troduced by Head, based on splicing (a
combination of cut-and-paste operations achievable by enzymes), predates
the experimental proof-of-principle of
DNA computing by almost 10 years.
Subsequently, studies on the computational power of such models proved
that various subsets of bio-operations
can achieve the computational power
of a Turing machine, showing thus that
molecular computers are in principle
possible. 27 Overall, molecular computing has created many novel theoretical
questions, and has considerably enriched the theory of computation.
Quantum Computing is another paradigm that uses an alternative “
hardware” for performing computations. 19
Already in 1980 Benioff introduced
simulations of classical Turing Machines on quantum mechanical systems. However the idea of a quantum
computer that would run according
to the laws of quantum physics and
operate exponentially faster than a
deterministic electronic computer to
simulate physics, was first suggested
by Feynman in 1982. Subsequently,
Deutsch introduced a formal model
of quantum computing using a Turing
machine formalism, and described a
universal quantum computer.
A quantum computer uses distinctively quantum mechanical phenomena, such as superposition and entanglement, to perform operations on
data stored as quantum bits (qubits).
A qubit can hold a 1, a 0, or a quantum
superposition of these. A quantum
computer operates by manipulating
those qubits with quantum logic gates.
The notion of information is different
when studied at the quantum level. For
instance, quantum information cannot
be measured reliably, and any attempt
at measuring it entails an unavoidable
and irreversible disturbance.
The 1980s saw an abundance of
research in quantum information
processing, such as applications to
quantum cryptography which, unlike
its classical counterpart, is not usually based on the complexity of computation but on the special properties
of quantum information. Recently an
open air experiment was reported in
quantum cryptography (not involving optical cable) over a distance of
144km, conducted between two Canary islands.
it is indeed
believed that one
of the possible
contributions of
computer science
to biology could be
the development of
a suitable language
to accurately and
succinctly describe,
and reason about,
biological concepts
and phenomena.
The theoretical results that catapulted quantum computing to the
forefront of computing research were
Shor’s quantum algorithms for factoring integers and extracting discrete logarithms in polynomial time, obtained
in 1994—the same year that saw the
first DNA computing experiment by
Adleman. A problem where quantum
computers were shown to have a quadratic time advantage when compared
to classical computers is quantum database search that can be solved by Grover’s algorithm. Possible applications
of Shor’s algorithm include breaking
RSA exponentially faster than an electronic computer. This joined other exciting applications, such as quantum
teleportation (a technique that transfers a quantum state, but not matter
or energy, to an arbitrarily distant location), in sustaining the general interest
in quantum information processing.
So far, the theory of quantum computing has been far more developed
than the practice. Practical quantum
computations use a variety of implementation methods such as ion-traps,
superconductors, nuclear magnetic
resonance techniques, to name just a
few. To date, the largest quantum computing experiment uses liquid state
nuclear magnetic resonance quantum
information processors that can operate on up to 12 qubits.
nature as computation
The preceding sections describe research on the theory, applications and
experimental implementations of na-ture-inspired computational models
and techniques. A dual direction of research in natural computing is one in
which the main goal becomes understanding nature by viewing processes
that take place in nature as information processing.
This dual aspect can be seen in
systems biology, and especially in
computational systems biology, wherein the
adjective “computational” has two
meanings. On one hand it means the
use of quantitative algorithms for computations, or simulations that complement experiments in hypothesis generation and validation. On the other
hand, it means a qualitative approach
that investigates processes taking place
in cells through the prism of communications and interactions, and thus of