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arXiv:2307.04681 · Physical Review A

Matrix permanent and determinant from a spin system

Abhijeet Alase, Owen Doty, David L. Feder

Published Jul 19, 2023• 3 citations
arXivHTMLPDF

Abstract

In contrast to the determinant, no algorithm is known for the exact determination of the permanent of a square matrix that runs in time polynomial in its dimension. Consequently, noninteracting fermions are classically efficiently simulatable while noninteracting bosons are not, underpinning quantum supremacy arguments for sampling the output distribution of photon interferometer arrays. This work introduces a graph-theoretic framework that bridges both the determinant and permanent. The only nonzero eigenvalues of a sparse non-Hermitian operator $\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}$ for $n$ spin-$1/2$ particles are the $n\mathrm{th}$ roots of the permanent or determinant of an $n\ifmmode\times\else\texttimes\fi{}n$ matrix $M$, interpreting basis states as bosonic or fermionic occupation states, respectively. This operator can be used to design a simple and straightforward method for the classical determination of the permanent that matches the efficiency of the best-known algorithm. Gauss-Jordan elimination for the determinant of $M$ is then equivalent to the successive removal of the generalized zero eigenspace of the fermionic $\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}$, equivalent to the deletion of some nodes and reweighting of the remaining edges in the graph such that only $n$ nodes survive after the last step. In the bosonic case, the successive removal of generalized zero eigenspaces for $\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}$ is also equivalent to node deletion, but new edges are added during this process, which gives rise to the higher complexity of computing the permanent. Our analysis may point the way to new strategies for classical and quantum evaluation of the permanent.

