In 2023, IBM introduced its 433-qubit Osprey processor, while Google’s Sycamore demonstrated quantum supremacy by completing a calculation in just 200 seconds—something that would take the fastest classical supercomputers over 10,000 years. These breakthroughs are not just milestones; they signal a monumental leap from theory to reality. Quantum computing is here, and it’s set to revolutionize artificial intelligence and reshape entire industries.

The concept of quantum computing emerged in the early 1980s when physicist Richard Feynman proposed harnessing quantum mechanical phenomena for computation. His insight came from the difficulty of simulating quantum systems using classical computers. Shortly after, David Deutsch described the first theoretical quantum computer, introducing the world to the possibility of solving complex problems exponentially faster than classical computers. However it was in 1994 that MIT mathematician Peter Shor published one of the first practical real-world applications for a quantum machine. His algorithm for integer factorization showed how a quantum mechanical computer could potentially break the most advanced cryptography systems of the time—some of which are still used today. Shor’s findings demonstrated a viable application for quantum systems, with profound implications for fields beyond just cybersecurity.

Quantum computing leverages the fascinating principles of quantum mechanics – the branch of physics that deals with the behaviour of subatomic particles. – electrons, photons, quarks, neutrinos and more. Now, photons are quantum particles that make up light. Experiments by scientists in the early 20th century showed how a photon with high enough frequency can knock an electron free from the surface of a metal. Another way to understand this is that just trying to get a better look at a subatomic particle would change the momentum of the electron.

This emerging technology spans multiple scientific domains, including the development of quantum hardware and the creation of specialized quantum algorithms. Though the field is still maturing, quantum computers promise to tackle computational challenges that lie far beyond the reach of even our most advanced classical supercomputers.

Quantum computers harness the counterintuitive behaviors of subatomic particles to perform certain types of calculations with unprecedented efficiency. While classical computers use binary bits – zeros and ones – to store and process data, quantum computers leverage quantum bits or qubits, which can hold far more information due to their ability to exist in superposition (the ability to exist in multiple states at once). This essentially means that while a binary bit holds a fixed value (0 or 1), a qubit can be both 0 and 1 at the same time. This is the strange and nearly inexplicable behaviour of quantum mechanics that this QC harnesses.

Quantum computing enhances AI by accelerating data processing and model training. Several specific advantages include:

• Enhanced Computational Power: Quantum algorithms solve complex problems faster, aiding AGI in processing large datasets and performing advanced computations.
• Efficient Problem-Solving: Optimization and simulation tasks, critical for AGI, are handled more effectively using QC.
• Improved Learning: Quantum Machine Learning (QML) speeds up AI training processes, helping AGI adapt across diverse tasks.
• Parallel Processing: QC’s ability to perform operations in parallel reduces training and inference times.
• Handling Complex Data Structures: Quantum systems process high-dimensional data efficiently, supporting AGI’s analytical capabilities.

Conversely AI plays a key role in the development and optimization of quantum systems:

• Error Correction and Noise Reduction: Machine learning models predict and mitigate quantum errors, improving system reliability.
• Quantum Algorithm Optimization: AI identifies and optimizes quantum circuits, reducing gate operations and error rates.
• Device Calibration: AI automates the calibration of quantum devices, ensuring peak performance.
• Hybrid Quantum-Classical Systems: AI balances task distribution between quantum and classical systems, maximizing computational efficiency.

Quantum Computing has recently reached a crucial milestone with IBM aiming for a 4,000+ qubit system by 2025. Meanwhile, governments worldwide are investing heavily in quantum research, recognizing its potential impact on national security and technological advancement. China has reportedly invested $10 billion in a national quantum laboratory, while the US has committed $1.2 billion to the National Quantum Initiative. Though a fully functional, error-corrected quantum computer remains years away, the progress in recent years suggests we’re moving steadily toward this revolutionary technology.

In 2023, private and corporate investments in quantum technology (QT)* start-ups experienced a 27% decline, totaling $1.71 billion, down from $2.35 billion in 2022. This decrease was less severe than the 38% drop observed across all global start-up investments during the same period. Notably, 62% of the QT funding was directed toward companies established five or more years ago, indicating a strategic focus on scaling mature start-ups.

In contrast, public investments in QT surged by over 50% in 2023, accounting for nearly a third of all QT investments. Countries such as Germany, the United Kingdom, and South Korea announced substantial new funding, bringing total global public investment to approximately $42 billion.

The quantum computing (QC) industry is undergoing rapid growth, as reflected in a recent McKinsey survey of industry leaders. The survey highlights a shift in the size and scale of companies in the sector: firms with fewer than six employees have increased from 0% to 11%, while those with over 100 employees have risen from 9% to 39%. This indicates both the emergence of new players and the scaling up of established companies, signaling progress toward fault-tolerant quantum computing (FTQC).

