What Microsoft's Majorana 1 means for the future of quantum computing

Introduction

This week, Microsoft unveiled a new brain for quantum computers called Majorana 1. The chip, which fits in the palm of your hand, leverages what are known as topological qubits – it sounds very complicated, many people have pointed this out, but the main benefit of this type of qubit is its innate error resistance.

The Redmond giant explained that the chip currently holds 8 qubits but the design makes it possible to scale this to 1 million qubits in the coming years. With this breakthrough, practical quantum computers are no longer decades away, but years.

In this editorial, I will take a look at the benefits of quantum computing, the history of quantum computing, explain Majorana 1 and where it fits into Microsoft’s quantum computing roadmap, and speculate on how far away quantum computing is, according to industry leaders.

The benefits of quantum computers

We’ve all been hearing about quantum computers for a long time now, but most people, I would wager, find the topic quite dense and impenetrable. The key point around quantum computing, is that when these computers are built, and capable of practical workloads, they’ll be able to solve some problems much more quickly than classical computers.

In classical computers, the basic units of data are called bits. They are binary, meaning at any one time they can have a value of 0 or 1. Classical computers run calculations and process data based on these bits and use logical operations to manipulate them. These 0s and 1s are represented by the switch position of transistors on microchips – for reference, microchips in modern smartphones have over 10 billion transistors.

Leveraging transistors, we get logic gates, the building blocks of classical computing. They perform operations on bits such as AND, OR, and NOT to create more complex functions. The final basic point about classical computers to note here is that they perform instructions in order, one at a time, which limits their speed and efficiency in some tasks.

With quantum computers, the basic unit is called a qubit, they have different properties to bits in classical computing which can make quantum computers much more efficient in certain areas. Unlike the bit, the qubit can have a value of 0, 1, or both at the same time – this is a principle from quantum mechanics called superposition and allows for simultaneous processing.

Another important property of quantum computers is entanglement. This refers to the ability of qubits to become entangled with other qubits so that they affect each other's state, this connection between them allows for faster information processing and complex problem-solving.

The final fundamental concept in quantum computing that I want to mention is called interference. After a quantum computer has used superposition and entanglement to create a vast number of possible states, it uses a principle called interference to amplify and suppress states. This further boosts the efficiency of computations.

To unlock these unique abilities of qubits, quantum computers use quantum gates, in addition to logic gates found in classical computing. Quantum computers enable quantum algorithms such as Shor’s Algorithm and Grover’s Algorithm, which efficiently factors large numbers and speeds up unstructured search problems, respectively.

Put simply, quantum computers can solve some problems much faster, are capable of parallelism, and help humanity to tackle problems that are currently unsolvable with classical computers.

When quantum computers for practical tasks emerge, it is expected that they’ll have a significant impact on several fields including cryptography, drug discovery and materials science, artificial intelligence and machine learning, financial modeling, as well as climate modeling and weather forecasting.

In the field of cryptography, experts are concerned that quantum computers could possibly break today’s encryption methods. However, this technology could also enable new forms of cryptography that’d be secure against quantum computers and classical computers.

Quantum computing’s ability to quicken drug discovery and materials science is one area where people would be impacted in their everyday lives for the better. Quantum computers can more accurately simulate molecular interactions, this could significantly speed up drug discovery processes and the development of new materials.

Quantum computers also hold potential in finance for creating better investment strategies and optimizing portfolios by analyzing difference scenarios and outcomes simultaneously.

Industries that stand to benefit from the development of practical quantum computing include healthcare, banking and finance, logistics and supply chain management, telecommunications, energy, AI, and manufacturing. The increased efficiency could also lead to cost reductions that could be passed onto consumers through the mechanism of competition between market participants.

A brief history of quantum computing

The idea of a quantum computer was proposed in the 1980s with no real consensus on who came up with it first. American physicist Paul Benioff described a quantum mechanical model of a computer that employed quantum mechanics in a 1980 paper, and Richard Feynman, the well-known American physicist, proposed the idea of using a quantum computer to simulate the behavior of physical systems. Feynman’s paper was released in 1982, he recognized that classical computers would struggle to simulate quantum systems and proposed a quantum computer to solve this.