DOI
10.1103/physreva.108.012207

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In contrast to the determinant, no algorithm is known for the exact determination of the permanent of a square matrix that runs in time polynomial in its dimension. Consequently, noninteracting fermions are classically efficiently simulatable while noninteracting bosons are not, underpinning quantum supremacy arguments for sampling the output distribution of photon interferometer arrays. This work introduces a graph-theoretic framework that bridges both the determinant and permanent. The only nonzero eigenvalues of a sparse non-Hermitian operator M\ifmmode\else˘\textasciibreve\fi\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}M\ifmmode\else˘​\textasciibreve\fi​ for nnn spin-1/21/21/2 particles are the nthn\mathrm{th}nth roots of the permanent or determinant of an n\ifmmode×\else\texttimes\finn\ifmmode\times\else\texttimes\fi{}nn\ifmmode×\else\texttimes\fin matrix MMM, interpreting basis states as bosonic or fermionic occupation states, respectively. This operator can be used to design a simple and straightforward method for the classical determination of the permanent that matches the efficiency of the best-known algorithm. Gauss-Jordan elimination for the determinant of MMM is then equivalent to the successive removal of the generalized zero eigenspace of the fermionic M\ifmmode\else˘\textasciibreve\fi\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}M\ifmmode\else˘​\textasciibreve\fi​, equivalent to the deletion of some nodes and reweighting of the remaining edges in the graph such that only nnn nodes survive after the last step. In the bosonic case, the successive removal of generalized zero eigenspaces for M\ifmmode\else˘\textasciibreve\fi\stackrel{\ifmmode \u\else \textasciibreve \fi{}}{M}M\ifmmode\else˘​\textasciibreve\fi​ is also equivalent to node deletion, but new edges are added during this process, which gives rise to the higher complexity of computing the permanent. Our analysis may point the way to new strategies for classical and quantum evaluation of the permanent.
Bosonic and fermionic statistics are well known to give rise to antinomic behaviors, most notably boson bunching vs fermion antibunching. Here, we establish a fundamental relation that combines bosonic and fermionic multiparticle interferences in an arbitrary linear interferometer. The bosonic and fermionic transition probabilities appear together in a single equation which constrains their values, hence expressing a boson–fermion complementarity that is independent of the details of the interferometer. For two particles in any interferometer, for example, it implies that the average between the bosonic and fermionic probabilities must coincide with the probability obeyed by classical particles. Crucially, this fundamental relation also provides a heretofore unknown mathematical identity connecting the squared moduli of the permanent and determinant of an arbitrary complex matrix, hence extending an identity by Muir dating from the nineteenth century.
It is known that computing the permanent of the matrix 1+A1+A1+A, where AAA is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: an expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.
In order to find the outcome probabilities of quantum mechanical systems like the optical networks underlying Boson sampling, it is necessary to be able to compute the permanents of unitary matrices, a computationally hard task. Here we first discuss how to compute the permanent efficiently on a parallel computer, followed by algorithms which provide an exponential speed-up for sparse matrices and linear run times for matrices of limited bandwidth. The parallel algorithm has been implemented in a freely available software package, also available in an efficient serial version. As part of the timing runs for this package we set a new world record for the matrix order on which a permanent has been computed. Next we perform a simulation study of several conjectures regarding the distribution of the permanent for random matrices. Here we focus on permanent anti-concentration conjecture, which has been used to find the classical computational complexity of Boson sampling. We find a good agreement with the basic versions of these conjectures, and based on our data we propose refined versions of some of them. For small systems we also find noticeable deviations from a proposed strengthening of a bound for the number of photons in a Boson sampling system.
It is known that computing the permanent of the matrix 1+A, where A is a finite-rank matrix, requires a number of operations polynomial in the matrix size. Motivated by the boson-sampling proposal of restricted quantum computation, I extend this result to a generalization of the matrix permanent: An expectation value in a product of a large number of identical bosonic states with a bounded number of bosons. This result complements earlier studies on the computational complexity in boson sampling and related setups. The proposed technique based on the Gaussian averaging is equally applicable to bosonic and fermionic systems. This also allows us to improve an earlier polynomial complexity estimate for the fermionic version of the same problem.
We study algorithms for approximating the permanent of a random matrix when the entries are slightly biased away from zero. This question is motivated by the goal of understanding the classical complexity of linear optics and \emph{boson sampling} (Aaronson and Arkhipov '11; Eldar and Mehraban '17). Barvinok's interpolation method enables efficient approximation of the permanent, provided one can establish a sufficiently large zero-free region for the polynomial per(zJ+W)\mathrm{per}(zJ + W)per(zJ+W), where JJJ is the all-ones matrix and WWW is a random matrix with independent mean-zero entries. We show that when the entries of WWW are standard complex Gaussians, all zeros of the random polynomial per(zJ+W)\mathrm{per}(zJ + W)per(zJ+W) lie within a disk of radius O~(n−1/3)\tilde{O}(n^{-1/3})O~(n−1/3), which yields an approximation algorithm when the bias of the entries is Ω~(n−1/3)\tildeΩ(n^{-1/3})Ω~(n−1/3). Previously, there were no efficient algorithms at biases smaller than 1/polylog(n)1/\mathrm{polylog}(n)1/polylog(n), and it was unknown whether there typically exist zeros zzz with ∣z∣≥1|z| \ge 1∣z∣≥1. As a complementary result, we show that the bulk of the zeros, namely (1−ε)n(1 - ε)n(1−ε)n of them, have magnitude Θ(n−1/2)Θ(n^{-1/2})Θ(n−1/2). This prevents our interpolation method from contradicting the conjectured average-case hardness of approximating the permanent. We also establish analogous zero-free regions for the hardcore model on general graphs with complex vertex fugacities. In addition, we prove universality results establishing zero-free regions for random matrices WWW with i.i.d. subexponential entries.
We consider the problem of calculating the approximate value (estimate) of the permanent of a square matrix over the field of real numbers using a photonic quantum computer that sampling bosons from a given distribution. It is pointed out that it is possible to effectively solve this problem for the following three classes of matrices: symmetric matrices which eigenvalues do not exceed 1 in absolute value; orthogonal matrices; doubly stochastic matrices. For the last two classes, we are talking about evaluating the square of the permanent. The mathematical basis for solving the problem is the formula, which represents the permanent of the Hermitian matrix as the mathematical expectation of some random variable which values are equal to the products of the powers of the eigenvalues of the Hermitian matrix. And the corresponding probabilities with which these products are accepted as values of a random variable are also completely determined by the same Hermitian matrix. This circumstance allows using the methods of probability theory in order to solve the problem of permanent estimation.