While there are many interesting questions that a computer can solve with barely any data (such as factorization, which requires "only" a single integer), most real-world applications, such as machine learning or AI, will require large amounts of data.

Can quantum computers handle this massive stream of data, in theory or in practice? Is it a good idea to store the data in a "quantum memory", or is it better to store it in a "classical memory"?

  • $\begingroup$ just for clarification, are you simply asking whether it is possible and/or convenient to handle large amounts of data with quantum computers? $\endgroup$ – glS Apr 1 '18 at 19:56
  • $\begingroup$ @glS Yes, that is what the final paragraph states. $\endgroup$ – Discrete lizard Apr 1 '18 at 20:45

It's not so much a matter of big data, but that of saving data. Quantum Storage is still (much like the rest of the field) in its infancy.

(Take what I write with a grain of salt. It's likely to change rapidly)
There are a few theories on how quantum computers might be able to hold "memory".

One of these is using nuclear spin. E.g. using long-lived nuclei in a quantum state. Converting an electron qubit (a qubit represented by an electron) to a nuclear qubit is possible.

Why nuclear qubit/spin?

A nucleus's coherence time - the time for which its phase is constant (when considering its wave function) - is longer than that of an electron. The linked article (same one as before) details how one can increase the coherence time of a nuclear spin (to some extent). The matter is being researched, but there is indication that nuclear qubits can be a form of quantum storage.

What makes it difficult

The quantum state needs to remain, well, quantum. Additionally, if you entangle two of your "storage" qubits, you are likely to lose data.

Due to no-cloning, one cannot simply "copy" a qubit (who's state is unknown), which is one of the reasons quantum storage is difficult.

As for "big" data, it's just a matter of how much memory you have.

  • $\begingroup$ Interesting. Although I have one remark. "As for "big" data, it's just a matter of how much memory you have.", This, I think, is a bit naive. Be aware that when dealing with $10^5$ TB LIDAR data, (i.e., elevation data for every square meter in whole Europa (that's about a square big footstep for you non-metric folks))), your data doesn't fit into memory, no way! $\endgroup$ – Discrete lizard Mar 22 '18 at 20:57
  • $\begingroup$ There is an entire sub-field of algorithmics (known as I/O or cache-aware algorithms, of which some experts are at my uni ) that says, wait the common RAM (random access machine, i.e. 'we can get data everywhere in $O(1)$') model doesn't help us here, I/O-latency is what dominates the actual running time, we have to be 'cache aware'. So, I think your final remark is a bit too naive. But feel free to expand on it! $\endgroup$ – Discrete lizard Mar 22 '18 at 20:59
  • $\begingroup$ @Discretelizard good point. I'll rethink that last bit $\endgroup$ – ItamarG3 Mar 23 '18 at 7:33
  • $\begingroup$ It may be interesting to think about how one can 'bridge' classical storage with QC, if Quantum storage is hard and possibly not very useful. $\endgroup$ – Discrete lizard Mar 23 '18 at 7:55

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