Does anyone know how to make a quantum circuit to compute exponentials where the exponent can be a fraction? To be more precise, I'm looking for a fixed point quantum arithmetic circuit that does the following:

$$|\vec{x}\rangle|\vec{0}\rangle \rightarrow |\vec{x}\rangle|e^x\rangle$$

where $|\vec{x}\rangle = |x_2x_1x_0x_{-1}x_{-2}\rangle$ is a $5$ qubit register, $|\vec{0}\rangle$ is an $n$ qubit register and where $x$ represents the binary number $$x = x_2\times 2^2 + x_1\times 2^1 + x_0\times 2^0+x_{-1}\times 2^{-1}+ x_{-2}\times 2^{-2}$$

and where $|e^x\rangle$ holds the fixed point representation of $e^x$ to within an error of $\epsilon$ (therefore the value of $n$ will be chosen based on the value of the desired $\epsilon$)

  • $\begingroup$ So $x$ is a 5-bit number, and $e^x$ is the exponential of it, which is could be 6-bits if $x_2=1$? How would the 6-bit number be defined if $x$ is defined in that symmetric way which requires an odd number of bits? Maybe 6-bit numbers have to be represented by something more bits long even if some bits aren't needed. Good luck with the question and +1 ! $\endgroup$ Mar 25 at 20:34
  • $\begingroup$ Ah right that's why I have defined $|\vec{0}\rangle$ as an $n$ bit register but I can see how that could be made more explicit in my question. I have added a bit of clarity to the question. Thanks for pointing that out! $\endgroup$ Mar 25 at 20:38
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    $\begingroup$ Another method could be to take the taylor expansion and then use a polynomial encoding circuit to encode this in the amplitude using rotations (see section D, static-content.springer.com/esm/…, for encoding polynomials into amplitudes). You can then use QFT^-1 to encode this value into a register. $\endgroup$
    – Sam Palmer
    Mar 30 at 18:59
  • $\begingroup$ As an aside, for $x<0$ here is a paper implementing a polynomial approximation method arxiv.org/abs/1805.12445 (as most people assumed correctly I am interested in the case when $x>0$ but I thought that maybe someone looking up this question might find this useful too) $\endgroup$ Apr 14 at 12:31

If $x$ is only 5 bits long, your best bet is almost definitely to just encode all the possible answers into a QROM circuit and do a lookup.

Here's a QROM circuit diagram from https://arxiv.org/abs/1805.036621:

enter image description here

And here's Q# source code for constructing a lookup circuit, from ancillary files of https://arxiv.org/abs/1905.076821.

/// # Summary
/// Performs 'a ^^^= T[b]' where 'a' and 'b' are little-endian quantum registers
/// and 'T' is a classical table.
/// Bits in 'T[b]' beyond the range of 'a' are ignored.
/// It is assumed that the address will never store a value beyond the end of the table.
/// Invalid addresses cause undefined behavior.
/// # Input
/// ## lvalue
/// The target of the xor. The 'a' in 'a ^^^= T[b]'.
/// ## table
/// The classical table containing integers to choose between and xor into the target.
/// The 'T' in 'a ^^^= T[b]'.
/// ## address
/// Determines which integer from the table will be xored into the target.
/// The 'b' in 'a ^^^= T[b]'.
operation XorEqualLookup (lvalue: LittleEndian, table: BigInt[], address: LittleEndian) : Unit {
    body (...) {
        Controlled XorEqualLookup(new Qubit[0], (lvalue, table, address));
    adjoint self;
    controlled (cs, ...) {
        if (Length(table) == 0) {
            fail "Can't lookup values in an empty table.";

        // Drop high bits that would place us beyond the range of the table.
        let maxAddressLen = CeilLg2(Length(table));
        if (maxAddressLen < Length(address!)) {
            let kept = LittleEndian(address![0..maxAddressLen - 1]);
            return Controlled XorEqualLookup(cs, (lvalue, table, kept));

        // Drop inaccessible parts of table.
        let maxTableLen = 1 <<< Length(address!);
        if (maxTableLen < Length(table)) {
            let kept = table[0..maxTableLen-1];
            return Controlled XorEqualLookup(cs, (lvalue, kept, address));

