  The eigenvalue density of a matrix plays an important role in various types
of scientific computing such as electronic-structure calculations. In this
paper, we propose a quantum algorithm for computing the eigenvalue density in a
given interval. Our quantum algorithm is based on a method that approximates
the eigenvalue counts by applying the numerical contour integral and the
stochastic trace estimator applied to a matrix involving resolvent matrices. As
components of our algorithm, the HHL solver is applied to an augmented linear
system of the resolvent matrices, and the quantum Fourier transform (QFT) is
adopted to represent the operation of the numerical contour integral. To reduce
the size of the augmented system, we exploit a certain symmetry of the
numerical integration. We also introduce a permutation formed by CNOT gates to
make the augmented system solution consistent with the QFT input. The
eigenvalue count in a given interval is derived as the probability of observing
a bit pattern in a fraction of the qubits of the output state.
