Project Abstract

Quantum and probabilistic automata play a crucial role in the simulation and analysis of stochastic and quantum processes. Developing a tool for reachability analysis would greatly enhance the capabilities of researchers and engineers who conduct reachability analysis for in both probabilistic and quantum domain.Why the Project is Worthwhile:?�� Solvers capable of addressing reachability problems have wide-ranging practical applications, from optimizing algorithms for quantum computing to enhancing security protocols in cryptographic systems and complex financial systems. ?�� In financial models, solvers serve as analytical tools that determine the stability of financial states by analysing how different conditions lead to stability or cause volatility. This capability is crucial for predicting how markets might behave under various scenarios, such as economic downturns or spikes in market demand. ?�� As we stand on the cusp of the quantum computing era, tools that can navigate both classical and quantum computational models are invaluable. This project not only addresses current computational challenges but also lays the groundwork for future advancements in quantum computing.Who Benefits:?�� Academic Researchers: Especially those in fields like computer science, quantum computing, and applied mathematics, who can use the tool to validate theoretical models and develop new algorithms.?�� Industry Practitioners: Professionals in technology and engineering can utilize the solver to design and verify systems where probabilistic states or quantum behaviors are significant, such as in physics, economics, finance etc.The solver will consist of two main components ?�� one for dealing with probabilistic automata and another for quantum automata. Each component will be capable of determining the reachability of specific states or sets of states given a set of initial conditions and inputs, under bounded conditions initially, with an aim to extend to unbounded scenarios.The first phase of the implementation will focus on solving probabilistic automatons. The user would specify an initial state, final state and a probability threshold to reach the final state. The solver then output if the automaton can reach the final state with the probability threshold defined by the user in a yes/no format. It would also introduce the feature which would allow the user to input the whole automaton, definition of its states, transitions, and associated probabilities in a graphical way. The second phase of the implementation will allow users to see the probability distribution of all states from a specified initial state. This enhancement will allow users to not only determine the feasibility of reaching a particular state but also to understand the overall behaviour and dynamics of the automaton under various conditions.The third phase of the implementation will extend the capabilities of the solver to the quantum scenarios. It will check for unbounded conditions in both probabilistic and quantum automata. examining scenarios where the automaton operates indefinitely or over an undefined time span. And come up with algorithms to handle unbounded cases.

Keywords: Automta, Quantum, Markov Decision Process

 Conference Details

Session: Presentation Stream 6 at Presentation Slot 5

Location: GH014 at Tuesday 7th 13:30 – 17:00

Markers: Jay Morgan, Trang Doan

Course: MSc Computer Science, Masters PG

Future Plans: I’m undecided