Quantum computers could eventually help researchers develop medicines, improve financial modelling, strengthen digital security and produce more accurate climate simulations. However, the technology remains at an early stage, and many of its proposed applications have yet to demonstrate practical benefits.
The technology attracted international attention in October 2019 when Google announced that its Sycamore quantum processor had completed a specialised calculation in about 200 seconds, a task the company estimated would take a state-of-the-art classical supercomputer approximately 10,000 years.
The experiment, published in the journal Nature, involved a processor designed with 54 qubits, although the researchers reported that the calculation used 53 functioning qubits. Google described the achievement as quantum supremacy, a term used to describe a quantum computer outperforming classical computers on a particular computational task.
The announcement, however, attracted criticism from IBM researchers, who argued that an improved classical computing approach could complete the same task in approximately 2.5 days rather than 10,000 years. The disagreement highlighted the difficulty of comparing quantum processors with conventional supercomputers. IBM Quantum Computing Blog
More importantly, the experiment did not demonstrate that quantum computers could immediately outperform conventional machines in everyday applications. Its significance was that it showed the potential of a different approach to computing.
Unlike conventional computers, which process information using bits represented as zeros or ones, quantum computers use quantum bits, known as qubits. These can exploit properties such as superposition and entanglement to perform certain calculations in ways that are difficult for conventional machines.
The potential applications extend across several industries.
1. Digital security and encryption
Quantum computing could have significant implications for cybersecurity, particularly the encryption systems used to protect online communications, financial transactions and confidential information.
Some widely used encryption methods rely on mathematical problems that are extremely difficult for conventional computers to solve. A sufficiently powerful quantum computer could potentially solve certain of these problems much faster, threatening the security of existing systems.
However, this does not mean quantum computers can currently crack ordinary passwords within seconds. Today’s quantum machines lack the scale and reliability needed to break widely used encryption.
Researchers are developing an alternative known as post-quantum cryptography. These encryption methods use mathematical problems designed to remain difficult for both conventional and quantum computers.
The US National Institute of Standards and Technology (NIST) has developed standards for post-quantum cryptography to help organisations prepare for future security threats. NIST+2
2. Drug discovery and medical research
Drug development is another area where quantum computing could make a difference.
Understanding how molecules interact is important when developing medicines. Researchers use computational models to study molecular structures, chemical reactions and interactions that influence how potential drugs work.
Conventional computers can struggle to simulate certain complex quantum-level interactions, particularly in large molecular systems. Quantum computers could eventually provide more effective ways of modelling these processes.
This could help scientists investigate potential drug compounds, understand molecular behaviour and identify promising candidates for further laboratory testing.
A review published in Briefings in Bioinformatics in 2026 examined how quantum computing could contribute to drug discovery, including molecular modelling and simulations. It also highlighted the limitations of current hardware and the need for conventional and quantum computing systems to work together.
The technology is not expected to replace laboratory experiments or clinical trials. Its potential contribution is to improve particular stages of research and, eventually, reduce some of the computational challenges involved in developing medicines.
3. Artificial intelligence
Artificial intelligence (AI) depends on processing large amounts of information to identify patterns, make predictions and perform complex tasks.
Quantum computing could eventually support some of these processes through an emerging field known as quantum machine learning.
Researchers are investigating whether quantum algorithms can improve particular machine-learning tasks, including optimisation, classification and data analysis.
Such developments could have applications in scientific research, financial modelling and other industries that rely on complex computational systems.
However, quantum computers will not automatically make AI systems faster or more intelligent. Many existing AI applications already run efficiently on conventional processors, and researchers have yet to establish broad, practical advantages for quantum machine learning.
For now, quantum computing is better understood as a potential addition to existing AI technologies rather than a replacement for conventional computing.
4. Financial services and investment
Financial institutions regularly use mathematical models to assess investment risks, estimate market movements and evaluate different financial scenarios.
One widely used technique is the Monte Carlo simulation, which runs numerous calculations using different possible outcomes to estimate the effects of uncertainty on financial decisions.
Quantum computing could improve some of these calculations by reducing the computational resources required to estimate certain probabilities and financial risks.
Researchers are also investigating applications in portfolio optimisation, financial modelling and derivative pricing.
A 2023 review published in Nature Reviews Physics examined the potential use of quantum algorithms in financial services, including stochastic modelling, optimisation and machine learning.
The researchers identified possible advantages in quantum Monte Carlo methods but noted that substantial technical work remains before these benefits can be realised in practical financial systems.
5. Climate modelling and environmental research
Climate scientists use computer models to examine interactions between the atmosphere, oceans, land and other components of the Earth’s climate system.
These models help researchers investigate temperature changes, rainfall patterns, extreme weather and the potential consequences of different greenhouse gas emissions scenarios.
Quantum computing could eventually assist with some of the complex calculations involved in climate modelling.
Researchers are exploring whether quantum algorithms could help solve mathematical equations used in climate simulations, improve optimisation processes and support the analysis of complex environmental systems.
A 2025 study published in Environmental Data Science examined opportunities for integrating quantum computing into climate modelling.
The research also identified important obstacles, including hardware limitations, short qubit coherence times and difficulties connecting quantum processors with existing high-performance computing systems.
Quantum computing is therefore a potential research tool, not an established solution for predicting climate change or reversing its effects.
The challenges ahead
Despite its potential, quantum computing faces significant engineering and scientific challenges.
Qubits are highly sensitive to environmental disturbances, which can introduce errors and affect calculations. Researchers are working to improve their stability, reduce errors and develop systems capable of performing more complex operations reliably.
Another challenge is identifying problems where quantum computers can deliver a measurable advantage over increasingly powerful conventional machines.
Google’s 2019 Sycamore experiment demonstrated progress in quantum computing, but translating that achievement into practical applications across medicine, finance, cybersecurity and climate research will require further advances.
For now, conventional computers remain essential to everyday computing. Quantum machines are being developed to complement them by tackling particular problems that are difficult to solve using existing technology.
