Direct numerical simulation of turbulent flows at high Reynolds numbers remains a major challenge for traditional computational fluid dynamics (CFD) tools running on classical computer hardware. This has motivated growing interest in quantum algorithms for CFD to enable flow simulations on quantum computers. The reason being that these computers are expected to deliver potential speed-ups for certain problems. One promising quantum CFD approach is a fully quantum implementation of the lattice Boltzmann method called QLBM. Although efficient quantum routines are now available for the streaming step, implementing the nonlinear, irreversible collision step with a low depth circuit that avoids additional ancilla qubits, probabilistic post-selection and repeated executions remains a significant challenge. In this study, we address this challenge by introducing a framework for learning a surrogate quantum circuit (SQC) that approximates the full Bhatnagar Gross Krook (BGK) collision operator for the D2Q9 lattice. The four qubit circuit is trained to respect the physical properties of the BGK collision operator, including mass and momentum conservation, D8 equivariance and scale equivariance. When compiled to the gate set used by IBM Heron processor under the assumption of full qubit connectivity, the 15 block SQC requires only 2,430 native gates and uses neither ancilla qubits nor post-selection or repeated executions. Moreover, its depth is independent of the grid resolution, as collision is a local operation that can exploit quantum parallelism to its full extent. We validate the SQC on two benchmark flows, the Taylor Green vortex decay and the lid driven cavity, demonstrating that it accurately captures vortex dissipation and flow recirculation.