In mathematics, the Hermite transform is an integral transform named after the mathematician Charles Hermite that uses Hermite polynomials H n ( x ) {\displaystyle H_{n}(x)} as kernels of the transform.
The Hermite transform H { F ( x ) } ≡ f H ( n ) {\displaystyle H\{F(x)\}\equiv f_{H}(n)} of a function F ( x ) {\displaystyle F(x)} is H { F ( x ) } ≡ f H ( n ) = ∫ − ∞ ∞ e − x 2 H n ( x ) F ( x ) d x {\displaystyle H\{F(x)\}\equiv f_{H}(n)=\int _{-\infty }^{\infty }e^{-x^{2}}\ H_{n}(x)\ F(x)\ dx}
The inverse Hermite transform H − 1 { f H ( n ) } {\displaystyle H^{-1}\{f_{H}(n)\}} is given by H − 1 { f H ( n ) } ≡ F ( x ) = ∑ n = 0 ∞ 1 π 2 n n ! f H ( n ) H n ( x ) {\displaystyle H^{-1}\{f_{H}(n)\}\equiv F(x)=\sum _{n=0}^{\infty }{\frac {1}{{\sqrt {\pi }}2^{n}n!}}f_{H}(n)H_{n}(x)}
Direct evaluation of a discrete Hermite transform with N {\displaystyle N} coefficients and O ( N ) {\displaystyle O(N)} sample points requires O ( N 2 ) {\displaystyle O(N^{2})} arithmetic operations. Leibon, Rockmore, Park, Taintor, and Chirikjian developed fast algorithms for the forward and inverse transforms requiring O ( N log 2 N ) {\displaystyle O(N\log ^{2}N)} operations, exploiting the three-term recurrence satisfied by the Hermite polynomials.
Jain, Iyer, Somma, Bao, and Jordan developed a quantum algorithm that implements an approximate discrete Hermite transform using a number of quantum gates polynomial in log N {\displaystyle \log N} and log ( 1 / ε ) {\displaystyle \log(1/\varepsilon )} , where N {\displaystyle N} is the number of Hermite modes and ε {\displaystyle \varepsilon } is the approximation error. The algorithm uses fast-forwarding of the quantum harmonic oscillator. It transforms information encoded in the amplitudes of a quantum state, rather than producing an explicit classical list of transform coefficients; the gate bound does not include preparing an arbitrary input state from classical data.
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