Quantum computing is computing using quantum-mechanical
phenomena, such as superposition and entanglement.
A quantum computer is a device that performs
quantum computing. Such a computer is different
from binary digital electronic computers based
on transistors. Whereas common digital computing
requires that the data be encoded into binary
digits (bits), each of which is always in
one of two definite states (0 or 1), quantum
computation uses quantum bits or qubits, which
can be in superpositions of states. A quantum
Turing machine is a theoretical model of such
a computer and is also known as the universal
quantum computer. The field of quantum computing
was initiated by the work of Paul Benioff
and Yuri Manin in 1980, Richard Feynman in
1982, and David Deutsch in 1985.As of 2018,
the development of actual quantum computers
is still in its infancy, but experiments have
been carried out in which quantum computational
operations were executed on a very small number
of quantum bits. Both practical and theoretical
research continues, and many national governments
and military agencies are funding quantum
computing research in additional effort to
develop quantum computers for civilian, business,
trade, environmental and national security
purposes, such as cryptanalysis. Noisy devices
with a small number of qubits have been developed
by a number of companies, including IBM, Intel,
and Google. IBM has made 5-qubit and 16-qubit
quantum computing devices available to the
public for experiments via the cloud on the
IBM Q Experience. D-Wave Systems has been
developing their own version of a quantum
computer that uses annealing.Large-scale quantum
computers would theoretically be able to solve
certain problems much more quickly than any
classical computers that use even the best
currently known algorithms, like integer factorization
using Shor's algorithm (which is a quantum
algorithm) and the simulation of quantum many-body
systems. There exist quantum algorithms, such
as Simon's algorithm, that run faster than
any possible probabilistic classical algorithm.
A classical computer could in principle (with
exponential resources) simulate a quantum
algorithm, as quantum computation does not
violate the Church–Turing thesis. On the
other hand, quantum computers may be able
to efficiently solve problems which are not
practically feasible on classical computers.
== Basics ==
A classical computer has a memory made up
of bits, where each bit is represented by
either a one or a zero. A quantum computer,
on the other hand, maintains a sequence of
qubits, which can represent a one, a zero,
or any quantum superposition of those two
qubit states; a pair of qubits can be in any
quantum superposition of 4 states, and three
qubits in any superposition of 8 states. In
general, a quantum computer with
n
{\displaystyle n}
qubits can be in an arbitrary superposition
of up to
2
n
{\displaystyle 2^{n}}
different states simultaneously. (This compares
to a normal computer that can only be in one
of these
2
n
{\displaystyle 2^{n}}
states at any one time).
A quantum computer operates on its qubits
using quantum gates and measurement (which
also alters the observed state). An algorithm
is composed of a fixed sequence of quantum
logic gates and a problem is encoded by setting
the initial values of the qubits, similar
to how a classical computer works. The calculation
usually ends with a measurement, collapsing
the system of qubits into one of the
2
n
{\displaystyle 2^{n}}
eigenstates, where each qubit is zero or one,
decomposing into a classical state. The outcome
can, therefore, be at most
n
{\displaystyle n}
classical bits of information (or, if the
algorithm did not end with a measurement,
the result is an unobserved quantum state).
Quantum algorithms are often probabilistic,
in that they provide the correct solution
only with a certain known probability. Note
that the term non-deterministic computing
must not be used in that case to mean probabilistic
(computing) because the term non-deterministic
has a different meaning in computer science.
An example of an implementation of qubits
of a quantum computer could start with the
use of particles with two spin states: "down"
and "up" (typically written
|
↓
⟩
{\displaystyle |{\downarrow }\rangle }
and
|
↑
⟩
{\displaystyle |{\uparrow }\rangle }
, or
|
0
⟩
{\displaystyle |0{\rangle }}
and
|
1
⟩
{\displaystyle |1{\rangle }}
). This is true because any such system can
be mapped onto an effective spin-1/2 system.
== Principles of operation ==
A quantum computer with a given number of
qubits is fundamentally different from a classical
computer composed of the same number of classical
bits. For example, representing the state
of an n-qubit system on a classical computer
requires the storage of 2n complex coefficients,
while to characterize the state of a classical
n-bit system it is sufficient to provide the
values of the n bits, that is, only n numbers.
Although this fact may seem to indicate that
qubits can hold exponentially more information
than their classical counterparts, care must
be taken not to overlook the fact that the
qubits are only in a probabilistic superposition
of all of their states. This means that when
the final state of the qubits is measured,
they will only be found in one of the possible
configurations they were in before the measurement.
It is generally incorrect to think of a system
of qubits as being in one particular state
before the measurement. Since the fact that
they were in a superposition of states before
the measurement was made directly affects
the possible outcomes of the computation.
