Quantum computing has gained media attention about the exponential increase in computational speeds but only when dealing with specific problems which are prone to a quantum computational approach. Do you agree with this statement currently and if so do you think that this limit can be broken with future advances of the technology?
Current machine learning strategies involve processing copious amounts of data to reach an output. This is a common bottleneck with classical computing and discourages more ambitious research with machine learning. Do you believe that Quantum Computing can/will be able to elevate this bottleneck in machine learning? If not, does Quantum Computing provide alternative approaches for machine learning?
The problem of decoherence is regarded as one of the bigger obstacles to the development (and application) of Quantum Computing To what extent does decoherence (in your opinion) offer a drawback when applying quantum computing to other fields? If so, do you believe it will be possible to reduce this drawback to negligible in the future?
Thank you! Ross