Quantum computing passes from the arrow of time: it moves better than classical computing in information processing, since it does not need to distinguish between cause and effect. The discovery suggests that our perception of time may arise by imposing a classical explanation on the events of the quantum world.
Quantum computing does not depend on the arrow of time to process information, has discovered an investigation of the Center for Quantum Technologies (CQT) of Singapore published in Physical Review X.
The international team of the CQT, Mile Gu, Jayne Thompson, Andrew Garner and Vlatko Vedral, and their collaborators, has proven that quantum computing does not need to distinguish between cause and effect in order to process information effectively, safely and quickly. This research is based on a discovery made nearly ten years ago by complexity scientists, James Crutchfield and John Mahoney, of the University of California, Davis. They showed that many statistical data sequences have a built-in time arrow. That means that the information follows a temporal sequence: an observer who sees the data reproduced from beginning to end, like the frames of a movie, can model what comes next using only a modest amount of memory about what happened before.
However, an observer who tries to model the system backwards, going from present to past, has to develop a much more difficult task that needs to process orders of information of greater magnitude. But it is not an impossible task. This discovery came to be known as “causal asymmetry”, one of the big surprises in predictive modeling, which uses statistics to predict results. According to the causal asymmetry, many information processing processes seem simpler in one temporal direction than in the other. In most cases, the event one wishes to predict is located in the future, but predictive modeling can be applied to any type of unknown event, regardless of when it occurred.
Jayne Thompson et al., 2018. Causal Asymmetry in a Quantum World. Phys. Rev. Vol. 8, Iss. 3, July – September 2018. DOI: 10.1103/PhysRevX.8.031013