Marilyne Andersen, Director General, GESDA, speaks during an interview at the Swiss Embassy in New Delhi on March 6, 2026.
| Photo Credit: Shiv Kumar Pushpakar

Marilyne Andersen, Director General, GESDA, speaks during an interview at the Swiss Embassy in New Delhi on Friday. March 6, 2026.
| Photo Credit: Shiv Kumar Pushpakar

In recent years, the field of quantum computing has been experiencing fast growth, with technological advances and large-scale investments regularly making the news.

The United Nations has designated 2025 as the International Year of Quantum Science and Technology.

The stakes are high – having quantum computers would mean access to tremendous data processing power compared to what we have today. They won’t replace your normal computer, but having this kind of awesome computing power will provide advances in medicine, chemistry, materials science and other fields.

So it’s no surprise that quantum computing is rapidly becoming a global race, and private industry and governments around the world are rushing to build the world’s first full-scale quantum computer. To achieve this, first we need to have stable and scalable quantum processors, or chips.

What is a quantum chip?

Everyday computers – like your laptop – are classical computers. They store and process information in the form of binary numbers or bits. A single bit can represent either 0 or 1.

By contrast, the basic unit of a quantum chip is a qubit. A quantum chip is made up of many qubits. These are typically subatomic particles such as electrons or photons, controlled and manipulated by specially designed electric and magnetic fields (known as control signals).

Unlike a bit, a qubit can be placed in a state of 0, 1, or a combination of both, also known as a “superposition state”. This distinct property allows quantum processors to store and process extremely large data sets exponentially faster than even the most powerful classical computer.

There are different ways to make qubits – one can use superconducting devices, semiconductors, photonics (light) or other approaches. Each method has its advantages and drawbacks.

Companies like IBM, Google and QueRa all have roadmaps to drastically scale up quantum processors by 2030.

Industry players that use semiconductors are Intel and Australian companies like Diraq and SQC. Key photonic quantum computer developers include PsiQuantum and Xanadu.

Qubits: quality versus quantity

How many qubits a quantum chip has is actually less important than the quality of the qubits.

A quantum chip made up of thousands of low-quality qubits will be unable to perform any useful computational task.

So, what makes for a quality qubit?

Qubits are very sensitive to unwanted disturbances, also known as errors or noise. This noise can come from many sources, including imperfections in the manufacturing process, control signal issues, changes in temperature, or even just an interaction with the qubit’s environment.

Being prone to errors reduces the reliability of a qubit, known as fidelity. For a quantum chip to stay stable long enough to perform complex computational tasks, it needs high-fidelity qubits.

When researchers compare the performance of different quantum chips, qubit fidelity is one of the crucial parameters they use.

How do we correct the errors?

Fortunately, we don’t have to build perfect qubits.

Over the last 30 years, researchers have designed theoretical techniques which use many imperfect or low-fidelity qubits to encode an abstract “logical qubit”. A logical qubit is protected from errors and, therefore, has very high fidelity. A useful quantum processor will be based on many logical qubits.

Nearly all major quantum chip developers are now putting these theories into practice, shifting their focus from qubits to logical qubits.

In 2024, many quantum computing researchers and companies made great progress on quantum error corrections, including Google, QueRa, IBM and CSIRO.

Quantum chips consisting of over 100 qubits are already available. They are being used by many researchers around the world to evaluate how good the current generation of quantum computers are and how they can be made better in future generations.

For now, developers have only made single logical qubits. It will likely take a few years to figure out how to put several logical qubits together into a quantum chip that can work coherently and solve complex real-world problems.

What will quantum computers be useful for?

A fully functional quantum processor would be able to solve extremely complex problems. This could lead to revolutionary impact in many areas of research, technology and economy.

Quantum computers could help us discover new medicines and advance medical research by finding new connections in clinical trial data or genetics that current computers don’t have enough processing power for.

They could also greatly improve the safety of various systems that use artificial intelligence algorithms, such as banking, military targeting and autonomous vehicles, to name a few.

To achieve all this, we first need to reach a milestone known as quantum supremacy – where a quantum processor solves a problem that would take a classical computer an impractical amount of time to do.

Late last year, Google’s quantum chip Willow finally demonstrated quantum supremacy for a contrived task – a computational problem designed to be hard for classical supercomputers but easy for quantum processors due to their distinct way of working.

Although it didn’t solve a useful real-world problem, it’s still a remarkable achievement and an important step in the right direction that’s taken years of research and development. After all, to run, one must first learn to walk.

What’s on the horizon for 2025 and beyond?

In the next few years, quantum chips will continue to scale up. Importantly, the next generation of quantum processors will be underpinned by logical qubits, able to tackle increasingly useful tasks.