To better understand this point, consider
a classical computer that operates on a three-bit
register. If the exact state of the register
at a given time is not known, it can be described
as a probability distribution over the
2
3
=
8
{\displaystyle 2^{3}=8}
different three-bit strings 000, 001, 010,
011, 100, 101, 110, and 111. If there is no
uncertainty over its state, then it is in
exactly one of these states with probability
1. However, if it is a probabilistic computer,
then there is a possibility of it being in
any one of a number of different states.
The state of a three-qubit quantum computer
is similarly described by an eight-dimensional
vector
(
a
0
,
a
1
,
a
2
,
a
3
,
a
4
,
a
5
,
a
6
,
a
7
)
{\displaystyle (a_{0},a_{1},a_{2},a_{3},a_{4},a_{5},a_{6},a_{7})}
(or a one dimensional vector with
each vector node holding the amplitude and
the state as the bit string of qubits). Here,
however, the coefficients
a
i
{\displaystyle a_{i}}
are complex numbers, and it is the sum of
the squares of the coefficients' absolute
values,
∑
i
|
a
i
|
2
{\displaystyle \sum _{i}|a_{i}|^{2}}
, that must equal 1. For each
i
{\displaystyle i}
, the absolute value squared
|
a
i
|
2
{\displaystyle \left|a_{i}\right|^{2}}
gives the probability of the system being
found in the
i
{\displaystyle i}
-th state after a measurement. However, because
a complex number encodes not just a magnitude
but also a direction in the complex plane,
the phase difference between any two coefficients
(states) represents a meaningful parameter.
This is a fundamental difference between quantum
computing and probabilistic classical computing.If
you measure the three qubits, you will observe
a three-bit string. The probability of measuring
a given string is the squared magnitude of
that string's coefficient (i.e., the probability
of measuring 000 =
|
a
0
|
2
{\displaystyle |a_{0}|^{2}}
, the probability of measuring 001 =
|
a
1
|
2
{\displaystyle |a_{1}|^{2}}
, etc.). Thus, measuring a quantum state described
by complex coefficients
(
a
0
,
a
1
,
a
2
,
a
3
,
a
4
,
a
5
,
a
6
,
a
7
)
{\displaystyle (a_{0},a_{1},a_{2},a_{3},a_{4},a_{5},a_{6},a_{7})}
gives the classical probability distribution
(
|
a
0
|
2
,
|
a
1
|
2
,
|
a
2
|
2
,
|
a
3
|
2
,
|
a
4
|
2
,
|
a
5
|
2
,
|
a
6
|
2
,
|
a
7
|
2
)
{\displaystyle (|a_{0}|^{2},|a_{1}|^{2},|a_{2}|^{2},|a_{3}|^{2},|a_{4}|^{2},|a_{5}|^{2},|a_{6}|^{2},|a_{7}|^{2})}
and we say that the quantum state "collapses"
to a classical state as a result of making
the measurement.
An eight-dimensional vector can be specified
in many different ways depending on what basis
is chosen for the space. The basis of bit
strings (e.g., 000, 001, …, 111) is known
as the computational basis. Other possible
bases are unit-length, orthogonal vectors
and the eigenvectors of the Pauli-x operator.
Ket notation is often used to make the choice
of basis explicit. For example, the state
(
a
0
,
a
1
,
a
2
,
a
3
,
a
4
,
a
5
,
a
6
,
a
7
)
{\displaystyle (a_{0},a_{1},a_{2},a_{3},a_{4},a_{5},a_{6},a_{7})}
in the computational basis can be written
as:
a
0
|
000
⟩
+
a
1
|
001
⟩
+
a
2
|
010
⟩
+
a
3
|
011
⟩
+
a
4
|
100
⟩
+
a
5
|
101
⟩
+
a
6
|
110
⟩
+
a
7
|
111
⟩
{\displaystyle a_{0}\,|000\rangle +a_{1}\,|001\rangle
+a_{2}\,|010\rangle +a_{3}\,|011\rangle +a_{4}\,|100\rangle
+a_{5}\,|101\rangle +a_{6}\,|110\rangle +a_{7}\,|111\rangle
}
where, e.g.,
|
010
⟩
=
(
0
,
0
,
1
,
0
,
0
,
0
,
0
,
0
)
{\displaystyle |010\rangle =\left(0,0,1,0,0,0,0,0\right)}
The computational basis for a single qubit
(two dimensions) is
|
0
⟩
=
(
1
,
0
)
{\displaystyle |0\rangle =\left(1,0\right)}
and
|
1
⟩
=
(
0
,
1
)
{\displaystyle |1\rangle =\left(0,1\right)}
.
