We present an approach to reduce the computational complexity and storage pertaining to quantum nuclear wave functions and potential energy surfaces. The method utilizes tensor networks implemented through sequential singular value decompositions. Two specific forms of tensor networks are considered to adaptively compress the data in multidimensional quantum nuclear wave functions and potential energy surfaces. In one case the well-known matrix product state approximation is used whereas in another case the wave function and potential energy surface space is initially partitioned into "system" and "bath" degrees of freedom through singular value decomposition, following which the individual system and bath tensors (wave functions and potentials) are in turn decomposed as matrix product states. We postulate that this leads to a mean-field version of the well-known projectionally entangled pair state known in the tensor networks community. Both formulations appear as special cases of more general higher order singular value decompositions known in the mathematics literature as Tucker decomposition. The networks are then used to study the hydrogen transfer step in the oxidation of isoprene by peroxy and hydroxy radicals. We find that both networks are extremely efficient in accurately representing quantum nuclear eigenstates and potential energy surfaces and in computing inner products between quantum nuclear eigenstates and a final-state basis to yield product side probabilities. We also present formal protocols that will be useful to perform explicit quantum nuclear dynamics.