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I want to simulate an arbitrary isolated quantum circuit acting on $n$ qubits (i.e. a pure state of $n$ qubits).

As I know RAM is the bottleneck for quantum simulators, you can consider a "normal" computer to have between $4$ and $8$ Gio of RAM, all the other components are considered sufficiently powerful to not be the bottleneck.

With this definition of a "normal" computer,

What is the maximum value of $n$ (the number of qubits) for which an arbitrary quantum circuit is simulable in a reasonable time ($<1\text{h}$) with a normal computer and freely accessible simulators?

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    $\begingroup$ You also need to specify how long you're willing to wait, since there are simulation strategies that use very little space but way more time. $\endgroup$
    – Craig Gidney
    Commented Jul 24, 2018 at 14:55
  • $\begingroup$ Presumably you're talking about pure states? $\endgroup$
    – DaftWullie
    Commented Jul 24, 2018 at 15:26
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    $\begingroup$ I edited the question, I talk about pure states. $\endgroup$
    – Adrien Suau
    Commented Jul 24, 2018 at 15:45
  • $\begingroup$ All other components are sufficiently powerful, yet you limit time? This does not make sense. $\endgroup$
    – Norbert Schuch
    Commented Jul 24, 2018 at 21:08
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    $\begingroup$ “Arbitrary quantum circuits” are arbitrarily long and so no simulator (or real quantum computer) can complete them within an hour, no matter the number of qubits. $\endgroup$
    – DaftWullie
    Commented Jul 26, 2018 at 20:35

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This answer doesn't directly answer the question (I have little experience of real simulators with practical overheads etc.), but here's a theoretical upper bound.

Let's assume that you need to store the whole state vector of $k$ qubits in memory. There are $2^n$ elements that are complex numbers. A complex number requires 2 real numbers, and a real number occupies 24 bytes in python. Let's say we want to cram this into $4\times 10^9$ bytes of RAM (probably leaving a few over for your operating system etc.) Hence, $$ 48\times 2^n\leq 4\times 10^9 $$ Rearrange for $n$ and you have $n\leq26$ qubits.

Note that applying gates in a quantum circuit is relatively inexpensive memory-wise. See the "Efficiency Improvements" section in this answer. From that strategy, one should be able to estimate the time it takes to apply a single one- or two-qubit gate to an $n$-qubit system, and hence how many gates you might expect to fit within some times limit (an hour is very modest, but would certainly serve for illustrative purposes).

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  • $\begingroup$ I'd say that's a fairly accurate estimate: All the memory you need is basically the one used to store the state. $\endgroup$
    – Norbert Schuch
    Commented Jul 24, 2018 at 21:10
  • $\begingroup$ Where do those 24 bytes come from, given that a usual double has 64 bits? $\endgroup$
    – Norbert Schuch
    Commented Aug 15, 2018 at 22:24
  • $\begingroup$ @NorbertSchuch I Have no idea! $\endgroup$
    – DaftWullie
    Commented Aug 16, 2018 at 5:29
  • $\begingroup$ Could it be that it's just not true? $\endgroup$
    – Norbert Schuch
    Commented Aug 17, 2018 at 23:58
  • $\begingroup$ @NorbertSchuch it does seem to be what is consistently claimed online for recent versions of python. There’s nothing wrong with it using 3 times as much memory as you might hope. It’s not like it’s as weird as 24 bits would be. $\endgroup$
    – DaftWullie
    Commented Aug 18, 2018 at 5:12
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Along with depending on time constraints, as Craig mentioned, you also need to specify how accurate/what gates you want the simulation to have. CHP (CNOT, Phase, Hadamard) simulations can do incredibly large circuits with large numbers of qubits incredible quickly, however they only allow a certain restricted gate set, so some gates, such as T gates, must be approximated.

Other simulations exist (such as quantumsim and others) which store full density matrices, and as a result are much more significantly limited in the number of qubits they work with seeing as they must store a $2^n \times 2^n$ matrix.

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