Using the eigenvectors of the Pauli-x operator,
a single qubit is
|
+
⟩
=
1
2
(
1
,
1
)
{\displaystyle |+\rangle ={\tfrac {1}{\sqrt
{2}}}\left(1,1\right)}
and
|
−
⟩
=
1
2
(
1
,
−
1
)
{\displaystyle |-\rangle ={\tfrac {1}{\sqrt
{2}}}\left(1,-1\right)}
.
== Operation ==
While a classical 3-bit state and a quantum
3-qubit state are each eight-dimensional vectors,
they are manipulated quite differently for
classical or quantum computation. For computing
in either case, the system must be initialized,
for example into the all-zeros string,
|
000
⟩
{\displaystyle |000\rangle }
, corresponding to the vector (1,0,0,0,0,0,0,0).
In classical randomized computation, the system
evolves according to the application of stochastic
matrices, which preserve that the probabilities
add up to one (i.e., preserve the L1 norm).
In quantum computation, on the other hand,
allowed operations are unitary matrices, which
are effectively rotations (they preserve that
the sum of the squares add up to one, the
Euclidean or L2 norm). (Exactly what unitaries
can be applied depend on the physics of the
quantum device.) Consequently, since rotations
can be undone by rotating backward, quantum
computations are reversible. (Technically,
quantum operations can be probabilistic combinations
of unitaries, so quantum computation really
does generalize classical computation. See
quantum circuit for a more precise formulation.)
Finally, upon termination of the algorithm,
the result needs to be read off. In the case
of a classical computer, we sample from the
probability distribution on the three-bit
register to obtain one definite three-bit
string, say 000. Quantum mechanically, one
measures the three-qubit state, which is equivalent
to collapsing the quantum state down to a
classical distribution (with the coefficients
in the classical state being the squared magnitudes
of the coefficients for the quantum state,
as described above), followed by sampling
from that distribution. This destroys the
original quantum state. Many algorithms will
only give the correct answer with a certain
probability. However, by repeatedly initializing,
running and measuring the quantum computer's
results, the probability of getting the correct
answer can be increased. In contrast, counterfactual
quantum computation allows the correct answer
to be inferred when the quantum computer is
not actually running in a technical sense,
though earlier initialization and frequent
measurements are part of the counterfactual
computation protocol.
For more details on the sequences of operations
used for various quantum algorithms, see universal
quantum computer, Shor's algorithm, Grover's
algorithm, Deutsch–Jozsa algorithm, amplitude
amplification, quantum Fourier transform,
quantum gate, quantum adiabatic algorithm
and quantum error correction.
== Potential ==
=== Cryptography ===
Integer factorization, which underpins the
security of public key cryptographic systems,
is believed to be computationally infeasible
with an ordinary computer for large integers
if they are the product of few prime numbers
(e.g., products of two 300-digit primes).
By comparison, a quantum computer could efficiently
solve this problem using Shor's algorithm
to find its factors. This ability would allow
a quantum computer to break many of the cryptographic
systems in use today, in the sense that there
would be a polynomial time (in the number
of digits of the integer) algorithm for solving
the problem. In particular, most of the popular
public key ciphers are based on the difficulty
of factoring integers or the discrete logarithm
problem, both of which can be solved by Shor's
algorithm. In particular, the RSA, Diffie–Hellman,
and elliptic curve Diffie–Hellman algorithms
could be broken. These are used to protect
secure Web pages, encrypted email, and many
other types of data. Breaking these would
have significant ramifications for electronic
privacy and security.
However, other cryptographic algorithms do
not appear to be broken by those algorithms.
Some public-key algorithms are based on problems
other than the integer factorization and discrete
logarithm problems to which Shor's algorithm
applies, like the McEliece cryptosystem based
on a problem in coding theory. Lattice-based
cryptosystems are also not known to be broken
by quantum computers, and finding a polynomial
time algorithm for solving the dihedral hidden
subgroup problem, which would break many lattice
based cryptosystems, is a well-studied open
problem. It has been proven that applying
Grover's algorithm to break a symmetric (secret
key) algorithm by brute force requires time
equal to roughly 2n/2 invocations of the underlying
cryptographic algorithm, compared with roughly
2n in the classical case, meaning that symmetric
key lengths are effectively halved: AES-256
would have the same security against an attack
using Grover's algorithm that AES-128 has
against classical brute-force search (see
Key size). Quantum cryptography could potentially
fulfill some of the functions of public key
cryptography. Quantum-based cryptographic
systems could ,therefore, be more secure than
traditional systems against quantum hacking.
