Breaking Down Quantum Computing
Quantum computing is no longer confined to theoretical research or science fiction. It’s becoming a transformative force poised to redefine the foundations of computing, data processing, and decision-making. It offers a leap forward through its unique computing paradigm driven by quantum mechanics and will enable breakthroughs in simulating nature, mathematical optimization, machine learning, and more.
What is Quantum Computing?
At its core, all computation is inherently physical. Whether it is a digital computer, a human brain, an abacus, a DNA computer or a quantum computer, they all rely on physical objects and processes to perform the two basic tasks in a computer, i.e. data representation and data processing.
For example, ENIAC (Electronic Numerical Integrator and Computer), the first general-purpose electronic digital computer was developed in 1940s and used a decimal system (0-9) to represent real world data, such as pixels of an image of a tree and subsequently process them. It made use of vacuum tubes to realize this representation and electronic circuits to count pulses, hence performing arithmetic operations like addition, subtraction, multiplication, division, and square root calculations physically.
Today, digital computers such as CPUs, GPUs, or supercomputers use binary digits or bits (0 or 1) to represent information and then transform it using binary logic circuits. The memory registers representing the information and the binary logic circuits applying the required transformation require transistors which act as very tiny switches and operate on principles of classical semi-conductor physics.
Quantum computers, on the other hand, generally use a 2-level quantum system, quantum bits or qubits (or any n-level system, qudits) for information representation and subsequently process that information using quantum logic circuits. This new paradigm makes use of physics of atomic and subatomic particles to utilize superposition, entanglement, interference and tunneling for data processing. Currently, these qubits are physically realized using superconducting circuits, Nitrogen vacancy (NV) centers in diamonds, electron spins among others. The quantum logic is achieved by applying energy pulses to manipulate the energy levels of atomic and sub-atomic particles.
But why a new type of computer? We can ask the same question about GPUs or Audio processors (APU). Why did we build these new processors? Though current GPUs and APUs follow classical physics, they differ greatly in their architecture and basic instructions and hence their effective applications i.e. Image processing and AI for GPUs and Audio processing for APUs.
Similarly, quantum computers of certain types can be considered as universal computers, i.e. simulating the functionality of other computers given enough resources and time (say using them as calculators), are particularly suited to solving specific problem types that would take classical computers an impractical amount of time (sometimes millions of years) to solve. These include tasks like modeling quantum chemistry, solving massive optimization problems, and breaking large cryptographic keys. Disruptive benefits can be seen in solution quality and energy consumption as well. They will work as co-processors with the current compute infra to solve such problems.
One additional aspect we will notice when looking at quantum computing today is the sheer variety we encounter. Differences can be observed in qubit modalities such as spin based, neutral atom based or superconducting based quantum computers and in computing models such as Gate based and Adiabatic quantum computers.
We are currently racing towards small scale error-corrected quantum computing era and use of hybrid quantum classical systems and quantum inspired classical systems to enhance our computing workflows. These machines are available on public clouds such as AWS, Azure and GCP today as well as on private clouds such as those offered by Rigetti, D-Wave etc.
How Quantum Computing Can Disrupt the World
The implications of quantum computing adoption in the real world could be profound and far-reaching. As technology matures, it’s expected to impact virtually every industry that relies on complex data, high-speed computation, or large-scale modeling. Here are some key areas where quantum computing is already showing game-changing potential.
1. Simulating Nature
The killer application of quantum computing, simulation of nature, is why quantum computing was envisioned by the pioneers in first place. Simulating molecular behavior is a complex computational task and classical computers fall short of modelling and predicting this behavior precisely at scale. Quantum computers are the right tools to achieve this goal, enabling faster and more efficient development of new drugs, chemicals, and materials.
2. Mathematical Optimization
Optimization is at the heart of many business decisions, from supply chain logistics to portfolio management and route planning. Quantum algorithms can solve complex optimization problems more efficiently than classical methods. This could drastically improve efficiency, reduce costs, and unlock new capabilities in industries such as transportation, energy, and finance.
3. Artificial Intelligence and Machine Learning
The mathematical framework utilized by quantum computing has the potential to significantly accelerate machine learning algorithms. This framework implemented through quantum enhanced machine learning enables better representation and exploration of data and can provide more powerful and accurate models. Tasks like clustering, classification, and regression could benefit from increased speed and precision, especially for high-dimensional and noisy data.
4. Cybersecurity and Cryptography
Current encryption methods such as RSA and ECC rely on the difficulty of factoring large numbers, something classical computers struggle with. Quantum algorithms like Shor’s algorithm could factor these large numbers exponentially faster, potentially rendering existing encryption schemes obsolete. This looming challenge is pushing the development of post-quantum cryptography, and organizations must prepare now to transition to quantum-resilient security.
Mphasis and Quantum Computing
Mphasis, a global leader in cloud and cognitive services, is actively investing in and advancing the field of quantum computing. Through its innovation arm, Next Labs, Mphasis is building quantum-enabled solutions aimed at solving real-world business challenges.
Quantum Strategy and Services
Mphasis provides a suite of quantum computing services, including:
Mphasis, a global leader in cloud and cognitive services, is actively investing in and advancing the field of quantum computing. Through its innovation arm, Next Labs, Mphasis is building quantum-enabled solutions aimed at solving real-world business challenges.
● Quantum Readiness Assessments to identify where quantum computing can create immediate or future value.
● Client Workshops that help map business problems to potential quantum solutions.
● Algorithm Development and Implementation that blends quantum and classical computing to build hybrid, scalable solutions.
At the heart of Mphasis’ approach is the Energy Optimized Network(EON), a patented hybrid framework that combines classical neural networks with quantum circuits to maximize efficiency and accuracy in tasks such as classification and pattern recognition.
Real-World Impact: Quantum and AI
In a compelling use case, Mphasis helped a major US healthcare client enhance predictions for customer engagement with a loyalty program. By using quantum feature transformation and mapping classical datasets into quantum-enhanced representations, the solution significantly improved predictive accuracy and effectively streamlined the process. This illustrates how Mphasis is leveraging quantum capabilities not just theoretically, but practically, to address critical business needs.
Patent Milestone
In April 2025, Mphasis, was awarded a US patent for a unique system that optimizes classical data for quantum processing, enabling scalable and efficient quantum machine learning. The patented approach minimizes the number of required qubits and improves the convergence of quantum models, making it well-suited for real-world implementation on today’s noisy intermediate-scale quantum (NISQ) devices.
Conclusion
Quantum computing represents the next frontier in technology. Its ability to process massive amounts of information in fundamentally new ways opens opportunities previously out of reach. While it won’t replace classical computing, it will augment and elevate what’s possible, especially in data-intensive and computationally demanding fields.
Mphasis is leading the charge by not just exploring quantum theory but building tangible solutions that bridge quantum innovation with real business value. As we stand at the dawn of the quantum era, organizations that embrace and prepare for this paradigm shift will be best positioned to lead in a quantum-powered future.
