Quantum Superposition - Theory - Quantum Mechanics in Imaginary Time

Quantum Mechanics in Imaginary Time

The analogy between quantum mechanics and probability is very strong, so that there are many mathematical links between them. In a statistical system in discrete time, t=1,2,3, described by a transition matrix for one time step, the probability to go between two points after a finite number of time steps can be represented as a sum over all paths of the probability of taking each path:

K_{x\rightarrow y}(T) = \sum_{x(t)} \prod_t K_{x(t)x(t+1)} \,

where the sum extends over all paths with the property that and . The analogous expression in quantum mechanics is the path integral.

A generic transition matrix in probability has a stationary distribution, which is the eventual probability to be found at any point no matter what the starting point. If there is a nonzero probability for any two paths to reach the same point at the same time, this stationary distribution does not depend on the initial conditions. In probability theory, the probability m for the stochastic matrix obeys detailed balance when the stationary distribution has the property:

\rho_n K_{n\rightarrow m} = \rho_m K_{m\rightarrow n} \,

Detailed balance says that the total probability of going from m to n in the stationary distribution, which is the probability of starting at m times the probability of hopping from m to n, is equal to the probability of going from n to m, so that the total back-and-forth flow of probability in equilibrium is zero along any hop. The condition is automatically satisfied when n=m, so it has the same form when written as a condition for the transition-probability R matrix.

\rho_n R_{n\rightarrow m} = \rho_m R_{m\rightarrow n} \,

When the R matrix obeys detailed balance, the scale of the probabilities can be redefined using the stationary distribution so that they no longer sum to 1:

p'_n = \sqrt{\rho_n}\;p_n \,

In the new coordinates, the R matrix is rescaled as follows:

\sqrt{\rho_n} R_{n\rightarrow m} {1\over \sqrt{\rho_m}} = H_{nm} \,

and H is symmetric

H_{nm} = H_{mn} \,

This matrix H defines a quantum mechanical system:

i{d \over dt} \psi_n = \sum H_{nm} \psi_m \,

whose Hamiltonian has the same eigenvalues as those of the R matrix of the statistical system. The eigenvectors are the same too, except expressed in the rescaled basis. The stationary distribution of the statistical system is the ground state of the Hamiltonian and it has energy exactly zero, while all the other energies are positive. If H is exponentiated to find the U matrix:

U(t) = e^{-iHt} \,

and t is allowed to take on complex values, the K' matrix is found by taking time imaginary.

K'(t) = e^{-Ht} \,

For quantum systems which are invariant under time reversal the Hamiltonian can be made real and symmetric, so that the action of time-reversal on the wave-function is just complex conjugation. If such a Hamiltonian has a unique lowest energy state with a positive real wave-function, as it often does for physical reasons, it is connected to a stochastic system in imaginary time. This relationship between stochastic systems and quantum systems sheds much light on supersymmetry.

Read more about this topic:  Quantum Superposition, Theory

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