=== Quantum search ===
Besides factorization and discrete logarithms,
quantum algorithms offering a more than polynomial
speedup over the best known classical algorithm
have been found for several problems, including
the simulation of quantum physical processes
from chemistry and solid state physics, the
approximation of Jones polynomials, and solving
Pell's equation. No mathematical proof has
been found that shows that an equally fast
classical algorithm cannot be discovered,
although this is considered unlikely. For
some problems, quantum computers offer a polynomial
speedup. The most well-known example of this
is quantum database search, which can be solved
by Grover's algorithm using quadratically
fewer queries to the database than that are
required by classical algorithms. In this
case, the advantage is not only provable but
also optimal, it has been shown that Grover's
algorithm gives the maximal possible probability
of finding the desired element for any number
of oracle lookups. Several other examples
of provable quantum speedups for query problems
have subsequently been discovered, such as
for finding collisions in two-to-one functions
and evaluating NAND trees.
Problems that can be addressed with Grover's
algorithm have the following properties:
There is no searchable structure in the collection
of possible answers,
The number of possible answers to check is
the same as the number of inputs to the algorithm,
and
There exists a boolean function which evaluates
each input and determines whether it is the
correct answerFor problems with all these
properties, the running time of Grover's algorithm
on a quantum computer will scale as the square
root of the number of inputs (or elements
in the database), as opposed to the linear
scaling of classical algorithms. A general
class of problems to which Grover's algorithm
can be applied is Boolean satisfiability problem.
In this instance, the database through which
the algorithm is iterating is that of all
possible answers. An example (and possible)
application of this is a password cracker
that attempts to guess the password or secret
key for an encrypted file or system. Symmetric
ciphers such as Triple DES and AES are particularly
vulnerable to this kind of attack. This application
of quantum computing is a major interest of
government agencies.
=== Quantum simulation ===
Since chemistry and nanotechnology rely on
understanding quantum systems, and such systems
are impossible to simulate in an efficient
manner classically, many believe quantum simulation
will be one of the most important applications
of quantum computing. Quantum simulation could
also be used to simulate the behavior of atoms
and particles at unusual conditions such as
the reactions inside a collider.
=== Quantum annealing and adiabatic optimisation
===
Adiabatic quantum computation relies on the
adiabatic theorem to undertake calculations.
A system is placed in the ground state for
a simple Hamiltonian, which is slowly evolved
to a more complicated Hamiltonian whose ground
state represents the solution to the problem
in question. The adiabatic theorem states
that if the evolution is slow enough the system
will stay in its ground state at all times
through the process.
=== Solving linear equations ===
The Quantum algorithm for linear systems of
equations or "HHL Algorithm", named after
its discoverers Harrow, Hassidim, and Lloyd,
is expected to provide speedup over classical
counterparts.
=== Quantum supremacy ===
John Preskill has introduced the term quantum
supremacy to refer to the hypothetical speedup
advantage that a quantum computer would have
over a classical computer in a certain field.
Google announced in 2017 that it expected
to achieve quantum supremacy by the end of
the year, and IBM says that the best classical
computers will be beaten on some task within
about five years. Quantum supremacy has not
been achieved yet, and skeptics like Gil Kalai
doubt that it will ever be. Bill Unruh doubted
the practicality of quantum computers in a
paper published back in 1994. Paul Davies
pointed out that a 400-qubit computer would
even come into conflict with the cosmological
information bound implied by the holographic
principle. Those such as Roger Schlafly have
pointed out that the claimed theoretical benefits
of quantum computing go beyond the proven
theory of quantum mechanics and imply non-standard
interpretations, such as the many-worlds interpretation
and negative probabilities. Schlafly maintains
that the Born rule is just "metaphysical fluff"
and that quantum mechanics does not rely on
probability any more than other branches of
science but simply calculates the expected
values of observables. He also points out
that arguments about Turing complexity cannot
be run backwards. Those who prefer Bayesian
interpretations of quantum mechanics have
questioned the physical nature of the mathematical
abstractions employed.
== Obstacles ==
There are a number of technical challenges
in building a large-scale quantum computer,
and thus far quantum computers have yet to
solve a problem faster than a classical computer.
David DiVincenzo, of IBM, listed the following
requirements for a practical quantum computer:
scalable physically to increase the number
of qubits;
qubits that can be initialized to arbitrary
values;
quantum gates that are faster than decoherence
time;
universal gate set;
qubits that can be read easily.
=== Quantum decoherence ===
One of the greatest challenges is controlling
or removing quantum decoherence. This usually
means isolating the system from its environment
as interactions with the external world cause
the system to decohere. However, other sources
of decoherence also exist. Examples include
the quantum gates, and the lattice vibrations
and background thermonuclear spin of the physical
system used to implement the qubits. Decoherence
is irreversible, as it is effectively non-unitary,
and is usually something that should be highly
controlled, if not avoided. Decoherence times
for candidate systems in particular, the transverse
relaxation time T2 (for NMR and MRI technology,
also called the dephasing time), typically
range between nanoseconds and seconds at low
temperature. Currently, some quantum computers
require their qubits to be cooled to 20 millikelvins
in order to prevent significant decoherence.As
a result, time-consuming tasks may render
some quantum algorithms inoperable, as maintaining
the state of qubits for a long enough duration
will eventually corrupt the superpositions.These
issues are more difficult for optical approaches
as the timescales are orders of magnitude
shorter and an often-cited approach to overcoming
them is optical pulse shaping. Error rates
are typically proportional to the ratio of
operating time to decoherence time, hence
any operation must be completed much more
quickly than the decoherence time.
