Quantum Computing

QUANTUM COMPUTERS
THE BUSINESS SCHOOL Page 1
THE BUSINESS SCHOOL, JAMMU
QUANTUM
COMPUTERS
THE FUTURE IS HERE!
KOMAL GUPTA, MBA – 1st SEM.

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CONTENTS
1. EVOLUTION OF COMPUTERS
1.1 FIRST GENERATION COMPUTERS
1.2 SECOND GENERATION COMPUTERS
1.3 THIRD GENERATION COMPUTERS
1.4 FOURTH GENERATIONS COMPUTERS
1.5 FIFTH GENERATION COMPUTERS
2. CHARACTERISTICS OF A DIGITAL SYSTEM
2.1 LOGICAL OPERATIONS
3. QUANTUM MECHANICS
3.1 WAVE PARTICLE DUALITY
4. QUANTUM COMPUTING
4.1 INTRODUCTION
4.2 QUANTUM BITS
4.3 CLASICAL BIT VS QUBIT
4.4 QUBIT STATES
4.5 QUANTUM SUPERPOSITION
4.6 QUANTUM ENTANGLEMENT
4.7 QUANTUM PARALELISM
4.8 QUANTUM HARDWARE
4.9 SOFTWAREAPPLICATIONS
5. ADVANTAGES
6. APPLICATIONS
7. QUANTUM COMPUTER TILL NOW
8. CURRENT CHALLENGES
9. THE FUTURE
BIBLOIGRAPHY

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1. EVOLUTION OF COMPUTERS
Although computers have technically been in use since the abacus approximately 5000 years
ago, it is modern computers that have had the greatest and most profound effect on society.
The first full-sized digital computer in history was developed in 1944. Called the Mark I, this
computer was used only for calculations and weighed five tons. Despite its size and limited
ability it was the first of many that would start off generations of computer development and
growth.
1.1 FIRST GENERATIONCOMPUTERS
First generation computers bore little resemblance to computers of today, either in
appearance or performance. The first generation of computers took place from 1940 to 1956
and was extremely large in size. The inner workings of the computers at that time were
unsophisticated. These early machines required magnetic drums for memory and vacuum
tubes that worked as switches and amplifiers. It was the vacuum tubes that were mainly
responsible for the large size of the machines and the massive amounts of heat that they
released. These computers produced so much heat that they regularly overheated despite
large cooling units. First generation computers also used a very basic programming language
that is referred to as machine language.
1.2 SECOND GENERATIONCOMPUTERS
The second generation (from 1956 to 1963) of computers managed to do away with vacuum
tubes in lieu of transistors. This allowed them to use less electricity and generate less heat.
Second generation computers were also significantly faster than their predecessors. Another
significant change was in the size of the computers, which were smaller. Transistor
computers also developed core memory which they used alongside magnetic storage.
1.3THIRD GENERATION COMPUTERS
From 1964 to 1971 computers went through a significant change in terms of speed, courtesy
of integrated circuits. Integrated circuits, or semiconductor chips, were large numbers of

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miniature transistors packed on silicon chips. This not only increased the speed of computers
but also made them smaller, more powerful, and less expensive. In addition, instead of the
punch cards and the printouts of previous systems, keyboards and monitors were now
allowing people to interact with computing machines.
1.4 Fourth Generation Computers
The changes with the greatest impact occurred in the years from 1971 to 2010. During this
time technology developed to a point where manufacturers could place millions of transistors
on a single circuit chip. This was called monolithic integrated circuit technology. It also
heralded the invention of the Intel 4004 chip which was the first microprocessor to become
commercially available in 1971. This invention led to the dawn of the personal computer
industry. By the mid-70s, personal computers such as the Altair 8800 became available to the
public in the form of kits and required assembly. By the late 70s and early 80s assembled
personal computers for home use, such as the Commodore Pet, Apple II and the first IBM
computer, were making their way onto the market. Personal computers and their ability to
create networks eventually would lead to the Internet in the early 1990s. The fourth
generation of computers also saw the creation of even smaller computers including laptops
and hand-held devices. Graphical user interface, or GUI, was also invented during this time.
Computer memory and storage also went through major improvements, with an increase in
storage capacity and speed.
1.5 THE FIFTH GENERATIONOF COMPUTERS
In the future, computer users can expect even faster and more advanced computer
technology. Computers continue to develop into advanced forms of technology. Fifth
generation computing has yet to be truly defined, as there are numerous paths that technology
is taking toward the future of computer development. For instance, research is ongoing in the
fields of nanotechnology, artificial intelligence, as well as quantum computation.