Another significant contributor to the field of quantum computing is British physicist and computer scientist David Deutsch. He made some notable contributions including the concept of a quantum Turing machine, quantum parallelism, and quantum error correction.

In the 1990s, we got the creation of Shor’s Algorithm (1994) and Grover’s Algorithm (1996), which we mentioned earlier. Shor’s Algorithm allows for the factoring of large numbers while Grover’s Algorithm improves the efficiency of searching an unsorted database. This decade also saw the development of small-scale quantum computers from IBM and MIT, among others.

During the early 2000s, Microsoft began its research into topological quantum field theory, laying the foundations for its work with topological qubits, and ultimately, the development of the Majorana 1 chip.

In 2011, D-Wave, a Canadian company, made headlines with the D-Wave One, which claimed to be the world’s first commercially-available quantum computer. Fast-forward to the end of the decade and Google has built a quantum computer called Sycamore which achieved quantum supremacy in 2019.

Quantum supremacy is the idea that a quantum computer solves a problem that no classical computer can in a reasonable amount of time. Google achieved this with the 53-qubit Sycamore in 2019 by solving a problem in just 200 seconds what it’s believed would have taken a classical computer 10,000 years.

In the 2020s, efforts have continued towards practical quantum computers with firms like IBM, Google, and Microsoft leading the way. Each of these three companies has outlined their goals for the coming years, with developments expected to culminate around 2035.

With the 2010s seeing quantum computers becoming commercially accessible, it also meant that developers needed tools to create software. We’ve seen the launch of quantum software frameworks and quantum programming languages. Within Microsoft’s quantum computing ecosystem, there is the Q# language, which uses high-level syntax for writing quantum algorithms that run within Microsoft’s quantum platform.

IBM also has a quantum programming language called OpenQASM which can be used in addition to Python and Rust in the Qisket software development kit. Meanwhile, Google has developed the Cirq framework where developers can use Python to create quantum circuits. Many of these tools allow developers to run the programs in a simulator before running it on a cloud-based quantum computer.

Microsoft’s quantum computing roadmap and Majorana 1

On Wednesday, February 19, 2025, Microsoft unveiled its Majorana 1 quantum computer chip that, right now, holds 8 topological qubits, but, over time, can be scaled up to 1 million qubits. Chips that use topological qubits are different to what Microsoft’s rivals are building and have the benefit of being more error resistant than other types of qubits.

Error resistance is very important in quantum computing because they’re inherently prone to errors due to the fragile nature of quantum states. As Microsoft scales up the number of qubits on its chip, the error resistant properties of topological qubits will start to shine and reduce the obstacles the Redmond giant faces in the future.

Before discussing Microsoft’s roadmap, I want to quickly provide information about why the company called its chip Majorana 1, as some people online have questioned the choice of name.

Microsoft’s chip is named after the Italian physicist Ettore Majorana who came up with the concept of Majorana particles. These particles, or fermions, make up the building blocks of topological qubits. The error resistance in topological qubits comes from the fact that Majorana particles are their own antiparticles which allows them to encode data that’s more error resistant.

With the Majorana 1 chip, Microsoft has achieved the second stage of its six-stage roadmap towards practical quantum computers. The stages of the roadmap are as follows:

MILESTONE 01: Create & control Majoranas

 

For the first time in history, Microsoft engineered devices allow us to induce and control the topological phase of matter bookended by Majorana Zero Modes. This breakthrough enables the engineering of a new type of qubit.

 

MILESTONE 02: Hardware Protected Qubit

 

Our protected qubit, with built-in error protection, extends our first breakthrough by changing qubit technology from analog to digital control.

 

MILESTONE 03: High Quality Hardware Protected Qubits

 

To scale operations and reduce errors, digitally controlled hardware-protected qubits can be entangled and braided with a series of quality advances.

 

MILESTONE 04: Multi-qubit System

 

A variety of quantum algorithms can be executed when multiple qubits operate together as a programmable Quantum Processing Unit (QPU) in a full stack Quantum Machine.

