Journal
PHYSICAL REVIEW A
Volume 102, Issue 6, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.102.062414
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Funding
- National Science Foundation [NSF PHY-1748958]
- National Natural Science Foundation of China [11805279]
- Ministry of Education of Singapore AcRF MOE Tier-II [MOE2018-T2-2-142]
- Australian Research Council Future Fellowship [FT160100073]
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We show how a tensor-network-based machine learning algorithm can learn the structures of generic, non-Markovian, quantum stochastic processes. First, a process is represented as a matrix product operator (MPO) and trained with a database of local input states at different times and the corresponding time-nonlocal output state. We then apply the algorithm to predict the output state of a process at different times for a system that is coupled to a spin-chain environment. We then reconstruct the full process, and we quantify the non-Markovian memory by means of the bond dimension of the MPO for various properties of the system, of the environment, and of their interaction. Our study paves the way for a possible experimental investigation into the process tensor and its properties, and an effective characterization of noise in quantum devices.
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