He and other critics say the company must still prove two things: that its qubits really can enter superpositions and become entangled, and that the chip delivers a significant "quantum speed-up" compared to a classical computer working on the same problem.
What are the most promising avenues for novel quantum speed-ups: are they in quantum simulation, in quantum machine learning, in new algorithms for post-quantum crypto, in algorithms for quantum learning or testing?
That this quadratic "speed-up" is also the optimal quantum "speed-up" possible for this problem was proved by Bennett, Bernstein, Brassard and Vazirani (1997).
Even if we could rule out the MWQC, the problem of finding the physical resource(s) responsible for quantum "speed-up" would remain a difficult one.
Shor's result is the most dramatic example so far of quantum "speed-up" of computation, notwithstanding the fact that factoring is believed to be only in NP and not in NP-complete.
Moreover even if these features \(do\) play an essential role in the putative quantum "speed-up", one must still answer the question of how they do so (Fortnow 2003, Cuffaro forthcoming).
Now, four separate teams have taken a step toward achieving such "quantum speed-up" by demonstrating a simpler, more limited form of quantum computing that, if it can be improved, might soon give classical computers a run for their money.
Also, while simulations of universal quantum computers show strong evidence of quantum speed-up, so far, it's unclear whether quantum annealing produces better results than could be achieved using conventional computers.
It has been shown, for example, that analyzing some spatial structure [2,27,39] as an extension of quantum speed-up algorithm [15,16], and application to a universal computation in quantum mechanical computers [6], expressing the energy transfer on the chromatographic network in the photosynthetic system [30] and so on are strongly influenced by its virtue.
The Grover quantum search algorithm, one of only a few representative quantum algorithms, can speed up many classical algorithms that use search heuristics.
We are studying ways to use quantum computing to speed up dynamic programming/reinforcement learning.
