Quantum Computing: A Beginner’s Guide to the Future of Computing

Imagine a computer that can solve in minutes what would take today’s supercomputers hundreds of years. This is the promise of quantum computing. By leveraging the unique properties of quantum mechanics, quantum computers can perform calculations at speeds and scales previously unimaginable.

Early Computers

The abacus and slide rule were some of the first tools used to perform calculations faster than paper. The introduction of electricity led to innovations that brought about the development of binary switches called transistors.

The best way to understand a transistor is to picture a light switch. When the switch is on, power flows and the light turns on. When the switch is off, there is no power and therefore no light. Traditional or classical computers work by using an array of transistors to determine voltage levels. If the voltage is high or low, it is assigned a binary value of 1 or 0 essentially, whether the light is on or off.

This value of 1 or 0 is called a bit, or a binary digit. A set of eight bits can represent 256 possible values. These values are converted to human-readable characters using coding schemes such as UTF-8, which is used by most computers today. For instance, the binary equivalent of ‘A’ in UTF-8 is 01000001.

The size of the array of bits is directly proportional to the size of the workload a processor can handle. Data moves from long-term storage on disks through a bus and is placed in different caches before being passed to the processor for computation. This data movement and calculation occur billions of times per second in every modern processor.

The use of transistors instead of slide rules and paper was not only a technological improvement that enhanced calculation speed but also a revolutionary step that led to the development of new sciences and had a significant impact on society.

Modern Computer Basics

Today’s typical classical computer is a collection of buses, caches, and processors that serve to store, move, and calculate data. Each processing unit, or core, works through data as quickly as possible while following a sequential order. Buses and processors are added to increase the number of calculations that can be done in parallel.

However, there are still limitations to how quickly even large clusters of computers can solve complex problems. For example, breaking the strongest encryption systems today would take hundreds of years, even for the best modern classical computers. Similarly, although a processor may perform billions of calculations per second, each core can only handle one calculation at a time.

After understanding how modern high-performance classical systems function, we can now explore how quantum computers operate differently.

Quantum Computers

While classical computers perform simple calculations very quickly, quantum computers rely on the physics of entangled quantum states to perform exponentially more calculations without needing many parallel processors. What could take hundreds of years to calculate can be determined in minutes.

Quantum computers don’t just perform calculations faster potentially about 160 million times faster than today’s supercomputers depending on the problem but they also compute in an entirely new way.

What Is Quantum Computing?

Quantum computers are built using subatomic particles such as electrons and photons to represent computation. They are not simply the next generation of supercomputers; they represent a completely new type of computing the first major shift since the invention of classical computers.

Quantum computers are programmable and can solve certain problems far beyond the capacity of even the most powerful classical supercomputers.

The power of a quantum computer comes from the qubit, the basic unit of quantum information. A qubit can exist in a state of superposition, where it can be both 0 and 1 at the same time. Another remarkable feature of qubits is entanglement, for which the 2022 Nobel Prize in Physics was awarded. Entanglement allows qubits to be linked or correlated so that when one is measured, the state of the other is instantly determined.

This means that computations involving entangled qubits can occur almost instantaneously. The more qubits that are entangled, the more calculations can be performed. Moreover, quantum systems do not just compute faster they provide results based on probabilities, offering answers in a fundamentally different way.

Technical leaders are interested in understanding how they can apply this exponential computational power, which is expected to transform technology and society as profoundly as the semiconductor once did.

How Does Quantum Computing Work?

Quantum computers are programmable, much like classical computers, and often use extensions or libraries of Python as their programming languages. However, the major components of classical computers such as cache or memory have not yet been built in the same way for quantum systems.

Quantum computers operate based on quantum mechanical principles to solve certain problems much faster than conventional computers. The quantum world, composed of subatomic particles, follows entirely different laws of physics.

Quantum computing uses particles like electrons or photons. A qubit, or quantum bit, can exist in superposition, meaning it can be in both the 0 and 1 states simultaneously. These qubits can also be entangled, entering a quantum correlation where measuring one instantly determines the state of the other.

Differences Between Quantum and Classical Computers

Quantum Computers	Classical Computers
Calculate with qubits, which can be both 0 and 1 at the same time	Calculates with bits, which can be either 0 or 1
Computing power increases exponentially in proportion to the number of qubits	Computing power increases linearly with the number of transistors
Has high error rates	Has low error rates
Ultracold temperatures necessary for working	Works at room temperature
Well-suited for simulation, optimization, and artificial intelligence tasks	Well-suited for everyday tasks like accounting, word processing, video editing, etc.
Quantum Volume has been doubling every year since 2017. Quantum Volume is a measure that combines the number of qubits and the errors and in a sense measures the computing power of quantum computers.	Since 1975, following Moore’s law, the number of transistors has been doubling every 2 years. This though is expected to slow down as transistor miniaturization reaches atomic levels. Moore’s law indicates how the computing power of classical computers increases exponentially.

