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post #11 of 13
Quote:
Originally Posted by DuckieHo View Post

Apples-to-oranges.....

Biological and neural network systems work non-linearly... they aren't one or zero in sequence but many variables at once. They can attempt to solve problems with mutltiple possible solutions simutaneously.

The are especially good at nondeterministic polynomial time (NP) problems.

A way to think about them is something half-way between a quantum computing and current "modified Harvard architecture" computers.

This is true, but nature doesnt always fall into the best possible optimized states. And they cant backtrack either. So while they may natrually be able to solve non-np complete problems to an efficient state quickly. They usually dont do it to the best possible state.
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post #12 of 13
Nature uses spontaneous organisation and reiterative methods to solve complex problems. Fungi are quite good at finding the shortest and most efficient neural networks.
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post #13 of 13
Thread Starter 
Quote:
Originally Posted by serp777 View Post

For NP problems, use a quantum computer. For all other processes, use a regular transistor system. Quantum computers are orders of magnitude better than biological computers because they don't explore many variables at once, they explore all solutions at once. In other words, regular silicon or graphene can do traditional problems better, and quantum computers can do computationally hard problems better, so there is no reason to use a biological computer.

In terms of big O, biological computers would be O(N) while quantum computers would be O(1), where N in this case represents the number of superpositions a biological computer can compute in a unit of time. I also imagine that a biological computer would run into the same issues as a quantum computer--that turing systems are extremely challenging to operate efficiently on a quantum computer. Finally, a 144 GHz graphene chip might still be able to compete with a biological computer on NP problems depending on how fast they can make a biological computer run.

Quantum computers are the best computers for those type of problems. However, is there any guarantee that a quantum computer will be cheap, small, and stable enough?

There's no reason NOT to research biological computers. Like I said... their perform better than traditional computers at certain problems.

Quote:
Originally Posted by Twinnuke View Post

This is true, but nature doesnt always fall into the best possible optimized states. And they cant backtrack either. So while they may natrually be able to solve non-np complete problems to an efficient state quickly. They usually dont do it to the best possible state.
With some types of real-world problems... close is often good enough. wink.gif
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