As described in the Quantum threshold theorem,
if the error rate is small enough, it is thought
to be possible to use quantum error correction
to suppress errors and decoherence. This allows
the total calculation time to be longer than
the decoherence time if the error correction
scheme can correct errors faster than decoherence
introduces them. An often cited figure for
the required error rate in each gate for fault-tolerant
computation is 10−3, assuming the noise
is depolarizing.
Meeting this scalability condition is possible
for a wide range of systems. However, the
use of error correction brings with it the
cost of a greatly increased number of required
qubits. The number required to factor integers
using Shor's algorithm is still polynomial,
and thought to be between L and L2, where
L is the number of qubits in the number to
be factored; error correction algorithms would
inflate this figure by an additional factor
of L. For a 1000-bit number, this implies
a need for about 104 bits without error correction.
With error correction, the figure would rise
to about 107 bits. Computation time is about
L2 or about 107 steps and at 1 MHz, about
10 seconds.
A very different approach to the stability-decoherence
problem is to create a topological quantum
computer with anyons, quasi-particles used
as threads and relying on braid theory to
form stable logic gates.
== Developments ==
=== 
Quantum computing models ===
There are a number of quantum computing models,
distinguished by the basic elements in which
the computation is decomposed. The four main
models of practical importance are:
Quantum gate array (computation decomposed
into a sequence of few-qubit quantum gates)
One-way quantum computer (computation decomposed
into a sequence of one-qubit measurements
applied to a highly entangled initial state
or cluster state)
Adiabatic quantum computer, based on quantum
annealing (computation decomposed into a slow
continuous transformation of an initial Hamiltonian
into a final Hamiltonian, whose ground states
contain the solution)
Topological quantum computer (computation
decomposed into the braiding of anyons in
a 2D lattice)The quantum Turing machine is
theoretically important but the direct implementation
of this model is not pursued. All four models
of computation have been shown to be equivalent;
each can simulate the other with no more than
polynomial overhead.
=== Physical realizations ===
For physically implementing a quantum computer,
many different candidates are being pursued,
among them (distinguished by the physical
system used to realize the qubits):
Superconducting quantum computing (qubit implemented
by the state of small superconducting circuits
(Josephson junctions))
Trapped ion quantum computer (qubit implemented
by the internal state of trapped ions)
Optical lattices (qubit implemented by internal
states of neutral atoms trapped in an optical
lattice)
Quantum dot computer, spin-based (e.g. the
Loss-DiVincenzo quantum computer) (qubit given
by the spin states of trapped electrons)
Quantum dot computer, spatial-based (qubit
given by electron position in double quantum
dot)
Coupled Quantum Wire (qubit implemented by
a pair of Quantum Wires coupled by a Quantum
Point Contact)
Nuclear magnetic resonance quantum computer
(NMRQC) implemented with the nuclear magnetic
resonance of molecules in solution, where
qubits are provided by nuclear spins within
the dissolved molecule and probed with radio
waves
Solid-state NMR Kane quantum computers (qubit
realized by the nuclear spin state of phosphorus
donors in silicon)
Electrons-on-helium quantum computers (qubit
is the electron spin)
Cavity quantum electrodynamics (CQED) (qubit
provided by the internal state of trapped
atoms coupled to high-finesse cavities)
Molecular magnet (qubit given by spin states)
Fullerene-based ESR quantum computer (qubit
based on the electronic spin of atoms or molecules
encased in fullerenes)
Linear optical quantum computer (qubits realized
by processing states of different modes of
light through linear elements e.g. mirrors,
beam splitters and phase shifters)
Diamond-based quantum computer (qubit realized
by the electronic or nuclear spin of nitrogen-vacancy
centers in diamond)
Bose-Einstein condensate-based quantum computer
Transistor-based quantum computer – string
quantum computers with entrainment of positive
holes using an electrostatic trap
Rare-earth-metal-ion-doped inorganic crystal
based quantum computers (qubit realized by
the internal electronic state of dopants in
optical fibers)
Metallic-like carbon nanospheres based quantum
computersA large number of candidates demonstrates
that the topic, in spite of rapid progress,
is still in its infancy. There is also a vast
amount of flexibility.