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2. CHARACTERISTICS OF A DIGITAL SYSTEM
Digital systems work on binary number system with two digits with base two. The two
binary digits called as bits are 0’s and 1’s.
2.1 LOGICAL OPERATIONS
The binary logic used in the digital systems assumes only two values either HIGH OR LOW.
The high and low voltage levels are used to denote these two values. The two levels, or
states, of a signal variable, can be considered to represent the two numerals viz. 1 and 0 of
the binary number system, or the two logic states, viz TRUE AND FALSE in logic
operations. In binary logic, the two voltage levels represent the two binary digits, 1 and 0 if
the higher of the two voltages represents a 1 and the lower voltage represents a 0, and the
system is called a positive logic system. On the other hand, if the lower voltage represents a 1
and the higher voltage represents a 0, we have a negative logic system.

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3. QUANTUM MECHANICS
The smallest amount of a physical quantity that can exist independently is termed as
Quantum. Quantum mechanics is a fundamental branch of physics concerned with
processes involving particles like atoms and photons. Quantum mechanics gradually
arose from Max Planck's solution in 1900 to the black-body radiation problem (reported
1859) and Albert Einstein's 1905 paper which offered a quantum-based theory to explain
the photoelectric effect (reported 1887). Early quantum theory was profoundly reconceived
in the mid-1920s.
The result of theory found that subatomic particles and electromagnetic waves are neither
simply a particle nor wave but have certain properties of each. This originated the concept
of wave–particle duality.
3.1 WAVE-PARTICLE DUALITY
Wave–particle duality is the concept that every elementary particle or quantic entity may be
partly described in terms not only of particles, but also of waves. A given kind of quantum
object will exhibit sometimes wave, sometimes particle character, in respectively different
physical settings.

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4. QUANTUM COMPUTING
Richard Feynman’s observation that certain quantum mechanical effects cannot be simulated
efficiently on a computer led to speculation that computation in general could be done more
efficiently if it used these quantum effects. This speculation proved justified when Peter Shor
described a polynomial time quantum algorithm for factoring integers.
In quantum systems, the computational space increases exponentially with the size of the
system which enables exponential parallelism. This parallelism could lead to exponentially
faster quantum algorithms than possible classically. The catch is that accessing the results,
which requires measurement, proves tricky and requires new non-traditional programming
techniques.
4.1 INTRODUCTION
Richard Feynman observed in the early 1980’s that certain quantum mechanical effects
cannot be simulated efficiently on a classical computer. This observation led to speculation
that perhaps computation in general could be done more efficiently if it made use of these
quantum effects. But building quantum computers, computational machines that use such
quantum effects, proved tricky, and as no one was sure how to use the quantum effects to
speed up computation, the field developed slowly. It wasn’t until 1994, when Peter Shor
surprised the world by describing a polynomial time quantum algorithm for factoring integers
that the field of quantum computing came into its own. This discovery prompted a flurry of
activity, both among experimentalists trying to build quantum computers and theoreticians
trying to find other quantum algorithms.
4.2 QUANTUM BITS
A qubit is a quantum bit, the counterpart in quantum computing to the binary digit or bit of
classical computing. Just as a bit is the basic unit of information in a classical computer, a
qubit is the basic unit of information in a quantum computer.

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In a quantum computer, a number of elemental particles such as electrons or photons can be
used (in practice, success has also been achieved with ions), with either their charge or
polarization acting as a representation of 0 and/or 1. Each of these particles is known as a
qubit; the nature and behavior of these particles (as expressed in quantum theory) form the
basis of quantum computing.
4.3 CLASSICAL BIT VERSUS QUBIT
The bit is the basic unit of information. It is used to represent information by computers.
Regardless of its physical realization, a bit has two possible states typically thought of as 0
and 1, but more generally—and according to applications—interpretable as true and false, or
HIGH or LOW. An analogy to this is a light switch—its OFF position can be thought of as 0
and its ON position as 1.
A qubit has a few similarities to a classical bit, but is overall very different. There are two
possible outcomes for the measurement of a qubit—usually 0 and 1, like a bit. The difference
is that whereas the state of a bit is either 0 or 1, the state of a qubit can also be
a superposition of both.
For a system of n components, a complete description of its state in classical physics requires
only n bits, whereas in quantum physics it requires 2n−1 complex numbers