Timeline of Quantum Computing Algorithms Milestones

Over the past few decades, several advances have been made in terms of quantum algorithms that show exponential or quadratic speedups. These algorithms have demonstrated that indeed quantum computing can solve certain problems much faster than classical computers and, in fact, they could even tackle some problems that will never be possible to implement on a classical computer.

Key Milestones	Details
1981: Richard Feynman urges the world to build a quantum computer	Feynman proposes a framework for simulating the evolution of quantum systems. Feynman urges, “Nature isn’t classical…and if you want to make a simulation of nature, you’d better make it quantum mechanical…”
1992: Deutsch-Jozsa Algorithm	The Deutsch–Jozsa algorithm is a deterministic quantum algorithm and was the first proposed quantum algorithm with exponential speedup. David Deutsch and Richard Jozsa proposed it and showed that there can be advantages to using a quantum computer as a computational tool for a specific problem. It inspired other quantum algorithms that followed.
1994: Shor’s Algorithm	Peter Shor discovered an algorithm to factor large integers exponentially much faster than the best-known classical algorithm. Shor’s Algorithm can theoretically break many of the public-key cryptography systems in use today. Shor’s Algorithm demonstrated that a quantum algorithm would do factorization in a few hours, which would take several billion years for a classical computer. This paved the way for more intensive research to look for other problems where quantum computers could provide huge speedups.
1996: Grover’s Algorithm (also called the Quantum Search Algorithm)	Lov Grover discovered an algorithm that provides quadratic speedup for finding one specific element in an unstructured dataset. For large unstructured datasets, Grover’s algorithm provides substantial savings in search time.
2014: Variational Quantum Eigensolver (VQE)	VQE is a quantum algorithm for quantum chemistry, quantum simulations, and optimization problems. It is a hybrid algorithm that uses both classical computers and quantum computers to find the ground state of a given physical system. VQE is one of the most promising near-term applications for quantum computing. It was developed by Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, and Jeremy L. O’Brien.
2014: Quantum Approximate Optimization Algorithm (QAOA)	QAOA can be regarded as an application of VQE to solve combinatorial optimization problems on near-term quantum computers. The applications of QAOA are broad and far-reaching, and the algorithm’s performance is of great interest to the quantum computing research community.
2014: Quantum Approximate Optimization Algorithm (QAOA)	QAOA can be regarded as an application of VQE to solve combinatorial optimization problems on near-term quantum computers. The applications of QAOA are broad and far-reaching, and the algorithm’s performance is of great interest to the quantum computing research community.
2022: Quantum becomes mainstream in business applications	Building on the algorithms above, machine learning, optimization, and simulation are being applied to applications in areas like finance, retail, supply chain, automotive, and others. Businesses have begun exploring quantum computing and applying it to various business problems.SimulationPharma – Protein foldingChemistry – Material science and discoveryElectric Vehicle – Battery performanceFinance – Risk ModelingOptimizationSupply Chain – Route optimizationPharma – Drug performanceFinance – Portfolio optimizationAutomotive – Design optimizationArtificial IntelligenceHealthcare – Disease diagnosisAutomotive – Computer visionFinance – Credit card fraudSupply Chain – Clustering

Timeline of Quantum Computing Hardware and Software Milestones

How do we implement quantum algorithms? We need quantum systems to run quantum algorithms. Quantum systems have been growing exponentially over the past few years. Several advances have been made to demonstrate and implement quantum algorithms in practical settings. The availability of quantum computers has also prompted the industry to start looking for business use cases that can be implemented on these systems.