=== Timeline ===
In 1959 Richard Feynman in his lecture "There's
Plenty of Room at the Bottom" states the possibility
of using quantum effects for computation.
In 1980 Paul Benioff described quantum mechanical
Hamiltonian models of computers and the Russian
mathematician Yuri Manin motivated the development
of quantum computers.In 1981, at a conference
co-organized by MIT and IBM, physicist Richard
Feynman urged the world to build a quantum
computer. He said, "Nature isn't classical,
dammit, and if you want to make a simulation
of nature, you'd better make it quantum mechanical,
and by golly, it's a wonderful problem because
it doesn't look so easy."In 1984, BB84 is
published, the world's first quantum cryptography
protocol by IBM scientists Charles Bennett
and Gilles Brassard.
In 1993, an international group of six scientists,
including Charles Bennett, showed that perfect
quantum teleportation is possible in principle,
but only if the original is destroyed.
In 1994 Peter Shor, at AT&T's Bell Labs discovered
an important quantum algorithm, which allows
a quantum computer to factor large integers
exponentially much faster than the best known
classical algorithm. Shor's algorithm can
theoretically break many of the public-key
cryptosystems in use today. Its invention
sparked a tremendous interest in quantum computers.
In 1996, The DiVincenzo's criteria are published
which is a list of conditions that are necessary
for constructing a quantum computer proposed
by the theoretical physicist David P. DiVincenzo
in his 2000 paper "The Physical Implementation
of Quantum Computation".
In 2001, researchers demonstrated Shor's algorithm
to factor 15 using a 7-qubit NMR computer.In
2005, researchers at the University of Michigan
built a semiconductor chip ion trap. Such
devices from standard lithography may point
the way to scalable quantum computing.In 2009,
researchers at Yale University created the
first solid-state quantum processor. The two-qubit
superconducting chip had artificial atom qubits
made of a billion aluminum atoms that acted
like a single atom that could occupy two states.A
team at the University of Bristol also created
a silicon chip based on quantum optics, able
to run Shor's algorithm.
Further developments were made in 2010.
Springer publishes a journal (Quantum Information
Processing) devoted to the subject.In February
2010, Digital Combinational Circuits like
an adder, subtractor etc. are designed with
the help of Symmetric Functions organized
from different quantum gates.In April 2011,
a team of scientists from Australia and Japan
made a breakthrough in quantum teleportation.
They successfully transferred a complex set
of quantum data with full transmission integrity,
without affecting the qubits' superpositions.
In 2011, D-Wave Systems announced the first
commercial quantum annealer, the D-Wave One,
claiming a 128 qubit processor. On May 25,
2011, Lockheed Martin agreed to purchase a
D-Wave One system. Lockheed and the University
of Southern California (USC) will house the
D-Wave One at the newly formed USC Lockheed
Martin Quantum Computing Center. D-Wave's
engineers designed the chips with an empirical
approach, focusing on solving particular problems.
Investors liked this more than academics,
who said D-Wave had not demonstrated they
really had a quantum computer. Criticism softened
after a D-Wave paper in Nature, that proved
the chips have some quantum properties. Two
published papers have suggested that the D-Wave
machine's operation can be explained classically,
rather than requiring quantum models. Later
work showed that classical models are insufficient
when all available data is considered. Experts
remain divided on the ultimate classification
of the D-Wave systems though their quantum
behavior was established concretely with a
demonstration of entanglement.During the same
year, researchers at the University of Bristol
created an all-bulk optics system that ran
a version of Shor's algorithm to successfully
factor 21.In September 2011 researchers proved
quantum computers can be made with a Von Neumann
architecture (separation of RAM).In November
2011 researchers factorized 143 using 4 qubits.In
February 2012 IBM scientists said that they
had made several breakthroughs in quantum
computing with superconducting integrated
circuits.In April 2012 a multinational team
of researchers from the University of Southern
California, Delft University of Technology,
the Iowa State University of Science and Technology,
and the University of California, Santa Barbara,
constructed a two-qubit quantum computer on
a doped diamond crystal that can easily be
scaled up and is functional at room temperature.
Two logical qubit directions of electron spin
and nitrogen kernels spin were used, with
microwave impulses. This computer ran Grover's
algorithm generating the right answer from
the first try in 95% of cases.In September
2012, Australian researchers at the University
of New South Wales said the world's first
quantum computer was just 5 to 10 years away,
after announcing a global breakthrough enabling
the manufacture of its memory building blocks.