While quantum hardware (that is, processors) has been progressing at a rapid pace, we also can’t overlook an enormous amount of research and development in the field of quantum software and algorithms.

Using quantum simulations on normal computers, researchers have been developing and testing various quantum algorithms. This will make quantum computing ready for useful applications when the quantum hardware catches up.

Building a full-scale quantum computer is a daunting task. It will require simultaneous advancements on many fronts, such as scaling up the number of qubits on a chip, improving the fidelity of the qubits, better error correction, quantum software, quantum algorithms, and several other sub-fields of quantum computing.

After years of remarkable foundational work, we can expect 2025 to bring new breakthroughs in all of the above.

This article is republished from The Conversation under a Creative Commons license. Read the original article here.

Image for Representation
| Photo Credit: Getty Images/iStockphoto

Imagine the tap of a card that bought you a cup of coffee this morning also let a hacker halfway across the world access your bank account and buy themselves whatever they liked. Now imagine it wasn’t a one-off glitch, but it happened all the time: imagine the locks that secure our electronic data suddenly stopped working.

This is not a science fiction scenario. It may well become a reality when sufficiently powerful quantum computers come online. These devices will use the strange properties of the quantum world to untangle secrets that would take ordinary computers more than a lifetime to decipher.

We don’t know when this will happen. However, many people and organisations are already concerned about so-called “harvest now, decrypt later” attacks, in which cybercriminals or other adversaries steal encrypted data now and store it away for the day when they can decrypt it with a quantum computer.

As the advent of quantum computers grows closer, cryptographers are trying to devise new mathematical schemes to secure data against their hypothetical attacks. The mathematics involved is highly complex – but the survival of our digital world may depend on it.

‘Quantum-proof’ encryption

The task of cracking much current online security boils down to the mathematical problem of finding two numbers that, when multiplied together, produce a third number. You can think of this third number as a key that unlocks the secret information. As this number gets bigger, the amount of time it takes an ordinary computer to solve the problem becomes longer than our lifetimes.

Future quantum computers, however, should be able to crack these codes much more quickly. So the race is on to find new encryption algorithms that can stand up to a quantum attack.

The US National Institute of Standards and Technology has been calling for proposed “quantum-proof” encryption algorithms for years, but so far few have withstood scrutiny. (One proposed algorithm, called Supersingular Isogeny Key Encapsulation, was dramatically broken in 2022 with the aid of Australian mathematical software called Magma, developed at the University of Sydney.)

The race has been hotting up this year. In February, Apple updated the security system for the iMessage platform to protect data that may be harvested for a post-quantum future.

Two weeks ago, scientists in China announced they had installed a new “encryption shield” to protect the Origin Wukong quantum computer from quantum attacks.

Around the same time, cryptographer Yilei Chen announced he had found a way quantum computers could attack an important class of algorithms based on the mathematics of lattices, which were considered some of the hardest to break. Lattice-based methods are part of Apple’s new iMessage security, as well as two of the three frontrunners for a standard post-quantum encryption algorithm.

What is a lattice-based algorithm?

A lattice is an arrangement of points in a repeating structure, like the corners of tiles in a bathroom or the atoms in a diamond crystal. The tiles are two dimensional and the atoms in diamond are three dimensional, but mathematically we can make lattices with many more dimensions.

Most lattice-based cryptography is based on a seemingly simple question: if you hide a secret point in such a lattice, how long will it take someone else to find the secret location starting from some other point? This game of hide and seek can underpin many ways to make data more secure.

A variant of the lattice problem called “learning with errors” is considered to be too hard to break even on a quantum computer. As the size of the lattice grows, the amount of time it takes to solve is believed to increase exponentially, even for a quantum computer.

The lattice problem – like the problem of finding the factors of a large number on which so much current encryption depends – is closely related to a deep open problem in mathematics called the “hidden subgroup problem”.

Yilei Chen’s approach suggested quantum computers may be able to solve lattice-based problems more quickly under certain conditions. Experts scrambled to check his results – and rapidly found an error. After the error was discovered, Chen published an updated version of his paper describing the flaw.

Despite this discovery, Chen’s paper has made many cryptographers less confident in the security of lattice-based methods. Some are still assessing whether Chen’s ideas can be extended to new pathways for attacking these methods.

More mathematics required

Chen’s paper set off a storm in the small community of cryptographers who are equipped to understand it. However, it received almost no attention in the wider world – perhaps because so few people understand this kind of work or its implications.

Last year, when the Australian government published a national quantum strategy to make the country “a leader of the global quantum industry” where “quantum technologies are integral to a prosperous, fair and inclusive Australia”, there was an important omission: it didn’t mention mathematics at all.

Australia does have many leading experts in quantum computing and quantum information science. However, making the most of quantum computers – and defending against them – will require deep mathematical training to produce new knowledge and research.