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4.4 QUBIT STATES
Figure 1: Bloch Sphere Representation of a Qubit. The probability amplitude are given
by α=cosθ/2 and β=eiф sinθ/2
A pure qubit state is a linear superposition of the basis states. This means that the qubit can
be represented as a linear combination of |0> and |1>
|Ѱ> = α|0> + β|1>
where α and β are probability amplitudes and can in general both be complex numbers.
When we measure this qubit in the standard basis, the probability of outcome |0> is |α|^2 and
the probability of outcome |1> is |β|^2. Because the absolute squares of the amplitudes equate
to probabilities, it follows that α and β must be constrained by the equation
|α|2 + |β|2 = 1
4.5 QUANTUM SUPERPOSITIONS
Quantum superposition is a fundamental principle of quantum mechanics. It states that,
much like waves in classical physics, any two (or more) quantum states can be added
together ("superposed") and the result will be another valid quantum state; and

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conversely, that every quantum state can be represented as a sum of two or more other
distinct states.
It is applied to quantum logical qubit state, as used in quantum information processing, as
a linear superposition of the "basis states" |0> and |1>. Here is the Dirac notation for the
quantum state that will always give the result 0 when converted to classical logic by a
measurement. Likewise is the state that will always convert to 1.
4.6 QUANTUM ENTANGLEMENT
Quantum entanglement is a physical phenomenon that occurs when pairs or groups
of particles are generated or interact in ways such that the quantum state of each particle
cannot be described independently of the others, even when the particles are separated by
a large distance – instead, a quantum state must be described for the system as a whole.
Measurements of physical properties such as position, momentum, spin, and polarization,
performed on entangled particles are found to be appropriately correlated.
4.7 QUANTUM PARALELLISM
Classically, the time it takes to do certain computations can be decreased by using
parallel processors. To achieve an exponential decrease in time requires an exponential
increase in the number of processors, and hence an exponential increase in the amount of
physical space needed. However, in quantum systems the amount of parallelism increases
exponentially with the size of the system. Thus, an exponential increase in parallelism
requires only a linear increase in the amount of physical space needed. This effect is
called quantum parallelism.
4.8 QUANTUM HARDWARE
To understand what happens inside the compute when programmers send information to
a Quantum Machine, it is important to understand how a quantum computer is physically

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built, how quantum bits and their associated circuitry are created, addressed, and
controlled, and what is happening inside the computer.
Inside the Processor
i. Building Blocks of QC
 Classical CMOS Transistor
The way the information is encoded and accessed in modern digital computers is
by adjusting and monitoring voltages that are present on tiny transistor switches
inside integrated circuits. Each transistor is addressed by a bus which is able to set
it to a state of either 0 (a low voltage) or 1 (a high voltage). Thus, the idea of an
electrical voltage to ‘encode’ bits of information in a physical device is used.
 The SQUID – A Quantum Transistor
Quantum computers have similarities to and differences from this CMOS
transistor ides. Figure 1 shows a schematic illustration of what is known as a
superconducting qubit (also called a SQUID), which is the basic building block of
a quantum computer (a quantum 'transistor', if you like). The name SQUID comes
from the phrase Superconducting QUantum Interference Device. The term
'Interference' refers to the electrons - which behave as waves inside a quantum
computer, interference patterns which give rise to the quantum effects. The reason
that quantum effects such as electron waves are supported in such a structure -
allowing it to behave as a qubit - is due to the properties of the material from
which it is made. The large loop in the diagram is made from a metal called
niobium (in contrast to conventional transistors which are mostly made from
silicon). When this metal is cooled down, it becomes what is known as a
superconductor, and it starts to exhibit quantum mechanical effects.

