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.

 

 

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