Majorana 1: Microsoft’s Work On Quantum Computing (Part 2)

Last week, we left off with the concept of quantum computing. Today, we’re continuing with the two main types of quantum processors, some challenges the sector is facing, what sets Majorana 1 apart from its competitors, and whether it’s actually revolutionizing the quantum computing industry.

The Hardware of Quantum Computing Explained

Before Majorana 1, there were two main approaches to quantum computing.

The first are superconductor quantum processors, which use superconducting material to form circuits often consisting of Josephson junctions (thin insulating barriers between superconducting material), and can be used to create qubits. The qubits can then be measured and manipulated using microwave pulses applied by quantum gates, allowing for operation resembling classical logic gates.

The term ‘superconductor’ is just a fancy name for materials that have zero electrical resistance when cooled below their superconducting temperature (which can near absolute zero for some), meaning no energy is lost as heat. In addition, they have the ability to repel magnets thanks to the Meissner Effect, which is why superconductors are useful in maglev (magnetic levitation), but more importantly, crucial for superconducting quantum processors.

You see, qubits are prone to something called quantum decoherence, where noise (referring to unwanted disturbances) degrades computational performance. There are many different types of noise, including quantum noise (the inherent uncertainty of quantum systems), environmental noise (originating from external factors such as electromagnetic radiation, temperature fluctuations, and vibrations), thermal noise (occurring at even the lowest of temperatures, causing excited qubits), flicker noise (characterized by frequency-dependent power spectral density), and flux noise (variations in magnetic flux occuring due to environmental fluctuation). The Meissner Effect helps mitigate the effect of magnetic noise, by forming an external magnetic barrier around the processor and ensuring relative stability and coherence of the system.

These superconducting quantum processing units can be scaled by incorporating them into larger circuits, expanding the potential of this technology.

Besides superconducting quantum processors, there is also the trapped ions quantum processor, developed by IonQ. These use ions as qubits, and the internal energy levels of these ions can be used to represent the quantum states 0 and 1.

The ‘trapped’ is characterized by the mechanism that confines these ions, which works by using the electromagnetic fields generated by laser beams or radio frequency traps. This provides precise control over their position and movement, and the lasers can also be used to measure the individual qubits. Quantum operations (quantum gates) are performed using laser pulses, by directing lasers at specific frequencies.

However, quantum computing, whether it’s trapped ions or superconducting, still have major challenges that they’ve yet to overcome.

For starters, even with methods like the Meissner Effect mitigating decoherence, it’s still a huge vulnerability to quantum computers. So to minimize noise and maximize efficiency, quantum computers are housed in multiple layers of protective shielding, put into a vacuum environment where air molecule interactions are reduced, cooled to millikelvin temperatures (approaching absolute zero -273.15ºC, the lowest possible temperature), housed in clean rooms to prevent the presence of dust and other contaminants, uses stable noise-free power supplies, have high-precision electronics to control qubit operations, and have error correction systems and specialized algorithms to lessen error.

Because of these extensive measures, the cost of maintaining quantum computers are very high, ranging from $500,000 to several million dollars per year, depending on requirements and the scale of the system.

Speaking of scale, that’s another challenge quantum computers currently face. It’s not exactly easy to scale up a computer just by adding more qubits, as you’re risking decoherence thereby defeating the purpose, while drastically increasing cooling and power needs. And even if you do manage to just pile on more qubits, it’s not easy to manufacture them either, as there are immense calibration and precision challenges, as well as material problems.

However, if these challenges could be overcome, in principle, 300 qubits would be enough to perform more calculations (approximately 10 to the power of 90) in parallel than there are atoms in the visible universe (approximately 10 to the power of 80). That would certainly be never-before-seen computing power.

How Does The Majorana 1 Work?

After understanding quantum computing and comparing it to classical computing, as well as the hardware that supports both types of computation, it’s time to answer the question: What sets the Majorana 1 apart?

The Majorana 1, as mentioned before, utilizes a new type of technology, called ‘topological protection’. This is possible thanks to the Majorana fermion, a particle hypothesized by Italian theoretical physicist Ettore Majorana that’s a fermion and its own antiparticle (with the same mass but opposite charges).

But if Majorana fermion is still ‘hypothesized’, then what have Microsoft used in their processor? This is where the world of quasi-particles comes in. Quasi-particle is a term used to describe the collective behaviour of a group of particles, treating them as if they were a single particle. The particle Microsoft used is actually a kind of quasi-particle that has similar properties as the theorized Majorana fermion.

This particle was created using a nanowire made of semi-conducting material coated in superconducting material, which was then subjected to a strong magnetic field and near absolute zero temperatures. Under these conditions, the electrons in the nanowire started behaving in ways that allowed Majorana fermions to appear at the ends of the wire.

The properties of the Majorana fermion are particularly useful in quantum computation, with everything to do with the noise and quantum decoherence mentioned earlier. Because it’s its own antiparticle, it’s like two pillars supporting a platform, making them inherently less prone to noise and decoherence, and ideal for quantum computing. Topological qubits made from Majorana fermions are also more easy to scale compared to those made by other companies, possibly supporting the claim by Microsoft to scale up to a million qubits in the future. Furthermore, Majorana fermions allow for non-local quantum operations, where information is stored in the collective state of particles instead in a single particle, increasing error resistance and the efficiency of computation.

Predictions

So is the Majorana 1 really game-changing? To some extent, yes, as it has proven there are possibly more efficient ways to quantum computing than previously thought, and that these methods could work. However, even with the new technology showcased in the Majorana 1, there are still significant hurdles that the sector has yet to overcome.

If the cost of developing and maintaining could be reduced yet quantum computing chips become more efficient, then QPUs could maybe become commonplace technology. They may not necessarily replace classical computing, but more likely, they will coexist and accelerate each other so that users can do things more efficiently than ever.

Quantum computing could be the key to finding cures to cancer and other life-threatening diseases, potentially help us achieve artificial general intelligence in the near future, and more.

Conclusion

However, those are just predictions. What the real future of quantum computing and tech may look can only be answered with time.

But you don’t need to wait to subscribe and share this article! Thank you for reading, and that concludes this two part series on the Majorana 1 and quantum computing in general.

I hope you learned something new in this series. Remember to come back next week for more in-depth news analysis.