We obtain results on small deviations of Bogoliubov's Gaussian measure occurring in the theory of the statistical equilibrium of quantum systems. For some random processes related to Bogoliubov processes, we find the exact asymptotic probability of their small deviations with respect to a Hilbert norm.The Bogoliubov measure, introduced in [1], [2], plays an important role in the theory of the statistical equilibrium of quantum systems. It occurs in representing Gibbs equilibrium means of Bose operators in the form of functional integrals using Bogoliubov's method of T -products (see [3]).We define the Bogoliubov process ξ(t), t ∈ [0, 1], as a Gaussian process with a zero mean and the covarianceIt follows from the general results of the theory of random processes that trajectories of a Bogoliubov process are almost certainly (a.c.) continuous. From definition (1), we obtain E ξ(1) − ξ(0) 2 = 0 and hencealmost all the trajectories of a Bogoliubov process belong to the space C 0 [0, 1] of continuous functions x(t) defined on the segment [0, 1] and satisfying the condition x(0) = x(1). The distribution of the process ξ(t) in the space C 0 [0, 1] endowed with a uniform metric is called the Bogoliubov measure μ B . Properties of the Bogoliubov measure, of functional integrals over this measure, and of the Bogoliubov process trajectories were studied in [2], [4], [5]. Approximate formulas constructed in [2] for functional integrals over a Bogoliubov measure are exact for functional polynomials. It was established in [4]that similarly to the case of Wiener processes, almost all trajectories of a Bogoliubov process do not satisfy the Hölder condition with the index γ > 1/2 and are therefore nondifferentiable. Some asymptotic formulas for functional integrals with respect to a Bogoliubov measure were obtained in [5], where the asymptotic form of the probability of large deviations of Bogoliubov processes in L p -norms, which is important in many problems in probability theory and mathematical physics, was also derived.Another fundamental characteristic of random processes (used, e.g., to estimate the accuracy of approximations of random process and to calculate the metric entropy of different sets) is the asymptotic form of their small deviations in one norm or another. The theory of small deviations for the norm of Gaussian processes has been rapidly developing in the last decade (see, e.g., [6], [7]). A connection was recently established between small deviations and problems in mathematical statistics: functional analysis of data and nonparametric Bayes estimates [8]- [10].In the majority of cases, it is possible to find a rough (logarithmic) asymptotic expression for the probabilities of small deviations for Gaussian processes. Calculating the exact asymptotic form is a much more complicated problem, whose solution is known for only a few processes [11]-[16].