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Consider a one-dimensional function $f(x)$ with $0 \leq x < 1$. The variable $x$ can be expressed to high precision as a binary fraction \begin{equation} x = 0.b_1b_2\cdots b_n = b_1/2 + b_2/2^2 + \ldots + b_n/2^n \ . \end{equation} described by bits $b_i = 0,1$. (For example, $1/3 \approx 0.010101$.) This way of writing numbers is similar to the to the binary representation of integers, but with the numbers stepping through a finely-spaced grid of spacing $1/2^n$ instead of steps of size 1.
Next we can write the values the function $f(x)$ takes on this grid as $f(x) = f(0.b_1b_2\cdots b_n) = F^{b_1 b_2 \cdots b_n}$ so that the values of $f(x)$ have been repackaged into an $n$-index tensor $F$.
The last move is to approximate of $F$ as a tensor network, such as a matrix product state / tensor train network: \begin{equation} F^{b_1 b_2 \cdots b_n} = \sum_{\{\mathbf{\alpha}\}} A^{b_1}_{\alpha_1} A^{b_2}_{\alpha_1 \alpha_2} A^{b_3}_{\alpha_2 \alpha_3} \cdots A^{b_n}_{\alpha_{n-1}} \end{equation}
Tensor networks of this class turn out to have very low rank (or bond dimension) for a wide class of functions $f(x)$ such as functions which are smooth or have periodic or self-similar structure. For example, both the functions $f(x) = e^{i k x}$ and $f(x) = \delta(x-k)$ give MPS/TT ranks of exactly one (Kronecker product of vectors).
The idea of using tensor train (TT) this way is known as the “quantics tensor train” (QTT) representation of a function. It can be straightforwardly generalized to two-dimensional, three-dimensional, or higher-dimensional functions by using a tensor train with two, three, or more open indices on each tensor.
“Quantum-inspired algorithms for multivariate analysis: from interpolation to partial differential equations”
Author: Juan Jose Garcia-Ripoll
Journal: Quantum 5, 431 (2021)
Notes: Uses of the QTT format to perform many computational tasks, such as interpolating functions, computing Fourier transforms, and solving differential equations.
“Multigrid Renormalization”
Author: Michael Lubasch, Pierre Moinier, Dieter Jaksch
Journal: J. Comp. Phys. 372, 587 (2018)
arxiv link: arxiv:1802.07259
Notes: Physics inspired methods for working with QTT functions, such as “prolonging” coarse-grid functions to finer grids and solving equations.
“Tensor numerical methods for high-dimensional PDEs: Basic theory and initial applications”
Author: Boris N. Khoromskij
Journal: arxiv:1408.4053
Notes: Applications of the QTT format to solving partial differential equations.
“O(d log(n))-Quantics Approximation of Nd tensors in High-Dimensional Numerical Modeling”
Author: Boris N. Khoromskij
Journal: Constr Approx 34, 257–280 (2011)
Notes: Introduces the quantics, or quantized tensor train (QTT) format for representing functions as tensor trains or matrix product states.
“Image Compression and Entanglement”
Author: Jose I. Latorre
Journal: quant‑ph/0510031
Notes: Proposes a binary “amplitude encoding” of images into quantum states.