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arXiv:2608.17065

Exact Moments of Gaussian Gram Hafnians Reveal an $n^2/\log n$ Threshold for Weak Anticoncentration

Hongru Zhao

quant-phmath.PRSubmitted Aug 17, 2026• Announced Aug 19, 2026• 0 citations
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Abstract

Anticoncentration is central to hardness arguments for approximate sampling. In the independent Gaussian surrogate for collision free Gaussian boson sampling, the moment ratio studied here also determines the averaged ideal linear cross entropy reference value. Let $H_{k,n}=\mathrm{haf}(X^{\mathsf T}X)$, where $X\in\mathbb{C}^{k\times 2n}$ has independent standard circular complex Gaussian entries. We evaluate $\mathbb{E}|H_{k,n}|^2$ and $\mathbb{E}|H_{k,n}|^4$ exactly by reducing four hafnian copies to a rank two Gaussian integral. For $R_{k,n}=(\mathbb{E}|H_{k,n}|^2)^2/\mathbb{E}|H_{k,n}|^4$, we obtain $R_{k,n}=4^{-n}\binom{2n}{n}/F_{k,n}$, where $F_{k,n}={}3F_2(-n,-n,1/2;1,k/2;1)$ is a terminating generalized hypergeometric polynomial. If $k/n^2\to c>0$, then $F{k,n}\to e^{1/c}I_0(1/c)$, where $I_0$ is the modified Bessel function of the first kind of order zero, and consequently $R_{k,n}\sqrt{πn}\to[e^{1/c}I_0(1/c)]^{-1}$. Thus $k\asymp n^2$ is a smooth Bessel crossover, whereas the scaling order boundary for inverse polynomial weak anticoncentration is $k\asymp n^2/\log n$. These conclusions concern the Gaussian surrogate moment criterion; finite dimensional Haar moment transfer and high probability small ball anticoncentration remain separate problems.

Comments
15 pages, 1 figure. Includes the main paper, End Matter, and Supplemental Material as appendices. An accompanying Lean 4 verification package is available from Zenodo at https://doi.org/10.5281/zenodo.21959946

