This paper is about the quantum

computing which is the new emerging field and which is applicable in the

Artificial Intelligence and QC can be used to solve issues which may occur in Artificial

Intelligence like search and pattern matching, so QC is the coming widely

useful field to solve many computation AI problems and thus it is the very

efficient approach to make potential applications of AI applicable in the

emerging world.

Basically

in quantum computation is the approach in which subatomic particles like

electron, photons behavior are used for computation and information processing.

Superposition and entanglement are two processes in the

quantum domain that provide a well-organized way to perform certain kinds of

computations than classical algorithmic methods. In QC information is stored in

quantum registers comprised of series of quantum bits (or qubits). QC defines a

set of operators called quantum gates that operate on quantum registers

performing simple qubit-range computations.

In

Quantum Computation the bit is called quantum bit or qubit and the energy state

of electron in an atom, also polarization of photon are the elements to

implement physically the quantum behavior. Qubit state is determined or observe

in order to obtain one or two distinct states in the form of |0> and |1>

which are analogs to 0 and 1 in classical bit .

Before the qubit is measured, its

state can be in a composition of its basis states denoted as:

In which a and b indicate complex

numbers called probability amplitudes|a|2 is the probability of the qubit to

appear in state |0> when observed, and |b|2 is the probability to appear in

state |1>. A series of qubits is called a quantum register.

In Quantum computation superposition indicates

that the states are not definite unless the quantum states are measured and

entangled means the values are link and we don’t need to know all states if

they are linked and thus sum of the sates values are not separate and thus they

are coherence.

Quantum Algorithms are also used which

are series of applications of quantum gates over the contents of a quantum

register.

One of the first contributions that QC

offers to AI is the production of truly random numbers. True randomness has

been report to cause measurable performance improvement to genetic programming

and other automatic program induction methods.

Critical points to

notice:-

·

The

main task is to find a way to encode the problem space within the quantum register

boundaries which is difficult in QC but some algorithms are defined to cope

with this problem.

·

Some

scientists argue that QC is not able to solve unpredictable problems like AI

question is that is QC is able to match human brain behavior AI is not

successful but QC helps a lot to access this kind of undefined problems with

some kind of solution with proper hardware and quantum algorithms.

·

It

includes extremely efficient algorithms and computations which may cause its

overhead and made it difficult to implement and as it uses energy states of

subatomic particles and QC consist of qubits so it is possible to disappear its

coherence soon and thus information to process may also can be lost. So this is

may be the drawback for Quantum computing for processing information.