Currently the best deterministic polynomial-time algorithm for approximating the permanent of a non-negative matrix is based on minimizing the Bethe free energy function of a certain normal factor graph (NFG). In order to improve the approximation guarantee, we propose a modified NFG with fewer cycles, but still manageable function-node complexity; we call the approximation obtained by minimizing the function of the modified normal factor graph the modified Bethe permanent. For nonnegative matrices of size 3 × 3, we give a tight characterization of the modified Bethe permanent. For non-negative matrices of size n ≥ n with n ě 3, we present a partial characterization, along with promising numerical results. The analysis of the modified NFG is also interesting because of its tight connection to an NFG that is used for approximating a permanent-like quantity in quantum information processing.
Currently the best deterministic polynomial-time algorithm for approximating the permanent of a non-negative matrix is based on minimizing the Bethe free energy function of a certain normal factor graph (NFG). In order to improve the approximation guarantee, we propose a modified NFG with fewer cycles, but still manageable function-node complexity; we call the approximation obtained by minimizing the function of the modified normal factor graph the modified Bethe permanent. For nonnegative matrices of size 3 × 3, we give a tight characterization of the modified Bethe permanent. For non-negative matrices of size n ≥ n with n ě 3, we present a partial characterization, along with promising numerical results. The analysis of the modified NFG is also interesting because of its tight connection to an NFG that is used for approximating a permanent-like quantity in quantum information processing.
In this thesis, we leverage finite graph covers to analyze the SPA and the Bethe partition function for both S-FGs and DE-FGs. There are two main contributions in this thesis. The first main contribution concerns a special class of S-FGs where the partition function of each S-FG equals the permanent of a nonnegative square matrix. The Bethe partition function for such an S-FG is called the Bethe permanent. A combinatorial characterization of the Bethe permanent is given by the degree-M Bethe permanent, which is defined based on the degree-M graph covers of the underlying S-FG. In this thesis, we prove a degree-M-Bethe-permanent-based lower bound on the permanent of a non-negative square matrix, resolving a conjecture proposed by Vontobel in [IEEE Trans. Inf. Theory, Mar. 2013]. We also prove a degree-M-Bethe-permanent-based upper bound on the permanent of a non-negative matrix. In the limit M→∞, these lower and upper bounds yield known Bethe-permanent-based lower and upper bounds on the permanent of a non-negative square matrix. The second main contribution is giving a combinatorial characterization of the Bethe partition function for DE-FGs in terms of finite graph covers. In general, approximating the partition function of a DE-FG is more challenging than for an S-FG because the partition function of the DE-FG is a sum of complex values and not just a sum of non-negative real values. Moreover, one cannot apply the method of types for proving the combinatorial characterization as in the case of S-FGs. We overcome this challenge by applying a suitable loop-calculus transform (LCT) for both S-FGs and DE-FGs. Currently, we provide a combinatorial characterization of the Bethe partition function in terms of finite graph covers for a class of DE-FGs satisfying an (easily checkable) condition.
Approximating the permanent of a complex-valued matrix is a fundamental problem with applications in Boson sampling and probabilistic inference. In this paper, we extend factor-graph-based methods for approximating the permanent of non-negative-real-valued matrices that are based on running the sum-product algorithm (SPA) on standard normal factor graphs, to factor-graph-based methods for approximating the permanent of complex-valued matrices that are based on running the SPA on double-edge normal factor graphs.
On the algorithmic side, we investigate the behavior of the SPA, in particular how the SPA fixed points change when transitioning from real-valued to complex-valued matrix ensembles. On the analytical side, we use graph covers to analyze the Bethe approximation of the permanent, i.e., the approximation of the permanent that is obtained with the help of the SPA.
This combined algorithmic and analytical perspective provides new insight into the structure of Bethe approximations in complex-valued problems and clarifies when such approximations remain meaningful beyond the non-negative-real-valued settings.
