  Topological data analysis has emerged as a powerful tool for analyzing
large-scale data. High-dimensional data form an abstract simplicial complex,
and by using tools from homology, topological features could be identified.
Given a simplex, an important feature is so-called Betti numbers. Calculating
Betti numbers classically is a daunting task due to the massive volume of data
and its possible high-dimension. While most known quantum algorithms to
estimate Betti numbers rely on homology, here we consider the `dual' approach,
which is inspired by Hodge theory and de Rham cohomology, combined with recent
advanced techniques in quantum algorithms. Our cohomology method offers a
relatively simpler, yet more natural framework that requires exponentially less
qubits, in comparison with the known homology-based quantum algorithms.
Furthermore, our algorithm can calculate its $r$-th Betti number $\beta_r$ up
to some multiplicative error $\delta$ with running time $\mathcal{O}\big(
\log(c_r) c_r^2 / (c_r - \beta_r)^2 \delta^2 \big)$, where $c_r$ is the number
of $r$-simplex. It thus works best when the $r$-th Betti number is considerably
smaller than the number of the $r$-simplex in the given triangulated manifold.
