Simulating Gaussian Boson Sampling with Tensor Networks in the Heisenberg picture
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916139003543552 |
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| author | Cilluffo, Dario Lorenzoni, Nicola Plenio, Martin B. |
| author_facet | Cilluffo, Dario Lorenzoni, Nicola Plenio, Martin B. |
| contents | Although the Schr{ö}dinger and Heisenberg pictures are equivalent formulations of quantum mechanics, simulations performed choosing one over the other can greatly impact the computational resources required to solve a problem. Here we demonstrate that in Gaussian boson sampling, a central problem in quantum computing, a good choice of representation can shift the boundary between feasible and infeasible numerical simulability. To achieve this, we introduce a novel method for computing the probability distribution of boson sampling based on the time evolution of tensor networks in the Heisenberg picture. In addition, we overcome limitations of existing methods enabling simulations of realistic setups affected by non-uniform photon losses. Our results demonstrate the effectiveness of the method and its potential to advance quantum computing research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_11215 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Simulating Gaussian Boson Sampling with Tensor Networks in the Heisenberg picture Cilluffo, Dario Lorenzoni, Nicola Plenio, Martin B. Quantum Physics Although the Schr{ö}dinger and Heisenberg pictures are equivalent formulations of quantum mechanics, simulations performed choosing one over the other can greatly impact the computational resources required to solve a problem. Here we demonstrate that in Gaussian boson sampling, a central problem in quantum computing, a good choice of representation can shift the boundary between feasible and infeasible numerical simulability. To achieve this, we introduce a novel method for computing the probability distribution of boson sampling based on the time evolution of tensor networks in the Heisenberg picture. In addition, we overcome limitations of existing methods enabling simulations of realistic setups affected by non-uniform photon losses. Our results demonstrate the effectiveness of the method and its potential to advance quantum computing research. |
| title | Simulating Gaussian Boson Sampling with Tensor Networks in the Heisenberg picture |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2305.11215 |