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Figure 2: A schematic of a superconducting qubit, the basic building block of
the Quantum Computer. The arrows indicate the magnetic spin states which
encode the bits of information as +1 and 0 values. Unlike regular bits of
information, these states can be put into quantum mechanical superposition.
A regular transistor allows you to encode 2 different states (using voltages). The
superconducting qubit structure instead encodes 2 states as tiny magnetic fields,
which either point up or down. These states are +1 and 0, and they correspond to
the two states that the qubit can 'choose' between. Using the quantum mechanics
that is accessible with these structures, objects can be controlled so that we can
put the qubit into a superposition of these two states. So by adjusting a control
knob on the quantum computer, you qubits can be controlled into a superposition
state where it has not yet decided which of those 1, 0 state to be.
ii. A Fabric of Programmable Elements
In order to go from a single qubit to a multi-qubit processor, the qubits must be
connected together such that they can exchange information. This is achieved
through the use of elements known as couplers. The couplers are also made from
superconducting loops. By putting many such elements (qubits and couplers)
together, building up a fabric of quantum devices that are programmable can be
started. Figure 3 shows a schematic of 8 connected qubits. The loop shown in the
previous diagram has now been stretched out to form one of the long gold

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rectangles. At the points where the rectangles cross, the couplers have been shown
schematically as blue dots.
Figure 3: A schematic of 8 connected qubits
iii. Support Circuitry: Addressing, Programming and Reading the Qubits
There are several additional components necessary for processor operation. A large
part of the circuitry that surrounds the qubits and couplers is a framework of switches
(or Josephson Circuits) forming circuitry which both addresses each qubit and stores
that information in a magnetic memory element local to each device. The majority of
the Josephson junctions are used to make up this circuitry. Additionally, there are
readout devices attached to each qubit. During the computation these devices are
inactive and do not affect the qubits' behavior. After the computation has finished,
and the qubits have settled into their final (classical) 0 or 1 states, the readouts are
used to query the value held by each qubit and return the answer as a bit string of 0's
and 1's to the end user.
The image in figure 4 shows the layout of the actual circuit, as drawn in a CAD
program and is ready to be sent off to the processor fabrication foundry. Here the full
complexity of the processor is revealed. In this image, the qubits are now shown as
long pink strips, which have been stretched out even more than in the previous figure.

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The green and yellow elements that sit in the spaces between qubits are components
which make up the programmable circuitry mentioned above. The yellow dots are
Josephson junctions embedded within this circuitry.
Figure 4: False-color view of part of a CAD layout of the 128 qubit chip
architecture. This image is from a real processor design layout file, which is sent to
the manufacturer and from which the processors are fabricated layer by layer. The
long qubit loops are now shown as the pink areas, the control circuitry lines which
carry currents to the programmable are indicated by the green features and the
Josephson junctions are shown in yellow.
Note that this architecture is very different from conventional computing. The
processor has no large areas of memory (cache), rather each qubit has a tiny piece of
memory of its own. In fact, the chip is architected more like a biological brain than
the common architecture of a conventional silicon processor. One can think of the
qubits as being like neurons, and the couplers as being like synapses that control the
flow of information between those neurons.

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iv. Manufacturing Quantum Processors
Figure 5 shows an image of the final chips after fabrication in a superconducting
electronics foundry. The chips are 'stamped' onto a silicon wafer using techniques
modified from the processes used to make semiconductor integrated circuits. There are
several processors visible on this wafer image. The largest, near the bottom center, has
128 qubits connected together with 352 connection elements between them. The
qubit/coupler circuits on each individual processor are the cross-hatched looking patches
visible in this image. This is known as a Rainier processor.
Figure 5: Photograph of a wafer of Rainier processors, including the 128-qubit
processor used in the D-Wave One™ QC system.
Outside the Processor
i. The Processor Packaging
To build the quantum computer, one of these chips is selected from the wafer, and placed
in the center of the processor packaging system, as shown in Figure 6. This image shows

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the chip area open, just after it has been wire bonded to connect it to the signal lines. It is
possible to see the signal lines on the surrounding printed circuit board. There are far
fewer incoming lines than there are programmable elements on the processor, which is
made possible by additional circuitry - in the form of de-multiplexers and signal routing
and addressing – all implemented in superconducting logic circuitry on the chip.
Figure 6: A photograph of the chip after being bonded to the circuit board which
allows signals lines to be connected.
ii. Computer Cooling
Reduction of the temperature of the computing environment below approximately 80mK
is required for the processor to function, and generally performance increases as
temperature is lowered - the lower the temperature, the better. The latest generation D-
Wave 2X system has an operating temperature of about 15 millikelvin. The processor and
parts of the input/output (I/O) system, comprising roughly 10kg of material, is cooled to
this temperature, which is approximately 180 times colder than interstellar space! Most of
the physical volume of the current system is due to the large size of the refrigeration
system. The refrigeration system used to cool the processors is known as a dilution
refrigerator.