A research team led by Australian engineers
created the first working qubit based on a
single atom in silicon, invoking the same
technological platform that forms the building
blocks of modern-day computers.In October
2012, Nobel Prizes were presented to David
J. Wineland and Serge Haroche for their basic
work on understanding the quantum world, which
may help make quantum computing possible.In
November 2012, the first quantum teleportation
from one macroscopic object to another was
reported by scientists at the University of
Science and Technology of China in Hefei.In
December 2012, the first dedicated quantum
computing software company, 1QBit was founded
in Vancouver, BC. 1QBit is the first company
to focus exclusively on commercializing software
applications for commercially available quantum
computers, including the D-Wave Two. 1QBit's
research demonstrated the ability of superconducting
quantum annealing processors to solve real-world
problems.In February 2013, a new technique,
boson sampling, was reported by two groups
using photons in an optical lattice that is
not a universal quantum computer but may be
good enough for practical problems. Science
Feb 15, 2013
In May 2013, Google announced that it was
launching the Quantum Artificial Intelligence
Lab, hosted by NASA's Ames Research Center,
with a 512-qubit D-Wave quantum computer.
The USRA (Universities Space Research Association)
will invite researchers to share time on it
with the goal of studying quantum computing
for machine learning. Google added that they
had "already developed some quantum machine
learning algorithms" and had "learned some
useful principles", such as that "best results"
come from "mixing quantum and classical computing".In
early 2014 it was reported, based on documents
provided by former NSA contractor Edward Snowden,
that the U.S. National Security Agency (NSA)
is running a $79.7 million research program
(titled "Penetrating Hard Targets") to develop
a quantum computer capable of breaking vulnerable
encryption.In 2014, a group of researchers
from ETH Zürich, USC, Google ,and Microsoft
reported a definition of quantum speedup,
and were not able to measure quantum speedup
with the D-Wave Two device, but did not explicitly
rule it out.In 2014, researchers at University
of New South Wales used silicon as a protectant
shell around qubits, making them more accurate,
increasing the length of time they will hold
information, and possibly making quantum computers
easier to build.In April 2015 IBM scientists
claimed two critical advances towards the
realization of a practical quantum computer.
They claimed the ability to detect and measure
both kinds of quantum errors simultaneously,
as well as a new, square quantum bit circuit
design that could scale to larger dimensions.In
October 2015, QuTech successfully conducts
the Loophole-free Bell inequality violation
test using electron spins separated by 1.3
kilometres.In October 2015 researchers at
the University of New South Wales built a
quantum logic gate in silicon for the first
time.In December 2015 NASA publicly displayed
the world's first fully operational $15-million
quantum computer made by the Canadian company
D-Wave at the Quantum Artificial Intelligence
Laboratory at its Ames Research Center in
California's Moffett Field. The device was
purchased in 2013 via a partnership with Google
and Universities Space Research Association.
The presence and use of quantum effects in
the D-Wave quantum processing unit is more
widely accepted. In some tests, it can be
shown that the D-Wave quantum annealing processor
outperforms Selby’s algorithm. Only two
of this computer have been made so far.
In May 2016, IBM Research announced that for
the first time ever it is making quantum computing
available to members of the public via the
cloud, who can access and run experiments
on IBM’s quantum processor. The service
is called the IBM Quantum Experience. The
quantum processor is composed of five superconducting
qubits and is housed at the IBM T. J. Watson
Research Center in New York.
In August 2016, scientists at the University
of Maryland successfully built the first reprogrammable
quantum computer.In October 2016 Basel University
described a variant of the electron-hole based
quantum computer, which instead of manipulating
electron spins uses electron holes in a semiconductor
at low (mK) temperatures which are a lot less
vulnerable to decoherence.
This has been dubbed the "positronic" quantum
computer as the quasi-particle behaves like
it has a positive electrical charge.In March
2017, IBM announced an industry-first initiative
to build commercially available universal
quantum computing systems called IBM Q. The
company also released a new API (Application
Program Interface) for the IBM Quantum Experience
that enables developers and programmers to
begin building interfaces between its existing
five quantum bit (qubit) cloud-based quantum
computer and classical computers, without
needing a deep background in quantum physics.
In May 2017, IBM announced that it has successfully
built and tested its most powerful universal
quantum computing processors. The first is
a 16 qubit processor that will allow for more
complex experimentation than the previously
available 5 qubit processor. The second is
IBM's first prototype commercial processor
with 17 qubits and leverages significant materials,
device, and architecture improvements to make
it the most powerful quantum processor created
to date by IBM.
In July 2017, a group of U.S. researchers
announced a quantum simulator with 51 qubits.
The announcement was made by Mikhail Lukin
of Harvard University at the International
Conference on Quantum Technologies in Moscow.
A quantum simulator differs from a computer.
Lukin’s simulator was designed to solve
one equation. Solving a different equation
would require building a new system. A computer
can solve many different equations.
In September 2017, IBM Research scientists
use a 7 qubit device to model the largest
molecule, Beryllium hydride, ever by a quantum
computer. The results were published as the
cover story in the peer-reviewed journal Nature.