Fault-tolerant QC refers to a system capable of performing quantum computations reliably, even in the presence of errors. Unlike today’s noisy intermediate-scale quantum (NISQ) systems, FTQC would use error-corrected qubits to process information more accurately and execute long computations without failure. Over 65% of surveyed experts expect FTQC to be achieved by 2030, though debates persist regarding whether “true” fault tolerance requires large-scale logical qubits or whether high-error tolerance with minimal correction suffices for useful applications.

Progress toward FTQC is evident in the rise of practical QC use cases, with 55% of respondents now having an in-production use case, up from 33% the prior year. Sectors like aerospace, automotive, oil and gas, and medical technology are among the early adopters likely to benefit from these advancements.

In India, the government has taken significant steps to bolster the quantum technology sector. The National Quantum Mission, approved by the Union Cabinet with a budget of ₹6,003.65 crore (approximately $730 million) for 2023–2031, aims to advance quantum technologies in alignment with national initiatives like Digital India and Make in India. This mission focuses on developing quantum computing, communication, sensing, and materials, with the establishment of Thematic Hubs (T-Hubs) at premier institutions such as the Indian Institute of Science (IISc) Bengaluru and various Indian Institutes of Technology (IITs). These efforts underscore India’s commitment to scaling established start-ups and fostering innovation in the quantum technology landscape.

Quantum computers can perform certain calculations exponentially faster than classical computers. This capability becomes particularly crucial for problems like cryptography, drug discovery, climate modeling, and optimization challenges that are practically impossible for traditional computers to solve. This makes quantum computing the potential key to breakthroughs in a number of critical industries.

• Pharmaceuticals: Quantum computers that can simulate molecular behavior and biochemical reactions could greatly expedite the research and development of life-saving new drugs and medical treatments.
• Chemistry: Quantum computing might also provide undiscovered solutions for mitigating dangerous or destructive chemical byproducts. It could lead to development of improved catalysts that provide alternatives for petrochemicals or better processes for the carbon breakdown necessary for combating climate-threatening emissions.
• Machine learning: With AI and ML inviting more and more investment, researchers are testing the limits of our existing hardware by pushing AI models to new extremes. Evidence suggests that some quantum algorithms may be able to assimilate datasets in a new way, expediting the finding of a solution to some machine learning problems.

Technological: The primary challenge in quantum computing lies in maintaining quantum coherence – keeping qubits in their quantum state long enough to perform calculations. Environmental factors like temperature, electromagnetic radiation, and physical vibrations can cause “decoherence,” destroying the quantum properties essential for computation. Engineers must also tackle the challenge of scaling up quantum systems while maintaining precise control over individual qubits. Error correction remains another significant hurdle, as quantum systems are inherently prone to errors that must be detected and corrected without disturbing the quantum state.

Scalability: Challenges persist, including limited access to advanced hardware, low awareness of QT applications, and inadequate interdisciplinary collaboration between academia and industry. To overcome these hurdles, coordinated innovation clusters are emerging globally. These partnerships between researchers, industry leaders, and governments aim to advance QT development, manage intellectual property, and drive regional technological value creation.

A sustainable quantum ecosystem depends on three pillars: talent development, effective collaboration, and realistic expectations.

Talent Development
Building a skilled workforce requires expanding the talent pipeline beyond traditional physics PhDs. Programs introducing quantum concepts at earlier educational stages can prepare students for the unique challenges of QC.

Talent development in quantum computing saw significant progress in 2023, with 367,000 graduates earning QT-relevant degrees. The number of universities offering QT programs grew by 8.3% to 195, and those offering master’s degrees increased by 10% to 55. The European Union and the United Kingdom lead in graduate numbers and density, respectively.

Collaboration Models
Strong partnerships among academia, industry, and governments are essential for advancing QC. Reducing bureaucratic barriers, fostering open-source tools, and securing venture capital will drive innovation and enable practical applications.

Managing Expectations
Clear communication about the current state and future potential of QC is crucial to avoid disillusionment. Transparent updates and realistic goal-setting will maintain trust and ensure that stakeholders remain engaged in advancing the field.

Focusing on these priorities will create an ecosystem capable of supporting QC’s long-term growth and delivering transformative technologies.

Quantum computing stands at a pivotal moment in its evolution. While companies have achieved significant milestones, the industry is moving from experimental phases toward practical applications with India throwing its hat in the ring through its National Quantum Mission.

With talent in the field only set to grow further, experts are more than hopeful that fault-tolerant quantum computing will be achieved by 2030, which would mark a revolutionary breakthrough. While challenges persist, the increasing number of practical use cases across industries and the growing ecosystem of researchers, industries, and governments suggest we’re steadily advancing toward a quantum-enabled future.