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To reach the near-absolute zero temperatures at which the system operates, the
refrigerators use liquid Helium as a coolant. The type of refrigerator inside the D-Wave
system is known as a "dry" dilution refrigerator. This means that all the liquid helium
resides inside a closed cycle system, where it is recycled and re-condensed using a pulse-
tube technology. This makes them suited to remote deployment, as there is no
requirement for liquid helium replenishment on-site.
Figure 7: Temperature set – up for the processor
iii. Computer Shielding and Wiring
The I/O subsystem is responsible for passing information from the user to the processor
and back. The signals are low frequency (<30MHz) analog currents, carried on metal
lines, transitioning to superconducting lines at low temperatures. Key components of the
I/O subsystem include the processor mount and wire bonding to it; low frequency band
pass filters for removing noise from the lines; room temperature electronics for

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converting signals coming from a front end server to analog currents; and the front end
server which receives programming instructions from a user.
Nearly all aspects of the I/O subsystem are designed, manufactured, and tested by D-
Wave. Many of the specifications of the I/O system place unusual demands on the
materials and processes involved. For example, much of the I/O subsystem must function
at 20mK and be robust against multiple warming / cooling cycles between room
temperature and base. Much of the subsystem must be made using superconducting
metals, such as tin, which are typically non-standard for manufacturers. Additionally
none of the materials close to the processor can be magnetic. To enforce this requirement,
the company individually tests the magnetic character of every single component of each
I/O subsystem at base temperature, and includes only those components that pass.
The current I/O subsystems provide 192 heavily filtered lines from room temperature to
the processor, and are designed for optimal operation of a single quantum processor. The
D-Wave processor design is adversely affected by stray magnetic fields, and extreme care
must be taken to exclude these. The current magnetic shielding system achieves fields
less than 1 nanoTesla (nT) in three dimensions across the entire volume of the processor.
This is achieved by a system comprising five concentric cylindrical shields, some of them
high permeability metals and some of them superconducting. Integrated, on the
processors, are magnetic sensors that measure the ambient field. Countering magnetic
fields are applied that zero the field at these sensors. The temperature of the assembly is
then slowly reduced, and the superconducting shields go superconducting, and 'lock' the
zeroed field in place.
4.9 SOFTWARE APPLICATION
The D-Wave 2X System has a web API with client libraries available for C, C++, Python
and MATLAB. This interface allows the machine to be easily accessed as a cloud
resource over a network. Using development tools and client libraries, users can write
code in the language of their choice.
The D-Wave software architecture is in the early stages of development. This picture
depicts the architecture, with future items indicated by italics.

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Figure 8: D-Wave Software Environment
Programming a quantum computer is different than programming a traditional computer.
To program the system, the user maps a problem into a search for the “lowest point in a
vast landscape,” which corresponds to the best possible outcome. The processor
considers all the possibilities simultaneously to determine the lowest energy required to
form those relationships. Because a quantum computer is probabilistic rather than
deterministic, the computer returns many very good answers in a short amount of time -
10,000 answers in one second. This gives the user not only the optimal solution or a
single answer, but also other alternatives to choose from.
Users can submit problems to the system in a number of different ways, as described
below. Values corresponding to the “weights” of the qubits and coupling “strengths” of

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the interaction between them are submitted to the system, which then executes a single
Quantum Machine Instruction (QMI) for processing. Up to about 1000 weights and about
3000 strengths can be specified, reflecting the number of qubits and the number of
connections in the current D-Wave 2X 1000 qubit processor.
The solutions are values that correspond to the optimal configuration of qubits found, or
the lowest points in the energy landscape. These values are returned to the user program
over the network. Users can specify the number of solutions they want the system to
return.
There are multiple ways to engage the system:
 Use a higher level program in C, C++, Fortran or Python to create and
execute a Quantum Machine Instruction.
 Use one of the D-Wave tools under development including:
 QSage, a translator designed for optimization problems
 ToQ, a High Level Language translator used for constraint
satisfaction problems and designed to let users “speak” in the
language of their problem domain.
 Directly program the system by using Quantum Machine Language to
issue the Quantum Machine Instruction.

