Some Numerical Methods And Analysis of A Class of Quantum Stochastic Differential Equations

The classical stochastic differential equation given as (t, ) = a (t, X) \, dt + b (t, X) \, dQ (t), X (t₀) = X₀, t [t₀, T, \] where the driving process \ (Q (t) \) is a martingale, and \ (a \) and \ (b \) are adapted functionals has several numerical schemes developed for it, where each exhibits distinct stability and convergence properties depending on the nature of the driving process and the functional space of the solution. A generalization of this equation is the quantum stochastic differential equation introduced by Hudson and Parthasarathy, expressed as (t) = E (t, X (t) ) \, d_ (t) + F (t, X (t) ) \, dAf^ (t) + G (t, X (t) ) \, dAg (t) + H (t, X (t) ) \, dt, \ where \ (E, F, G, \) and \ (H \) are stochastic operator-valued functions defined on a Hilbert space. In 1992, Ekhaquere reformulated this equation in an equivalent form involving the non-classical differential equation stated as; dt, X (t) = P (t, X (t) ), , X (t₀) = X₀, t [t₀, T, \] which represents an ordinary differential equation of non-classical type. The stochastic process \ (X (t) \) acts as a densely defined linear operator on a tensor product of two Hilbert spaces, one of which is the Boson Fock space. The vectors \ (\) and \ (\) belong to a dense subset of this tensor product Hilbert space, and the mapping \ (, ) P (t, X) (, ) \ defines a sesquilinear form for each fixed pair \ ( (t, X) \). We analyze the numerical approximation of the QSDE using its equivalent inner-product form. The numerical investigation employs three principal methods: the Lagrangian Quadrature Method, the Linear Multistep Scheme, and the Newton–Cotes Method. Each method is carefully designed to ensure the consistency, stability, and convergence of the approximate solution \ (X (t) \) toward the exact stochastic process. These schemes are implemented using Python programming language, which provides a flexible and efficient computational environment for testing the proposed algorithms. Our computational results show that the developed schemes generalize Taylor-type approximations of Itô processes under weak convergence criteria. The proposed algorithms exhibit numerical stability and yield results that compare favorably with analytical benchmarks. Furthermore, the implementation avoids direct approximation of the driving quantum processes, thereby preserving the natural adaptation of the schemes to quantum dynamical models.