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arXiv:2504.11049

A quantum algorithm for estimating the determinant

Vittorio Giovannetti, Seth Lloyd, Lorenzo Maccone

Published Apr 15, 2025• 0 citations
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Abstract

We present a quantum algorithm for estimating the matrix determinant based on quantum spectral sampling. The algorithm estimates the logarithm of the determinant of an $n \times n$ positive sparse matrix to an accuracy $ε$ in time ${\cal O}(\log n/ε^3)$, exponentially faster than previously existing classical or quantum algorithms that scale linearly in $n$. The quantum spectral sampling algorithm generalizes to estimating any quantity $\sum_j f(λ_j)$, where $λ_j$ are the matrix eigenvalues. For example, the algorithm allows the efficient estimation of the partition function $Z(β) =\sum_j e^{-βE_j}$ of a Hamiltonian system with energy eigenvalues $E_j$, and of the entropy $ S =-\sum_j p_j \log p_j$ of a density matrix with eigenvalues $p_j$.

DOI
10.48550/arxiv.2504.11049

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We propose new quantum algorithms for estimating spectral sums of positive semi-definite (PSD) matrices. For a matrix A and a function f, the spectral sum is the trace of f(A), equivalently the sum over eigenvalues of A of f applied to each eigenvalue. Typical examples of spectral sums are the von Neumann entropy, the trace of the inverse of A, the log-determinant, and the Schatten p-norm, where the latter does not require the matrix to be PSD. The current best classical randomized algorithms estimating these quantities have a runtime that is at least linearly in the number of nonzero entries of the matrix and quadratic in the estimation error. Assuming access to a block-encoding of a matrix, our algorithms are sub-linear in the matrix size, and depend at most quadratically on other parameters, like the condition number and the approximation error, and thus can compete with most of the randomized and distributed classical algorithms proposed in the literature, and polynomially improve the runtime of other quantum algorithms proposed for the same problems. We show how the algorithms and techniques used in this work can be applied to three problems in spectral graph theory: approximating the number of triangles, the effective resistance, and the number of spanning trees in a graph.
We propose new quantum algorithms for estimating spectral sums of positive semi-definite (PSD) matrices. The spectral sum of an PSD matrix AAA, for a function fff, is defined as $ \text{Tr}[f(A)] = \sum_j f(λ_j)$, where λjλ_jλj​ are the eigenvalues of AAA. Typical examples of spectral sums are the von Neumann entropy, the trace of A−1A^{-1}A−1, the log-determinant, and the Schatten ppp-norm, where the latter does not require the matrix to be PSD. The current best classical randomized algorithms estimating these quantities have a runtime that is at least linearly in the number of nonzero entries of the matrix and quadratic in the estimation error. Assuming access to a block-encoding of a matrix, our algorithms are sub-linear in the matrix size, and depend at most quadratically on other parameters, like the condition number and the approximation error, and thus can compete with most of the randomized and distributed classical algorithms proposed in the literature, and polynomially improve the runtime of other quantum algorithms proposed for the same problems. We show how the algorithms and techniques used in this work can be applied to three problems in spectral graph theory: approximating the number of triangles, the effective resistance, and the number of spanning trees within a graph.
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Gaussian processes (GPs) are important models in supervised machine learning. Training in Gaussian processes refers to selecting the covariance functions and the associated parameters in order to improve the outcome of predictions, the core of which amounts to evaluating the logarithm of the marginal likelihood (LML\mathrm{LML}LML) of a given model. The LML\mathrm{LML}LML gives a concrete measure of the quality of prediction that a GP model is expected to achieve. The classical computation of the LML\mathrm{LML}LML typically carries a polynomial time overhead with respect to the input size. We propose a quantum algorithm that computes the logarithm of the determinant of a Hermitian matrix, which runs in logarithmic time for sparse matrices. This is applied in conjunction with a variant of the quantum linear system algorithm that allows for logarithmic-time computation of the form ${\mathbf{y}}^{T}{A}^{\ensuremath{-}1}\mathbf{y}$, where y\mathbf{y}y is a dense vector and AAA is the covariance matrix. We hence show that quantum computing can be used to estimate the LML\mathrm{LML}LML of a GP with exponentially improved efficiency under certain conditions.
We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method , which determines the frequencies and damping factors of signals consisting of finite sums of exponentially damped sinusoids. Our algorithm provides a quantum speedup in a natural regime where the sampling rate is much higher than the number of sinusoid components. Along the way, we develop techniques that are expected to be useful for other quantum algorithms as well—consecutive phase estimations to efficiently make products of asymmetric low rank matrices classically accessible and an alternative method to efficiently exponentiate non-Hermitian matrices. Our algorithm features an efficient quantum–classical division of labor: the time-critical steps are implemented in quantum superposition, while an interjacent step, requiring much fewer parameters, can operate classically. We show that frequencies and damping factors can be obtained in time logarithmic in the number of sampling points, exponentially faster than known classical algorithms.
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