QUANTUM COMPUTERS

THE BUSINESS SCHOOL Page 1

THE BUSINESS SCHOOL, JAMMU

QUANTUM COMPUTERS

THE FUTURE IS HERE!

KOMAL GUPTA, MBA – 1st SEM.

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 2

QUANTUM COMPUTERS

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 GENERATION COMPUTERS

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 GENERATION COMPUTERS

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

THE BUSINESS SCHOOL Page 3

QUANTUM COMPUTERS

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 GENERATION OF 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.

THE BUSINESS SCHOOL Page 4

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 5

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 6

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 7

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 8

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 9

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 10

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 11

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 12

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 13

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 14

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 15

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 16

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 17

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 18

QUANTUM COMPUTERS

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

THE BUSINESS SCHOOL Page 19

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 20

QUANTUM COMPUTERS

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.”

THE BUSINESS SCHOOL Page 21

QUANTUM COMPUTERS

6.2RADIOTHERAPY OPTIMIZATION

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. 

THE BUSINESS SCHOOL Page 22

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 23

QUANTUM COMPUTERS

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. 

THE BUSINESS SCHOOL Page 24

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 25

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 26

QUANTUM COMPUTERS

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.

THE BUSINESS SCHOOL Page 27

QUANTUM COMPUTERS

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 even to 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.

THE BUSINESS SCHOOL Page 28

QUANTUM COMPUTERS

BIBLIOGRAPHY

http://www.slideshare.net/RecruitCommunications/quantum-computing-and-

dwave?qid=2bead8a9-a4a7-4151-ad87-689e47ec4249&v=&b=&from_search=1

http://www.slideshare.net/nisargbhagavantanavar/quantum-computer-ppt

http://www.slideshare.net/SendashPangambam/quantum-computing-basic-

concepts

http://www.slideshare.net/VernBrownell/quantum-computing-welcome-to-the-

future

http://www.slideshare.net/Deepti.B/quantum-computers-1727930

http://www.slideshare.net/t0pgun/quantum-computing-33771346

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

http://homepages.cwi.nl/~rdewolf/qcnotes.pdf

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

THE BUSINESS SCHOOL Page 29