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5. ADVANTAGES OF QUANTUM COMPUTERS
Though complex, Quantum Computers have a lot of advantages which make it a need for the
future. Its powerful processor is a major breakthrough in the field of science. Some of its
advantages are:
 It can process massive amount of complex data.
 It has the ability to solve scientific and commercial problems.
 Its powerful processor can process data in a much faster speed.
 It has the capability to convey more accurate answers.
 Its feature of parallelism enables it to counter large number of problems simultaneously.
6. APPLICATIONS
6.1 OPTIMIZATION
Imagine you are building a house, and have a list of things you want to have in your
house, but you can’t afford everything on your list because you are constrained by a
budget. What you really want to work out is the combination of items which gives you
the best value for your money.
This is an example of a optimization problem, where you are trying to find the best
combination of things given some constraints. Typically, these are very hard problems to
solve because of the huge number of possible combinations. With just 270 on/off
switches, there are more possible combinations than atoms in the universe!
These types of optimization problems exist in many different domains - systems design,
mission planning, airline scheduling, financial analysis, web search, cancer radiotherapy
and many more. They are some of the most complex problems in the world, with
potentially enormous benefits to businesses, people and science if optimal solutions can
be readily computed.
“Optimization problems are some of the most complex problems to solve.”

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6.2RADIOTHERAPYOPTIMIZATION
There are many examples of problems where a quantum computer can complement an
HPC (high performance computing) system. While the quantum computer is well suited
to discrete optimization, the HPC system is much better at large scale numerical
simulations. Problems like optimizing cancer radiotherapy, where a patient is treated by
injecting several radiation beams into the patient intersecting at the tumor, illustrates how
the two systems can work together.
The goal when devising a radiation plan is to minimize the collateral damage to the
surrounding tissue and body parts – a very complicated optimization problem with
thousands of variables. To arrive at the optimal radiation plan requires many simulations
until an optimal solution is determined. With a quantum computer, the horizon of
possibilities that can be considered between each simulation is much broader. But HPC is
still the more powerful computation tool for running simulations. Using the quantum
computer with an HPC system will allow faster convergence on an optimal design than is
attainable by using HPC alone.

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6.3PROTEIN FOLDING
Simulating the folding of proteins could lead to a radical transformation of our
understanding of complex biological systems and our ability to design powerful new
drugs.
This application looks into how to use the quantum computer to explore the possible
folding configurations of these interesting molecules. With an astronomical number of
possible structural arrangements, protein folding is an enormously complex
computational problem. Scientific research indicates that nature optimizes the amino acid
sequences to create the most stable protein - which correlates well to the search for the
lowest energy solutions.
With researchers at Harvard, we designed a system for predicting the folding patterns for
lattice protein folding models and successfully ran small protein folding problems in
hardware.

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6.4MACHINE LEARNING
When you look at a photograph it is very easy for you to pick out the different objects in
the image: Trees, Mountains, Velociraptors etc. This task is almost effortless for humans,
but is in fact a hugely difficult task for computers to achieve. This is because
programmers don’t know how to define the essence of a ‘Tree’ in computer code.
Machine learning is the most successful approach to solving this problem, by which
programmers write algorithms that automatically learn to recognize the ‘essences’ of
objects by detecting recurring patterns in huge amounts of data. Because of the amount of
data involved in this process, and the immense number of potential combinations of data
elements, this is a very computationally expensive optimization problem. As with other
optimization problems, these can be mapped to the native ability of the D-Wave
processor.
“Machines learn to recognize objects by detecting recurring patterns.”
6.5OBJECT DETECTION
Quantum hardware, trained using a binary classification algorithm, is able to detect
whether or not an image contains a car.
Together with researchers at Google, we built software for determining whether or not
there is a car in an image using a binary classification algorithm run in hardware. In
excess of 500,000 discrete optimization problems were solved during the learning phase,
with Google developers accessing the D-Wave system remotely.