Key Milestones	Details
1981: Richard Feynman urges the world to build a quantum computer	Feynman proposes a framework for simulating the evolution of quantum systems. Feynman urges, “Nature isn’t classical…and if you want to make a simulation of nature, you’d better make it quantum mechanical…”
1996: DiVincenzo’s criteria	David P. DiVincenzo, from IBM, proposes a list of minimal requirements for creating a quantum computer.
1998: NMR Quantum Computer	First experimental demonstration of a quantum algorithm. A working 2-qubit NMR quantum computer is used to solve Deutsch’s problem by Jonathan A. Jones and Michele Mosca at Oxford University and shortly after by Isaac L. Chuang at IBM’s Almaden Research Center and Mark Kubinec at the University of California, Berkeley together with coworkers at Stanford University and MIT.
2001: Physical implementation of key quantum algorithm using quantum mechanics	First execution of Shor’s algorithm at IBM’s Almaden Research Center and Stanford University. The number 15 was factored in using 10¹⁸ identical molecules, each containing seven active nuclear spins.
2011: Quantum Annealing Computer Commercially Available	D-Wave developed quantum annealing and introduced their product called D-Wave One. This was the first commercially available quantum computer.
2016: Quantum Computer accessible over the cloud	IBM Quantum Lab enables cloud-based quantum computing. IBM provides an online interface to their superconducting systems. The system is immediately used to publish new protocols in quantum information processing.
2017: Qiskit Software Development Kit released	Qiskit was founded by IBM Research to allow software development for their cloud quantum computing service, IBM Quantum Lab. The primary version of Qiskit uses the Python programming language.
2019: Quantum Supremacy Experiment	Google claimed quantum supremacy using their quantum computer. Using a 53-qubit processor, they demonstrated that they could sample one instance of a quantum circuit a million times in just 200 seconds, a task that would take 10,000 years on the best supercomputers. This claim was disputed by IBM, but still, it remains one of the most interesting experiments to show gains on actual quantum computers.
2022: IBM launches 433-qubit Quantum Computer	IBM, which published its roadmap in 2020, has been on track so far and plans to deliver a 4000+ qubit quantum computer by 2025.

Quantum Computing Providers

There are already several providers that sell access to quantum computers, and as the technology improves, more may enter the field:

Amazon Bracket
Annealing Cloud Web
Atom Computing
Azure Quantum
AQT
Baidu
Bleximo
ColdQuanta
D-Wave Systems
Honeywell Quantum Solutions
IBM Quantum Computing
IonQ
IQM Quantum Computers
Qutech Quantum Inspire
Quantinuum
Rigetti Computing
Xanadu

In addition to individual cloud providers, platforms such as Strangeworks act as consolidation services.

Building a Private Quantum Computer

Quantum computers, which are proprietary systems, have not been developed rapidly due to their high costs, complexity, and the secrecy surrounding them. Building the necessary equipment typically requires billions of dollars in investment. However, this cost is expected to decrease as technology improves similar to the evolution of classical computers.

Hybrid Systems

Because of the cost and complexity of quantum computing, many organizations will use conventional computers for most computations and only pass specific calculations to a quantum computer when it can perform them more efficiently. This approach allows businesses to take advantage of quantum computing while paying for quantum resources only when needed. This could involve on-premises quantum computers or access through providers offering Quantum as a Service (QaaS).

Use Cases

Financial Services

Investment Risk Analysis
Risk and liability are central to any investment, whether in business or government. Due diligence involves assessing not only the current state of a project but also its future economic environment. Factors such as interest rates, stock prices, and exchange rates all influenced by broader economic conditions play key roles in investment decisions.
Classical computers often struggle to process all these changing parameters quickly enough. Quantum computing can handle large, dynamic datasets and incorporate real-time changes, enabling faster and more accurate analysis. This agility allows investors to make better-informed decisions and adjust positions before losses occur.

Insurance
The insurance industry relies on understanding risk. Actuaries constantly refine models and risk tables as population data and other factors evolve. Quantum computing can manage many interacting variables at once, helping insurers better understand risk and adjust pricing models more precisely.

Fraud Detection and Offer Recommendations
Balancing portfolios and detecting fraud require massive computing resources, which can slow decision-making. Quantum computing allows even extremely large datasets to be analyzed in seconds or minutes. This enables faster responses to market shifts, reduces losses from fraud, and improves offer recommendations for customers.

Manufacturing

New Material Discovery
Discovering new materials requires immense computing power and time. Quantum computing can significantly reduce both while handling a larger number of factors per material. It can simulate electron integrals, optimize coefficients, and invert matrices faster and more accurately avoiding false configurations and focusing only on promising candidates.

Fabrication Optimization
From airplanes to tractors, manufacturers strive to make vehicles that are light, strong, and efficient. Using simulations powered by quantum computing, manufacturers can optimize designs to reduce excess durability margins, resulting in lighter and safer vehicles that consume less fuel.

Supply Chain
Supply chains often maintain excess inventory to meet demand, leading to inefficiencies. Quantum computing can analyze production timing, material availability, and assembly schedules to optimize manufacturing flow. This could reduce time and overhead costs by 30% or more.

