Importance of NITheCS for science and engineering underlined
What is probably the first paper with a National Institute for Theoretical and Computational Sciences (NITheCS) affiliation has been published under the title ‘Quantum-enhanced analysis of discrete stochastic processes.’ The authors are Carsten Blank, Daniel K. Park and, fittingly, also the Interim Director of the NITheCS, Francesco Petruccione.
Prof Petruccione comments: ‘We are proud to publish under the NITheCS affiliation and encourage our associates also to use the affiliation in future. The Institute is still young, but it builds on the work done and ties formed by its predecessor organisation, the National Institute for Theoretical Physics (NITheP).
‘With the incorporation of more disciplines, especially the computational sciences, we are positioned exactly where international scientific attention is currently focused. Among others, work on quantum computational science is opening many doors for researchers everywhere. While one obvious task for a quantum computer is to simulate quantum mechanical behaviours of nature, quantum simulation of probability distributions and stochastic processes has gained attention recently.’
Physical processes on quantum computers
The paper relates to the simulation of physical processes on quantum computers. This has many facets, and recent developments improving the use of this technology have benefits for science as well as engineering applications.
Prof Petruccione continues: ‘The framework developed by our work in the paper ‘opens tremendous and extensive opportunities in physics, biology, epidemiology, hydrology, engineering, and finance.’
With his co-authors he points out in the paper that ‘since quantum mechanics can be viewed as a mathematical generalisation of probability theory, where non-negative real-valued probabilities are replaced by complex-valued probability amplitudes, quantum computing appears to be a natural tool for simulating classical probabilistic processes.’
The discrete stochastic processes (DSP) instrumental to model the dynamics of probabilistic systems have a wide spectrum of applications in science and engineering.
‘We propose a quantum algorithm for calculating the characteristic function of a DSP, which completely defines its probability distribution, using the number of quantum circuit elements that grows only linearly with the number of time steps. The quantum algorithm reduces the Monte-Carlo sampling to a Bernoulli trial while taking all stochastic trajectories into account. This approach guarantees the optimal variance without the need for importance sampling.’
The full paper can be found here.