We study approximate counting and sampling algorithms for the hard-core model on $Δ$-regular bipartite graphs under a spectral expansion condition. Let $M_G$ be the biadjacency matrix of $G$. For every fixed $ξ\in(0,1)$, we give an FPRAS for the hard-core partition function and an efficient approximate sampler whenever \[ λ\leq \frac{1-ξ}{σ_2(M_G)}. \] The main idea is to introduce a family of quadratic tilts in the left-right occupation imbalance and show that each tilted measure can be sampled efficiently using Glauber dynamics. A discrete Gaussian identity expresses the original hard-core model as an exact positive mixture of these tilted measures; truncation and simulated annealing then yield efficient counting and sampling algorithms. For the complementary high-fugacity regime, we refine the polymer-model approach and show that the required phase-dominance and cluster expansion conditions follow from the singular-spectrum bound alone. Combining the two regimes, we obtain efficient approximate counting and sampling at every fugacity $λ>0$ whenever \[ σ_2(M_G)\leq c\left(\frac{Δ^2}{\log(\mathrm eΔ)}\right)^{1/3} \] for an absolute constant $c>0$. In particular, this recovers all-fugacity algorithms for random $Δ$-regular bipartite graphs for all sufficiently large $Δ$, while providing an efficiently verifiable certificate of their success on a given instance.

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