The purpose of this research is to find new ways to solve problems. This kind of solving problems is also called computation with timedelays.
One of the most popular implementation uses light (other signals can be used also) for solving NPComplete problems.
Here are some properties of light useful for our system:
 light has a limited speed, and thus we can delay it
 due to the massive parallelism, light rays can be divided into 2
(sub)rays of smaller intensity.
Basic ideas:

Optical computing devices are very simple having a graphlike structure.
Each device usually has a start node (where the light/signal enters) and a
destination node (where the light/signal is expected to come out).

The light
is marked in each node or in each arc so that it can be easily
identified at the destination node.

The marking operation can be
implemented by delaying the light by a certain amount of time. A delay
can be obtained by forcing the ray to pass through a cable of a given
length. Sometime this way to solve problems is also called computations with timedelays.

Later, each ray of light is divided into several small rays
which are sent to the outgoing links. This operation can be implemented
by using several beamsplitters (half silvered mirrors).

At the destination node we will
search for a particular ray which have a particular property required by
the problem. This operation can be done easily due to the special
properties of the system which delays the rays passing through a node.
Problems that have been solved by using this idea:
Strengths
On some problems it can be faster than digital computers.
Weaknesses
The required amount of energy is exponential.
Note that this difficulty is not specific to this system only. Other major
unconventional computation paradigms, trying to solve NPcomplete
problems share the same fate. For instance, a quantity of DNA equal to
the mass of Earth is required to solve Hamiltonian Path problem with 200
cities using DNA computers