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6.7LABELING NEWS STORIES
We built software for automatically applying category labels to news stories and images.
We found that our approach provided better labeling accuracy than a state of the art
conventional approach.
The labeling of news stories can be difficult for computers as they can see the keywords
but don’t understand the meaning of the words when combined. For labeling news stories
the corpus we used for training and testing performance was the REUTERS corpus, a
well-known data set for testing multiple label assignment algorithms.
We took a similar approach to labeling images and used the SCENE corpus for training
and testing performance, a well-known data set for testing multiple label assignment
algorithms.
We found that our approach worked extremely well on these problems, demonstrating the
quantum computer's ability to do multiple label assignment and to label images.
6.8VIDEO COMPRESSION
Using unsupervised machine learning approaches, one can automate the discovery of a
very sparse way to represent objects. This technique can be used for incredibly efficient
compression.

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The algorithm works by finding a concise representation of the objects being fed into the
computer. The techniques involved are closely related to those in compressive sensing.
As a test of the unsupervised feature learning algorithm, we discovered an extremely
sparse representation of the ‘Frey faces’ data set, and demonstrated the ability to perform
video compression on the quantum computer.
6.9MONTE CARLO SIMULATION
Many things in the world are uncertain, and governed by the rules of probability. We
have, in our heads, a model of how things will turn out in the future, and the better our
model is, the better we are at predicting the future. We can also build computer models to
try and capture the statistics of reality. These tend to be very complicated, involving a
huge number of variables.
In order to check to see if a computer’s statistical model represents reality we need to be
able to draw samples from it, and check that the statistics of our model match the
statistics of real world data. Monte Carlo simulation, which relies on repeated random
sampling to approximate the probability of certain outcomes, is an approach used in
many industries such as finance, energy, manufacturing, engineering oil & gas and the
environment. For a complex model, with many different variables, this is a difficult task
to do quickly.

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7. QUANTUM COMPUTER TILL NOW
8. CURRENT CHALLENGES
Scientists have a challenge to prove that a quantum machine is actually doing quantum
computations. That’s because in a quantum system, the very act of observing information is
transit, changes in the nature of the data.

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9. THE FUTURE
Quantum computing technology will only continue to improve. Quantum computers
can also be used to efficiently simulate other quantum systems. Perhaps someday
quantum computers will be used to design the next generation of classical computers.
Recently, D-Wave Systems, announce that it broke the 1000 qubit barrier, which (if
true) would make it the most powerful computer on the planet. Now IBM, Microsoft,
HP and Google are trying to figure out how to advance and commercialize the
technology, in association with D-wave.
It is impossible evento predict what technology will win out in the long term. Theory
also continues to advance. Various researchers are actively looking for new algorithms
and communication protocols to exploit the properties of quantum systems. It’s a trend
worth watching while we won’t be able to buy a quantum computer for a few more
years.
This is still science--but it may become technology sooner than we expect.

QUANTUM COMPUTERS
BIBLIOGRAPHY
 http://www.slideshare.net/RecruitCommunications/quantum-computing-and-
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 http://www.slideshare.net/nisargbhagavantanavar/quantum-computer-ppt
 http://www.slideshare.net/SendashPangambam/quantum-computing-basic-
concepts
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future
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 http://www-bcf.usc.edu/~tbrun/Talks/IEEE_Lecture.ppt
 https://www.eecis.udel.edu/~saunders/courses/879-03s/quantumComputers.ppt
 http://www2.fiit.stuba.sk/~kvasnicka/QuantumComputing/Gruska_QC.pdf
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 http://www.cs.virginia.edu/~robins/The_Limits_of_Quantum_Computers.pdf
 https://arxiv.org/pdf/quant-ph/9809016.pdf
 http://www.qudev.ethz.ch/content/QSIT15/Shors%20Algorithm.pdf
 http://www.uwyo.edu/moorhouse/slides/talk2.pdf
 http://www.allaboutcircuits.com/technical-articles/fundamentals-of-quantum-
computing/
 http://whatis.techtarget.com/definition/qubit
 http://www.dwavesys.com/tutorials/background-reading-series/introduction-d-
wave-quantum-hardware
 http://www.dwavesys.com/tutorials/background-reading-series/quantum-
computing-primer
 http://www.dwavesys.com/quantum-computing/applications

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