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Exact Moments of Gaussian Gram Hafnians Reveal an n2/log⁡nn^2/\log nn2/logn Threshold for Weak Anticoncentration
Anticoncentration is central to hardness arguments for approximate sampling. In the independent Gaussian surrogate for collision free Gaussian boson sampling, the moment ratio studied here also determines the averaged ideal linear cross entropy reference value. Let Hk,n=haf(XTX)H_{k,n}=\mathrm{haf}(X^{\mathsf T}X)Hk,n​=haf(XTX), where X∈Ck×2nX\in\mathbb{C}^{k\times 2n}X∈Ck×2n has independent standard circular complex Gaussian entries. We evaluate E∣Hk,n∣2\mathbb{E}|H_{k,n}|^2E∣Hk,n​∣2 and E∣Hk,n∣4\mathbb{E}|H_{k,n}|^4E∣Hk,n​∣4 exactly by reducing four hafnian copies to a rank two Gaussian integral. For Rk,n=(E∣Hk,n∣2)2/E∣Hk,n∣4R_{k,n}=(\mathbb{E}|H_{k,n}|^2)^2/\mathbb{E}|H_{k,n}|^4Rk,n​=(E∣Hk,n​∣2)2/E∣Hk,n​∣4, we obtain Rk,n=4−n(2nn)/Fk,nR_{k,n}=4^{-n}\binom{2n}{n}/F_{k,n}Rk,n​=4−n(n2n​)/Fk,n​, where Fk,n=3F2(−n,−n,1/2;1,k/2;1)F_{k,n}={}3F_2(-n,-n,1/2;1,k/2;1)Fk,n​=3F2​(−n,−n,1/2;1,k/2;1) is a terminating generalized hypergeometric polynomial. If k/n2→c>0k/n^2\to c>0k/n2→c>0, then Fk,n→e1/cI0(1/c)F{k,n}\to e^{1/c}I_0(1/c)Fk,n→e1/cI0​(1/c), where I0I_0I0​ is the modified Bessel function of the first kind of order zero, and consequently Rk,nπn→[e1/cI0(1/c)]−1R_{k,n}\sqrt{πn}\to[e^{1/c}I_0(1/c)]^{-1}Rk,n​πn​→[e1/cI0​(1/c)]−1. Thus k≍n2k\asymp n^2k≍n2 is a smooth Bessel crossover, whereas the scaling order boundary for inverse polynomial weak anticoncentration is k≍n2/log⁡nk\asymp n^2/\log nk≍n2/logn. These conclusions concern the Gaussian surrogate moment criterion; finite dimensional Haar moment transfer and high probability small ball anticoncentration remain separate problems.
Gaussian Boson Sampling is a popular method for experimental demonstrations of quantum advantage, but many subtleties remain in fully understanding its theoretical underpinnings. An important component in the theoretical arguments for approximate average-case hardness of sampling is anticoncentration, which is a second-moment property of the output probabilities. In Gaussian Boson Sampling these are given by hafnians of generalized circular orthogonal ensemble matrices. In a companion work [arXiv:2312.08433], we develop a graph-theoretic method to study these moments and use it to identify a transition in anticoncentration. In this work, we find a recursive expression for the second moment using these graph-theoretic techniques. While we have not been able to solve this recursion by hand, we are able to solve it numerically exactly, which we do up to Fock sector 2n=802n = 802n=80. We further derive new analytical results about the second moment. These results allow us to pinpoint the transition in anticoncentration and furthermore yield the expected linear cross-entropy benchmarking score for an ideal (error-free) device.
Local anticoncentration controls the conversion of additive output probability estimates to relative estimates in Gaussian boson sampling. We prove a center-uniform quadratic small-ball bound for hafnians of complex Gaussian transpose Gram matrices for 2n2n2n selected modes, with an explicit finite coefficient that is polynomial when the number of Gaussian rows is at least of order n2/log⁡nn^2/\log nn2/logn. Letting the Gaussian row count tend to infinity yields anticoncentration for the independent complex symmetric Gaussian ensemble. For the transpose Gram hafnian bound, the proof compresses dependent hafnian cofactors by Fourier interpolation and controls their self-coupled variance through a conditional Wishart identity that preserves the transpose Gram coordinate. Independent perturbations satisfy related bounds, and a positive mixture density shows that the local quadratic power is exact. Together with the uniform hiding theorem in the companion paper, the result gives local anticoncentration for finite Haar interferometers.
Anticoncentration and entanglement are two properties used to study classical hardness or easiness of Gaussian boson sampling in varying regimes. In this paper, we consider three directions concerning anticoncentration and entanglement in Gaussian boson sampling: (1) We derive closed-form expressions for the second moment of hafnians of symmetric Gaussian products, and use this to precisely locate the anticoncentration transition as a function of the number of squeezed input modes. (2) We derive closed-form expressions for the Rényi-ααα Page curves for Gaussian boson sampling. (3) We study unequal input squeezing parameters (si)i(s_i)_i(si​)i​, and prove estimates as well as monotonicity of the average-case Rényi-2 entropy Page curve in terms of the magnitudes ∣si∣|s_i|∣si​∣, which allow for extending equal squeezing results to unequal squeezing.
We prove a uniform shifted anticoncentration theorem for the hafnian of a real Gaussian Gram matrix under an explicit condition on the row dimension. After normalization by its root mean square, the law has a bounded continuous density, maximal at zero, and every interval has probability bounded by an explicit coefficient times its radius. Under suitable growth conditions on the row dimension, this coefficient grows at most polynomially in the hafnian order, meaning half the dimension of the Gram matrix. We also compute the exact second moment. The proof exploits the perfect matching structure, combining conditional Gaussian representations, row suspension, and bilinear interpolation to control an inverse moment of the conditional variance. At fixed hafnian order, a rescaled limit as the row dimension grows yields corresponding bounds for the hafnian of a real symmetric Gaussian matrix with independent entries above the diagonal.
Let G2nG_{2n}G2n​ be a complex symmetric random matrix whose entries above the diagonal are independent standard circular complex Gaussians, and let Hn=haf⁡(G2n)H_n=\operatorname{haf}(G_{2n})Hn​=haf(G2n​). We prove the uniform shifted anticoncentration bound Pr⁡ ⁣(∣Hn(2n−1)!!−z∣≤ε)≤2nπ ε2 \Pr\!\left( \left| \frac{H_n}{\sqrt{(2n-1)!!}}-z \right| \le \varepsilon \right) \le 2\sqrt{\frac nπ}\,\varepsilon^2 Pr(​(2n−1)!!​Hn​​−z​≤ε)≤2πn​​ε2 for every z∈Cz\in\mathbb Cz∈C and ε>0\varepsilon>0ε>0. This establishes a local anticoncentration property that supports hardness arguments for quantum advantage in Gaussian boson sampling.
Sampling problems give some of the clearest conditional separations between quantum and classical computation. In Fermion Sampling, passive linear optics acts on a non-Gaussian magic input. Globally Haar-random transformations give anticoncentration and high-precision average-case hardness, but require linear depth and quadratically many gates. A natural open question is whether linear depth is necessary? We show that logarithmic depth suffices for both guarantees in the same ensemble. For fresh uniform matchings with independent Haar two-mode gates, the collision reaches the passive-Haar scale at a sharp threshold of log⁡n/log⁡(9/4)≈0.855log⁡2n\log n/\log(9/4)\approx0.855\log_2 nlogn/log(9/4)≈0.855log2​n layers, with an explicit limiting transition profile and a matching lower bound from two-particle correlations. The mechanism is input-dependent: the magic input suppresses the extensive weight of the slowest relaxation mode, leaving the next to set the scale. An occupation-basis input retains large collision even under full Haar randomness. At a larger logarithmic depth, we prove, in the real-RAM model, average-case #P\#\mathsf{P}#P-hardness of estimating output probabilities to additive error 2−O(nlog⁡2n)2^{-O(n\log^2 n)}2−O(nlog2n) on any fixed fraction of instances above 3/43/43/4. We construct hard instances in four native layers, embed them in typical schedules, and interpolate along Cayley paths using a rational linear-program decoder. A finite 192192192-gate alphabet preserves the collision law exactly. Both guarantees use logarithmic native fermionic depth between arbitrary mode pairs and O(nlog⁡n)O(n\log n)O(nlogn) gates, replacing the global-Haar construction's quadratic budget. The accuracy proved is finer than the 1/N1/N1/N scale, where N=(nn/2)N=\binom{n}{n/2}N=(n/2n​), required by sampling-to-counting; hardness of sampling within constant total-variation distance remains open.