Healthcare

Modern healthcare depends on computing to diagnose illnesses, personalize treatments, manage resources, and optimize insurance models. Quantum computing can greatly enhance these processes through faster and more accurate computation.

Advanced Accelerated Diagnosis
Early and accurate diagnosis is vital to improving patient outcomes and reducing costs. Quantum-enhanced machine learning can improve medical image scans and genomic analysis, allowing earlier detection and customized treatment. As more data is added, predictive healthcare becomes possible, further improving outcomes.

Treatment Discovery
Developing new drugs costs billions of dollars and takes years of testing. Quantum computing can simulate molecular interactions millions of times faster than classical systems, leading to quicker discoveries, fewer side effects, and lower development costs.

Mineral Exploration

Drilling Locations
Oil and gas exploration involves analyzing vast datasets with complex algorithms. Classical systems often cannot process all the data efficiently. Quantum computers can handle these analyses quickly, reducing the number of unused boreholes, lowering costs, and increasing energy availability.

Extraction Optimization
Once resources are located, extraction methods must be optimized. Small geological differences can drastically affect costs. Quantum computing enables large-scale data analysis and process optimization to determine the most efficient extraction strategies before operations begin.

Refining Process
Oil refineries are costly to build and operate, and many use outdated processes that lead to waste. Quantum computing, combined with AI and machine learning, can optimize refining processes to increase fuel yield, reduce by-products, and even find new uses for waste materials. This could make refining difficult crude sources—like high-sulfur oil—more practical and profitable.

Chemical and Alloy Design
About 60% of a barrel of oil becomes fuel, while the rest is used for plastics and chemicals. Designing new products is time-consuming and often wasteful. Quantum computing enables molecular-level simulations that accelerate the creation of new compounds.
Similarly, new alloys can be designed based on intended performance. For instance, optimizing steel for nuclear reactors could be done through simulation rather than trial and error, increasing safety and reducing costs.

Agriculture

Harvest Optimization
Factors like moisture, temperature, wind patterns, and microclimates affect when crops should be harvested. Quantum computing can analyze large data sets to reveal hidden correlations, improve pattern recognition, and predict optimal harvest times, reducing waste and increasing yield.

Hybridization
Hybridizing plants and animals through traditional experiments is costly and time-consuming. Quantum computing allows genetic hybridization to be simulated digitally, testing more possibilities faster and at lower cost.

Climate Effect Forecasting
Climate is a complex system affected by countless variables. Even predicting next-day weather is difficult. Quantum computers can analyze vast environmental datasets to improve weather and climate forecasting, helping farmers optimize planting and harvesting decisions.

Logistics

Freight Forecasting
Most goods require transportation. In 2020, over ten billion metric tons were shipped globally. Problems like route optimization (the Traveling Salesman Problem) grow exponentially complex when considering factors like weather and inventory. Quantum computing can process these variables efficiently, improving routing and forecasting, saving billions in costs, and reducing emissions.

Disruption Management
Weather, traffic, equipment failure, and politics can all disrupt optimized logistics. Quantum computers can rapidly recalculate routes and schedules in real time, minimizing fuel waste, delays, and operational costs.

Vehicle Routing
Over half of shipping costs occur in the “last mile” of delivery. Quantum computing can further optimize delivery routes by considering real-time conditions.
In 2012, UPS used its ORION system to plan mostly right-turn routes:

“By building efficient, right-turn loops, ORION reduces fuel consumption by over 10 million gallons, carbon emissions by 100,000 metric tons, and avoidable costs by $300–$400 million each year.”
— Christopher Savoie, 2021

Further optimization through quantum computing could yield even greater savings. A 10% improvement in U.S. trucking efficiency alone would eliminate 45 million pounds of carbon emissions annually. Applying similar methods to public and private transportation could save billions and reduce global emissions.

Conclusion

Quantum computing is a new paradigm that uses the principles of quantum mechanics such as superposition, entanglement, and quantum parallelism to perform computations far beyond the capacity of classical computers. It has potential applications in cryptography, drug discovery, material science, financial modeling, and artificial intelligence, marking a leap from linear to exponential improvement in efficiency and problem-solving.

As the field develops, major technology firms and research institutions are investing heavily in scalable quantum systems, error correction methods, and real-world applications. Although current quantum computers remain experimental, progress in algorithms, hybrid quantum-classical systems, and commercial availability will bring significant changes across industries.

The future of quantum computing looks bright. As the technology matures, it will revolutionize industries, open new research avenues, and reshape the future turning problems once considered unsolvable into easily addressable ones. Quantum computing is not just the next step; it is the next revolution that will define the future of innovation.