A Paradigm Shift in the Computing World: Introduction to Quantum Computing

"Nature isn’t classical . . . and if you want to make a simulation of nature, you’d better make it quantum mechanical..."- Richard Feynman

The idea of quantum computing was first suggested by Richard Feynman, a Nobel Prize-winning physicist, in a paper published in 1982. Quantum mechanics is a subfield of physics that describes the behavior and properties of nature on an atomic scale. The underlying principles of Quantum mechanics provide the basis for Quantum computing (QC). Unlike the previous generations of computers which operated on Vacuum Tubes (1st gen), Transistors (2nd Gen), Integrated Circuits (3rd Gen), until the present day Microprocessors (4th Gen)– Quantum Computers are a new paradigm that disrupts the architecture and functioning of the computer down to the sub-atomic level.

Quantum Computers do not just differ from a traditional computer in terms of the hardware used for performing the computations. The fundamental difference between the computers and computing systems we interact with daily and quantum computing is the way information is processed on the backend.

A traditional computer relies on a binary system, meaning the computer processes information using bits- 0’s and 1’s- the smallest unit of data in a computer. The 0’s and 1’s are based on the boolean logic and represent states False (0) and True (1) respectively. All data— the applications that are run, the images that appear, the music that we hear—are translated into bits for the computer to understand and process.1

On the contrary, the basic units of a Quantum Computer are referred to as quantum bits or qubits. Qubits take the fundamental idea of a bit- 0’s and 1’s but the behavior of qubits is derived directly from the behavior of a spinning electron orbiting around the nucleus of an atom. An electron in Quantum mechanics exhibits three properties–

1. Quantum Superposition– The location of a spinning electron cannot be pinpointed to a specific location at any time, it is rather calculated as the probability of existing at a specific place at a specific time. According to this Quantum Superposition, the electron can exist at all locations at all times with varying probabilities.
2. Quantum Entanglement– All revolving electrons have a spin associated with it. When an electron spins clockwise on its axis, it is described as spin-up (Up); when it spins counterclockwise it is referred to as spin-down (Down). When a pair of electrons are formed together, they are referred to as entangled with each other. According to Quantum Entanglement, the pair of entangled electrons always spin in opposite directions and can influence one another through time and space, even when they’re not physically connected. As bizarre as this concept sounds in theory, the implementation is groundbreaking. This means that if we consider a pair of entangled electrons and separate them through time and space, determining the state (or spin) of one electron from the pair can definitely determine the state of the other electron from the same pair.
3. Quantum Interference– An individual particle, such as a photon (light particles) can cross its own trajectory and interfere with the direction of its path.

Based on the above principles, qubits can be both a 1 and 0 at the same time and therefore carry out many computations concurrently. Qubits can exist in multiple states at the same time via Quantum Superposition. Since every qubit “q” can exist in two states, it grows exponentially in the form of 2^q. For example, two qubits can represent four numbers (2^2=4), three qubits eight numbers (2^3=2*2*2=8), and nine qubits 512 numbers (2^9=512), at the same time. Therefore a quantum computer‘s capabilities increase exponentially, and computation steps can be carried out massively parallel instead of one step at a time (as is done with bits in traditional computing). This allows quantum computers to carry out complex operations quicker than classical computers. Qubits can also influence one another even when they’re not physically connected, via Quantum Entanglement. Thus the state of one entangled qubit could be altered or determined using the second qubit in the pair. Quantum Interference is a byproduct of superposition, which allows us to bias the measurement of a qubit toward a desired state or set of states. 

Quantum computing is a fairly new technology yet has garnered a lot of traction lately. This can be attributed to two main factors- 

First, the concern about the slowing of technology-scaling leading to an increased interest in alternative computing technology– Since the early days of modern classical computers, the semiconductor chip industry was governed by “Moore‘s Law“ by Gordon Moore (co-founder of Intel) in 1965. “Moore‘s Law” is based on Gordon Moore's projection that the number of transistors on a microchip doubles every two years, though the cost of computers is halved.2 Meaning that since the 1960s, computing power has been on an exponential growth path, and doubling roughly every two years.

The representation above depicts Moore’s Law in action depicting the exponential growth in Transistor count on the chip starting from 1970 until 2018. Moore's law held true till about 2010 beyond which this relatively steady, reliable, growth has been slowing down.