In October 2017, IBM Research scientists successfully
"broke the 49-qubit simulation barrier" and
simulated 49- and 56-qubit short-depth circuits,
using the Lawrence Livermore National Laboratory's
Vulcan supercomputer, and the University of
Illinois' Cyclops Tensor Framework (originally
developed at the University of California).
The results were published in arxiv.In November
2017, the University of Sydney research team
in Australia successfully made a microwave
circulator, an important quantum computer
part, 1000 times smaller than a conventional
circulator by using topological insulators
to slow down the speed of light in a material.In
December 2017, IBM announced its first IBM
Q Network clients. The companies, universities,
and labs to explore practical quantum applications,
using IBM Q 20 qubit commercial systems, for
business and science include: JPMorgan Chase,
Daimler AG, Samsung, JSR Corporation, Barclays,
Hitachi Metals, Honda, Nagase, Keio University,
Oak Ridge National Lab, Oxford University
and University of Melbourne.
In December 2017, Microsoft released a preview
version of a "Quantum Development Kit". It
includes a programming language, Q#, which
can be used to write programs that are run
on an emulated quantum computer.
In 2017 D-Wave reported to start selling a
2000 qubit quantum computer.In late 2017 and
early 2018 IBM, Intel, and Google each reported
testing quantum processors containing 50,
49, and 72 qubits, respectively, all realized
using superconducting circuits. By number
of qubits, these circuits are approaching
the range in which simulating their quantum
dynamics is expected to become prohibitive
on classical computers, although it has been
argued that further improvements in error
rates are needed to put classical simulation
out of reach.In February 2018, scientists
reported, for the first time, the discovery
of a new form of light, which may involve
polaritons, that could be useful in the development
of quantum computers.In February 2018, QuTech
reported successfully testing a silicon-based
two-spin-qubits quantum processor.In June
2018, Intel begins testing silicon-based spin-qubit
processor, manufactured in the company's D1D
Fab in Oregon.In July 2018, a team led by
the University of Sydney has achieved the
world's first multi-qubit demonstration of
a quantum chemistry calculation performed
on a system of trapped ions, one of the leading
hardware platforms in the race to develop
a universal quantum computer.
== Relation to computational complexity theory
==
The class of problems that can be efficiently
solved by quantum computers is called BQP,
for "bounded error, quantum, polynomial time".
Quantum computers only run probabilistic algorithms,
so BQP on quantum computers is the counterpart
of BPP ("bounded error, probabilistic, polynomial
time") on classical computers. It is defined
as the set of problems solvable with a polynomial-time
algorithm, whose probability of error is bounded
away from one half. A quantum computer is
said to "solve" a problem if, for every instance,
its answer will be right with high probability.
If that solution runs in polynomial time,
then that problem is in BQP.
BQP is contained in the complexity class #P
(or more precisely in the associated class
of decision problems P#P), which is a subclass
of PSPACE.
BQP is suspected to be disjoint from NP-complete
and a strict superset of P, but that is not
known. Both integer factorization and discrete
log are in BQP. Both of these problems are
NP problems suspected to be outside BPP, and
hence outside P. Both are suspected to not
be NP-complete. There is a common misconception
that quantum computers can solve NP-complete
problems in polynomial time. That is not known
to be true, and is generally suspected to
be false.The capacity of a quantum computer
to accelerate classical algorithms has rigid
limits—upper bounds of quantum computation's
complexity. The overwhelming part of classical
calculations cannot be accelerated on a quantum
computer. A similar fact takes place for particular
computational tasks, like the search problem,
for which Grover's algorithm is optimal.Bohmian
Mechanics is a non-local hidden variable interpretation
of quantum mechanics. It has been shown that
a non-local hidden variable quantum computer
could implement a search of an N-item database
at most in
O
(
N
3
)
{\displaystyle O({\sqrt[{3}]{N}})}
steps. This is slightly faster than the
O
(
N
)
{\displaystyle O({\sqrt {N}})}
steps taken by Grover's algorithm. Neither
search method will allow quantum computers
to solve NP-Complete problems in polynomial
time.Although quantum computers may be faster
than classical computers for some problem
types, those described above cannot solve
any problem that classical computers cannot
already solve. A Turing machine can simulate
these quantum computers, so such a quantum
computer could never solve an undecidable
problem like the halting problem. The existence
of "standard" quantum computers does not disprove
the Church–Turing thesis. It has been speculated
that theories of quantum gravity, such as
M-theory or loop quantum gravity, may allow
even faster computers to be built. Currently,
defining computation in such theories is an
open problem due to the problem of time, i.e.,
there currently exists no obvious way to describe
what it means for an observer to submit input
to a computer and later receive output.
== See also
