most critical problem at all.
+ Last thing: I am sure someone out there will be thinking, “How will artificial intelligence benefit from quantum computing?” For the time being, AI most often equals advanced pattern recognition trained on (large) datasets. Quantum computing can boost the training process so much that what now takes days can be done in minutes. This allows for larger and more complex AI algorithms, and a natural consequence will be more sophisticated AI. It is, however, hard to see that a quantum computer will be the direct key to artificial general intelligence, a thinking machine.
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