The algorithm and complexity of approximating the permanent of a matrix is an extensively studied topic. Recently, its connection with quantum supremacy and more specifically BosonSampling draws a special attention to the average-case approximation problem of the permanent of random matrices with zero or small mean value for each entry. Eldar and Mehraban (FOCS 2018) gave a quasi-polynomial time algorithm for random matrices with mean at least 1/polyloglog(n). In this paper, we improve the result by designing a deterministic quasi-polynomial time algorithm and a PTAS for random matrices whose module of mean is at least 1/ polylog(n). We note that if the algorithm can be further improved to work with a mean value that is a sufficiently small 1/poly(n), it will disprove a central conjecture for quantum supremacy. Our algorithm is also much simpler and has a better and flexible trade-off for running time. The running time can be quasi-polynomial in both n and 1/∊, or PTAS (polynomial in n but exponential in 1/∊), where ∊ is the approximation parameter.
The permanent is #P-hard to compute exactly on average for natural random matrices including matrices over finite fields or Gaussian ensembles. Should we expect that it remains #P-hard to compute on average if we only care about approximation instead of exact computation? In this work we take a first step towards resolving this question: We present a quasi-polynomial time deterministic algorithm for approximating the permanent of a typical n × n random matrix with unit variance and vanishing mean μ = O(ln ln n)-1/8to within inverse polynomial multiplicative error. (alternatively, one can achieve permanent approximation for matrices with mean μ = 1/polylog(n) in time 2n(ε), for arbitrarily small ε>0). The proposed algorithm significantly extends the regime of matrices for which efficient approximation of the permanent is known. This is because unlike previous algorithms which require a stringent correlation between the signs of the entries of the matrix [1], [2] it can tolerate random ensembles in which this correlation is negligible (albeit non-zero). Among important special cases we note: 1) Biased Gaussian: each entry is a complex Gaussian with unit variance 1 and mean μ. 2) Biased Bernoulli: each entry is -1 + μ with probability 1/2, and 1 with probability 1/2. These results counter the common intuition that the difficulty of computing the permanent, even approximately, stems merely from our inability to treat matrices with many opposing signs. The Gaussian ensemble approaches the threshold of a conjectured hardness [3] of computing the permanent of a zero mean Gaussian matrix. This conjecture is one of the baseline assumptions of the BosonSampling paradigm that has received vast attention in recent years in the context of quantum supremacy experiments. We furthermore show that the permanent of the biased Gaussian ensemble is #P-hard to compute exactly on average. To our knowledge, this is the first natural example of a counting problem that becomes easy only when average case analysis and approximation are combined. On a technical level, our approach stems from a recent approach taken by Barvinok [1], [4], [5], [6] who used Taylor series approximation of the logarithm of a certain univariate polynomial related to the permanent. Our main contribution is to introduce an average-case analysis of such related polynomials. We complement our approach with a new technique for iteratively computing a Taylor series approximation of a function that is analytical in the vicinity of a curve in the complex plane. This method can be viewed as a computational version of analytic continuation in complex analysis.
For standard factor graphs (S-FGs), i.e., factor graphs with local functions taking on non-negative real values, Vontobel gave a characterization of the Bethe approximation to the partition function in terms of the partition function of finite graph covers. The proof of that statement heavily relied on the method of types. In this paper we give a similar characterization for so-called double-edge factor graphs (DE-FGs), which are a class of factor graphs where local functions take on complex values and have to satisfy some positive semi-definiteness constraints. Such factor graphs are of interest in quantum information processing. In general, approximating the partition function of DE-FGs is more challenging than for S-FGs because the partition function is a sum of complex values and not just a sum of non-negative real values. In particular, for proving the above-mentioned characterization of the Bethe approximation in terms of finite graph covers, one cannot use the method of types anymore. We overcome this challenge by applying the loop-calculus transform by Chertkov and Chernyak, along with using the symmetricsubspace transform, a novel technique for factor graphs that should be of interest beyond proving the main result of this paper. Currently, the characterization of the Bethe approximation of the partition function of DE-FGs is for DE-FGs satisfying an (easily checkable) condition. However, based on numerical results, we suspect that the characterization holds more broadly.
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), where J is the all-ones matrix and W is a random matrix with independent mean-zero entries.
We show that when the entries of W are standard complex Gaussians, all zeros of the random polynomial per(zJ+W) lie within a disk of radius O~(n−1/3), which yields an approximation algorithm when the bias of the entries is Ω~(n−1/3). Previously, there were no efficient algorithms at biases smaller than 1/polylog(n), and it was unknown whether there typically exist zeros z with ∣z∣≥1. As a complementary result, we show that the bulk of the zeros, namely (1−ε)n of them, have magnitude 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 W with i.i.d. subexponential entries.