        // Base case: singleton table.
        if (Length(table) == 1) {
            XorEqualConst(address, ToBigInt(-1));
            Controlled XorEqualConst(cs + address!, (lvalue, table[0]));
            XorEqualConst(address, ToBigInt(-1));
            return ();

        // Recursive case: divide and conquer.
        let highBit = address![Length(address!) - 1];
        let restAddress = LittleEndian(address![0..Length(address!) - 2]);
        let h = 1 <<< (Length(address!) - 1);
        let lowTable = table[0..h-1];
        let highTable = table[h..Length(table)-1];
        using (q = Qubit()) {
            // Store 'all(controls) and not highBit' in q.
            Controlled InitToggle(cs + [highBit], q);

            // Do lookup for half of table where highBit is 0.
            Controlled XorEqualLookup([q], (lvalue, lowTable, restAddress));

            // Flip q to storing 'all(controls) and highBit'.
            Controlled X(cs, q);

            // Do lookup for half of table where highBit is 1.
            Controlled XorEqualLookup([q], (lvalue, highTable, restAddress));

            // Eager uncompute 'q = all(controls) and highBit'.
            Controlled UncomputeToggle(cs + [highBit], q);
    controlled adjoint self;

1 Note: I'm an author

  • $\begingroup$ Thank you for the explanation, comments and references Craig! Would this be reasonable for higher numbers of bits in $x$, say 10 or 20? And if not, do you know if there are implementations for generic multi-bit values of $x$ that use things like polynomial approximations or any other technique as like that? $\endgroup$ Mar 25 at 20:53
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    $\begingroup$ @RajivKrishnakumar For 20 I'd probably do an interpolated lookup, and maybe try to extract factors of 2 to canonicalize the problem because factors of 2 can be handled by manual shifts. $\endgroup$ Mar 25 at 21:39
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    $\begingroup$ @RajivKrishnakumar You could also break $e^{x}$ into $e^{x_1 + x_2/2^5 + x_3 / 2^10 + x_4 / 2^15}$ where each $x_k$ value was a 5 qubit integer, separately look up each, and multiply them all together. $\endgroup$ Mar 30 at 23:14

This might not be the most ideal implementation but an interesting way you could approximate this is using $R_y$s and $QFT^{-1}$.

If we Taylor expand $e^x = 1 + x + \frac{x^2}{2!} ...$ we can use the polynomial amplitude encoding in https://static-content.springer.com/esm/art%3A10.1038%2Fs41534-019-0130-6/MediaObjects/41534_2019_130_MOESM1_ESM.pdf, Section D.

Using the example from the paper for a polynomial $p(x) = ax^2 + bx + c$, where we substitute our coefficients from the Taylor expansion, we can apply the controlled $R_y$s to an ancilla:

$ U_{e^x}|0\rangle = R_y(1.5)^{q_2}R_y(2)^{q_1,q_2}R_y(4)^{q_1}R_y(1)|0\rangle \rightarrow \cos(e^x/2)|0\rangle + \sin(e^x/2)|1\rangle$.

We can use take QFT of the ancilla to a register $|R\rangle$

$QFT^{-1}U_{e^x}|0\rangle|R\rangle \rightarrow U_{e^x}|0\rangle|\sin(e^x/2)\rangle$.

Finally we can use the approximation $\sin(x) \approx x$ assuming $x$ is sufficiently small. We can control this by scaling the Taylor approximation polynomial.

$QFT^{-1}U_{e^x}|0\rangle|R\rangle \rightarrow U_{e^x}|0\rangle|\sin(e^x/2)\rangle \approx U_{e^x}|0\rangle|e^x/2\rangle $

The main caveat is we need to ensure the correct scaling, with a constant $K$, ideally we would like our scaling s.t. $\sup Ke^x < \frac{\pi}{4}$ in our domain of $x$. As such we would obtain


and then we would need to have a circuit to descale. If we take $K$ in the form of $2^{-n}$, then in fact we don't need to implement a descaling circuit and we can just rescale the values of the coefficients of our binary representation by $2^{n+1}$ to recover our approximation to $e^x$.

Note: Here was are assuming $x$ is a binary integer.


Leaving this here. Recently published, might be useful for this question.

Efficient Evaluation of Exponential and Gaussian Functions on a Quantum Computer https://arxiv.org/abs/2110.05653

  • $\begingroup$ Just found this yesterday too! Thanks for posting it here :) $\endgroup$ Oct 14 at 15:54

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