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Quantum Models Efficiently Predict Behavior of Complex Systems

Researchers from the University of Bristol have demonstrated that quantum computer may need lesser memory for simulations of reality when compared to a classical computer.

A simulator for a stochastic process can be thought of a physical system that stores select information about past outputs, and uses them to generate the require statistics for the future

Dr Karoline Wiesner from the School of Mathematics and Centre for Complexity Sciences and scientists at the Centre for Quantum Technologies in Singapore conducted the study, which shows a new way wherein quantum physics-based computers could perform better than classical computer. The study results have been reported in Nature Communications.

A typical approach to deal with a complex system is to determine its basics, which are then defined as basic principles and natural laws. However, this technique does not work with many complex systems. Scientists have found that complexity of a complex system can be reduced if they agree to exploit quantum physics. A complex system’s quantum model is simpler and envisages the behavior of the system more effectively when compared to classical models.

Predictability of a particular process or system is a measure of its complexity. For instance, the complexity of a coin toss is zero. On the other hand, protein conformational dynamics and neural spike sequences are more complex systems than a coin toss. They have memory and can be predicted to some extent. A simulation of reality is the basis of the function of these complex systems in numerous organisms. This simulation enables the organism to envisage and thus respond to the surroundings. Conversely, complexity of such systems can be lowered if they exploit quantum dynamics to facilitate identical predictions with lesser memory.

Dr Wiesner explained that the researchers have discovered that on a basic level, prediction efficiency will not reach the lower level provided by the thermodynamics principles, thus indicating a chance for improvement. It is not possible to reversibly model some observable statistics with ideal efficiency.



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