Discover how ‘tensor trains’, a mathematical representation, can revolutionize the understanding and simulation of evolving quantum systems.
Quantum systems are notoriously complex to describe, but tensor trains offer a breakthrough solution. In a recent publication in The European Physical Journal Special Topics, a team of researchers from France demonstrates how tensor trains can accurately capture and simulate the dynamics of quantum systems.
This groundbreaking research has significant implications for quantum computing, where maintaining quantum information is crucial. By reducing the interaction between qubits and their surroundings, tensor trains can help prolong the retention of quantum information.
With many variables at play in systems containing interacting quantum particles, understanding their dynamics is a daunting task. However, researchers have discovered that the overall properties of these systems often depend on a limited number of variables. This allows them to describe the system as a subsystem embedded in its environment.
Tensors, mathematical structures, are commonly used to represent and simulate these embedded subsystems. To simplify the descriptions further, tensor trains break down higher-order tensors into sums of lower-order tensors.
The team of researchers utilized the theoretical framework of ‘hierarchical equations of motion’ (HEOM) to implement the tensor train format. Their aim was to accurately describe the evolution and dynamics of quantum systems within their environments.
This survey conducted by the team is expected to become an invaluable resource for future researchers, providing accurate simulations for a wide range of evolving quantum systems.
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