Intel To Launch 10nm Cannonlake in 2017.

[jos]

My only qualm with this is your definition of quantum computing. It would actually make something like the traveling salesman (underlying problem of GPS quickest route, using least amount of movement for robotic arms on assembly lines, etc.) easy to solve quickly. Encryption/decryption as well will become jokes before a true quantum computer.

Only problem is with most classical app. it will be slow. for a robotic arms if it is adaptive algorithm learning from environment it will be fast. If the number of environment variables is to high and as i said in case of deep neural networks for learning. I am not so sure of GPS quickest root as i do not know the algorithm used. But i doubt it to be so complex if few parameters are already saved in db. Encryption is really a good application. The reason why this happens is due to uncertainty principle. That  is any of a variety of mathematical inequalities asserting a fundamental limit to the precision with which certain pairs of physical properties of a particle known as complementary variables, such as position x and momentum p, can be known simultaneously. Since we cant know all the parameters of the subatomic, it is impossible to copy. making hacking impossible. Then people say what about quantum entanglement. The state of one system can be entangled with the state of another system. For instance, one can use the controlled NOT gate and the Walsh–Hadamard gate to entangle two qubits. This is not cloning. No well-defined state can be attributed to a subsystem of an entangled state. Cloning is a process whose result is a separable state with identical factors.

[patrickjp93]

Only problem is with most classical app. it will be slow. for a robotic arms if it is adaptive algorithm learning from environment it will be fast. If the number of environment variables is to high and as i said in case of deep neural networks for learning. I am not so sure of GPS quickest root as i do not know the algorithm used. But i doubt it to be so complex if few parameters are already saved in db. Encryption is really a good application. The reason why this happens is due to uncertainty principle. That  is any of a variety of mathematical inequalities asserting a fundamental limit to the precision with which certain pairs of physical properties of a particle known as complementary variables, such as position x and momentum p, can be known simultaneously. Since we cant know all the parameters of the subatomic, it is impossible to copy. making hacking impossible. Then people say what about quantum entanglement. The state of one system can be entangled with the state of another system. For instance, one can use the controlled NOT gate and the Walsh–Hadamard gate to entangle two qubits. This is not cloning. No well-defined state can be attributed to a subsystem of an entangled state. Cloning is a process whose result is a separable state with identical factors.

No no no, no learning for the case of the robotic arm. It would be image processing, graph construction, and running the quantum solution for Traveling Salesman. Quantum computers are highly suited for the NP-Complete problems.

[jos]

No no no, no learning for the case of the robotic arm. It would be image processing, graph construction, and running the quantum solution for Traveling Salesman. Quantum computers are highly suited for the NP-Complete problems.

In complexity theory you're interested in how fast will some different variations of computing machines (that might not be turing-complete or might by more than turing complete) be at solving some problems. Conversely, if you assume a model of computing (for example, AC0 is the model you get if your computing machine is restricted to be a circuit of constant depth plus a polynomial number of AND and OR gates; the L^HALTING model is what you get if you augment a finite-state automaton with an oracle that can tell it whether or not a given program halts). The model P is the set of problems that you can solve with a traditional turing machine with only a polynomial number of steps. It is also the set of problems that you can specify with first-order-logic formulas plus a least-fixed-point operator. NP is the set of problems solvable by a nondeterministic turing machine in polynomial time, but it also is the set of problems that a deterministic turing machine can verify a solution of in polynomial time, the class of problems for which there exists a probabilistic proof checker that can verify a solution to it with as high a probability as you want by checking only a constant number of bits from a properly-formatted proof. I could go on.

But what I'm getting at is that quantum computing today is basically trying to solve the question of where would you place the BQP (bounded-error quantum probabilistic algorithms) complexity class in the complexity zoo. If it is found equivalent to NP (that is, if a quantum computer can solve any NP problem with as high a probability as you want in polynomial time) that's what it means. There is nothing known so far that makes this a priori impossible. What there is is an exponential speedup for some problems (ie, problems that are not known to be in BPP, which is the deterministic equivalent of BQP for turing machines, are known to be in BQP), no NP-complete problem is in BQP and it does not seem possible for a quantum cmputer to solve an NP-complete problem. On the other hand, if you can prove that BQP is better than BPP but worse than NP you've just proven that P is not NP. From my experience deep neural network is where more speeding was necessary in image processing. but thinking now. it has some iterative elements to it adjusting the weights. so may be it may cause problem in quantum computing.

[Bensemus]

@vm'N ok I went and reread your replay and the link and it has nothing to prove that Broadwell is not 14nm. What am I missing?