 

MILESTONE 05: Resilient Quantum System

 

A Quantum Machine, when operating on true logical qubits, demonstrates higher quality operations than the underlying physical qubits. This breakthrough enables the first reliable quantum operations and opens the gates to quantum supercomputing.

 

MILESTONE 06: Quantum Supercomputer

 

The Quantum Supercomputer solves scientific or commercial problems faster than classical computers, starting at 1 million reliable rQOPS/sec with an error rate below 1 in a trillion, scaling to 100 million rQOPS/sec for advanced chemistry and materials science challenges.

To get from the first to the second stage of the roadmap, it took Microsoft 18 months. The company has stated that it’ll reach its goal in years rather than decades. This suggests that the latest date by which it will reach its goal will be around 2035, however, if we assume it takes 18 months to reach each goal, it could be completed by 2031.

The work Microsoft is doing with Majorana 1 has attracted the attention of the Defense Advanced Research Projects Agency (DARPA). Microsoft is one of just two companies in the final phase of DARPA’s Underexplored Systems for Utility-Scale Quantum Computing (US2QC) program. The program makes up part of the Quantum Benchmarking Initiative which aims to deliver “the industry’s first utility-scale fault-tolerant quantum computer”.

How far away are quantum computers?

Predicting the arrival of quantum computers is tricky, simply, we don’t know exactly when machines with a million stable qubits will arrive. Right now, there are machines in the low hundreds of qubits, but a million is still quite far off. Reducing the number of errors in quantum computers, and cooling, are also significant issues that need to be overcome.

Similarly to Microsoft, Google also has a six-stage timeline and so far, the first two milestones have been hit; the first was hit in 2019, and the second was in 2023. At the 2023 milestone, Google said its quantum computer has 100 physical qubits. The aim in milestone 3, 4, 5, and 6, respectively, is to have 1,000, 10,000, 100,000, and 1,000,000 physical qubits.

If Google reaches subsequent milestones at the same rate it took to get from the first to the second milestone, then milestone 3 will be reached in 2027, M4 in 2031, M5 in 2035, and M6 in 2039. That’s not too far off what Microsoft is saying about quantum computers being years away, not decades.

IBM states in its roadmap that it will have a quantum computer around 2033 capable of running 1,000s of logical qubits. Logical qubits are actually encoded across lots of physical qubits in an attempt to reduce the number of errors. Google is taking this approach too, but Microsoft’s topological qubits are designed to be inherently stable, which could give the Windows maker the edge in this race.

Before we get there, a number of hurdles need to be overcome, chiefly error rates and decoherence. There is also the issue of scalability; the quantum computers around today only have small numbers of qubits and as companies try to add more, it will add to the complexity of systems.

On the hardware side of things, for practical quantum computers to become a reality, there will need to be new materials developed such as new semiconductors, better insulators, and topological insulators. These will need to be stable, cheap, and reproducible.

Quantum computers are also expensive to run due to the cooling they require, limiting their deployment to governments and big tech companies. To make them more accessible, there will need to be a miniaturization of setups, and new ways unlocked for them to operate at higher temperatures so they don’t have to be so intensively cooled.

Finally, there is the issue of standardization. As you may have gathered from this editorial, the big tech firms are all approaching the quantum computing problem from multiple angles making their systems incompatible with each other. In the long term, there will have to be some sort of convergence around common standards.

Conclusion

In this editorial, I have provided an outline of the benefits of quantum computers, a brief history of quantum computers, explained why Microsoft’s Majorana 1 is notable, and had a look at how far away we are from practical quantum computers.

The impetus for this editorial was the unveiling of Majorana 1. I think it’s a very worthwhile development, given the fact that Microsoft has shown off a new type of qubit that attempts to solve error issues, while offering a seemingly viable path for scaling to 1 million qubits. The Redmond giant’s prediction that quantum computers are no longer decades away suggests that optimistic timelines could turn out to be correct, which is an exciting development.

I think the potential that quantum computers hold is massive, with the anticipated improvements in healthcare, materials, and climate modeling. When I first tried ChatGPT in 2022, it felt like magic. I think that quantum computing, while it probably won’t be directly in our hands at first, will also lead to similar magnitude developments in the mentioned fields and beyond.

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