Time-dependent variational Monte Carlo explained

The time-dependent variational Monte Carlo (t-VMC) method is a quantum Monte Carlo approach to study the dynamics of closed, non-relativistic quantum systems in the context of the quantum many-body problem. It is an extension of the variational Monte Carlo method, in which a time-dependent pure quantum state is encoded by some variational wave function, generally parametrized as

\Psi(X,t)=\exp\left(\sumkak(t)Ok(X)\right)

where the complex-valued

ak(t)

are time-dependent variational parameters,

X

denotes a many-body configuration and

Ok(X)

are time-independent operators that define the specific ansatz. The time evolution of the parameters

ak(t)

can be found upon imposing a variational principle to the wave function. In particular one can show that the optimal parameters for the evolution satisfy at each time the equation of motion

i

\sum
k\prime

\langleOk

O
k\prime
c
\rangle
t
a
k\prime

=\langleOk

c,
l{H}\rangle
t

where

l{H}

is the Hamiltonian of the system,

\langleAB

c=\langle
\rangle
t

AB\ranglet-\langleA\ranglet\langleB\ranglet

are connected averages, and the quantum expectation values are taken over the time-dependent variational wave function, i.e.,

\langle\ranglet\equiv\langle\Psi(t)||\Psi(t)\rangle

.

In analogy with the Variational Monte Carlo approach and following the Monte Carlo method for evaluating integrals, we can interpret

|\Psi(X,t)|2
\int|\Psi(X,t)|2dX

as a probability distribution function over the multi-dimensional space spanned by the many-body configurations

X

. The Metropolis–Hastings algorithm is then used to sample exactly from this probability distribution and, at each time

t

, the quantities entering the equation of motion are evaluated as statistical averages over the sampled configurations. The trajectories

a(t)

of the variational parameters are then found upon numerical integration of the associated differential equation.

References