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Although the Schr\"odinger and Heisenberg pictures are two equivalentformulations of quantum mechanics, simulations performed choosing one over theother can greatly impact the computational resources required to solve aproblem. Here we demonstrate that in Gaussian boson sampling, a central problemin quantum computing, a good choice of representation can shift the boundarybetween feasible and infeasible numerical simulability. To achieve this, weintroduce a novel method for computing the probability distribution of bosonsampling based on the time evolution of tensor networks in the Heisenbergpicture. This approach overcomes limitations of existing methods and enables,for example, simulations of realistic setups affected by non-uniform photonlosses. Our results demonstrate the effectiveness of the method and itspotential to advance quantum computing research.

Simulating Gaussian Boson Sampling with Tensor Networks in the Heisenberg picture