However, the newest Intel fabrication plant, meant to build chips with minimum feature sizes of 10 nanometers, was much delayed, delivering chips in 2019, five years after the previous generation of chips with 14-nanometer features. Contrary to Moore's Law, which talks about the rate of progress every two years, the technology is no longer scaling at a similar rate anymore.3

“The world is running out of computing capacity. Moore’s law is kind of running out of steam. We need quantum computing to create all of these rich experiences we talk about, all of this artificial intelligence.” – Satya Nadella (CEO, Microsoft), 2018

Second, the untapped potential in the computational power of a quantum computer backed by the technological advancement in hardware, software, and algorithms necessary to make it work– Even though in its infancy and limited practicality, Quantum Computing and its possible uses have led to the development of a number of possible use-cases by researchers and scientists. From cybersecurity to pharmaceutical research to finance, Quantum Computing promises to facilitate major advancements. Since the universe fundamentally operates on the principles governed by Quantum mechanics, an underlying computational principle for Quantum Computers, a number of complex problems involving multiple variables could be tackled using QC.

1. Molecular Biology– Quantum Computers could be used to simulate molecules, molecular processes, aiding in drug development. This is a complex process in which proteins are engineered for targeted medical purposes and are challenging from a computational standpoint. The traditional old-school trial-and-error method of running chemical experiments would take a substantially longer time with today's computing power. In fact, Boston Consulting Group noted, merely modeling a penicillin molecule would require an impossibly large classical computer with 10-to-the-86th-power bits. For advanced quantum computers, though, that same process could be a snap — and could lead to the discovery of new drugs for serious maladies like cancer, Alzheimer’s, and heart disease.?
2. Traffic Optimization– Another application of Quantum Computing is in the field of Routing and Transportation. The “traveling salesman” problem, for instance, is one of the most difficult and famous optimization computations. It aims to determine the shortest possible route between multiple cities, hitting each city once, and returning to the starting point. What makes this computation difficult is that if there is a way to break this problem into smaller component problems, the components will be at least as complex as the original one. The exact solution to the problem can be found, using branch and bound algorithms which makes it incredibly difficult for a classical computer to tackle its hardware limitations. For fully realized QCs, though, this problem could be a cakewalk. D-Wave (a Canadian quantum computing company) and Volkswagen (a German automaker) have already run pilot programs on a number of traffic-related optimization challenges, including streamlining traffic flows in Beijing, Barcelona, and Lisbon. Volkswagen continues to tweak the quantum algorithm after each trial run using a fleet of buses that travel along distinct routes and are tailored to real-time traffic conditions.?
3. Artificial Intelligence and Machine learning– AI systems thrive when the machine learning algorithms used to train the AI are fed with massive amounts of data to ingest, classify, and analyze. The performance of AI is directly proportional to the precise classification of data according to specific characteristics, or features. Quantum computers are expected to play a crucial role in machine learning since they could achieve classification and massive data processing in a breath. IBM’s AI team performed a simple classification experiment that involves using machine learning to organize data into similar groups (in this case, dots with similar colors). IBM’s team first ran the task on a quantum machine without entangling the qubits, which produced an error rate of 5 percent.? The second time around it ran the same experiment with the qubits entangled, which produced an error rate of just 2.5 percent. Suggesting that as quantum computers get better at harnessing qubits and at entangling them, they’ll also get better at tackling machine-learning problems.

The main breakthrough of quantum computing isn’t just the speed at which it will solve challenges, but the kinds of challenges it can solve. Leveraging quantum qualities like Superposition, Entanglement, and Interference, adding extra qubits to a quantum machine increases its computing power exponentially. While there are still challenges like maintaining quantum states and manipulating qubits reliably to reduce errors in calculations, overcoming them would make Quantum Computers commercially feasible.

The revenues for the global quantum computing market are projected to surpass 2.5 billion U.S. dollars by 2029.?

The race for “Quantum Supremacy” has already begun, when quantum computers become powerful enough to complete certain calculations that classical supercomputers cannot accomplish and therefore surpass the capabilities of classical computing. It will be interesting to be a part of this new paradigm change and start learning more about the technology and develop algorithms that run on Quantum Computer to show their worth.

References:

1 https://search-proquest-com.ezproxy.babson.edu/docview/2265863731?accountid=36796

2 https://www.investopedia.com/terms/m/mooreslaw.asp

3 https://www.technologyreview.com/2020/02/24/905789/were-not-prepared-for-the-end-of-moores-law/

? https://www.bcg.com/publications/2019/quantum-computers-create-value-when.aspx

? https://builtin.com/hardware/quantum-computing-applications

? https://www.technologyreview.com/2018/03/27/67122/ibms-dario-gil-says-quantum-computing-promises-to-accelerate-ai/

? Statista. (November 6, 2019). Forecast size of the quantum computing market worldwide from 2019 to 2029 (in million U.S. dollars)* [Graph]. In Statista. Retrieved May 08, 2020, from https://www-statista-com.ezproxy.babson.edu/statistics/1067216/global-quantum-computing-revenues/