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How will ordinary silicon chips be used in quantum computing?

"We're hacking the qubit development process."

According to Quantum Motion, the most recent experiment paves the way for large-scale, functional quantum computers.

Image Source- Quantum Motion

Forget superconducting circuits, trapped ions, and other exotic-sounding manufacturing methods associated with quantum computing: scientists have now demonstrated that a qubit can be generated on a regular silicon chip, similar to those used in smartphones.

a start-up based in the United Kingdom Researchers at Quantum Motion released the results of their most recent experiments, in which they successfully isolated and measured the quantum state of a single electron for nine seconds by cooling CMOS silicon chips to a fraction of a degree above absolute zero (-273 degrees Celsius).

In contrast to the approaches taken by larger players like IBM, Google, or Honeywell in their attempts to create a large-scale quantum computer, the method's apparent simplicity, which taps identical hardware found in handsets and laptops, is striking.

Scientists must first maintain control over the smallest, quantum particles that make up a substance in order to generate and read qubits, which are the building blocks of such devices; however, there are many ways to do so, each with varying degrees of difficulty.

IBM and Google, for example, have chosen to make superconducting qubits, which necessitates the creation of an entirely new manufacturing process; Honeywell, on the other hand, has developed a technology that individually traps atoms to allow researchers to calculate the states of the particles.

These methods necessitate the production of new quantum processors in the lab and are limited in size. Intel, for example, has produced a 49-qubit superconducting quantum processor that is about three inches square, which the company described as "relatively large" and likely to cause problems when it comes to mass-producing the million-qubit chips needed for real-world commercial implementations.

Quantum Motion set out to see if a better approach could be found in tested, current technologies with this in mind. "We need millions of qubits," says John Morton, a professor of nanoelectronics at University College London (UCL) and co-founder of Quantum Motion. "There are very few technologies that can allow millions of everything – but the silicon transistor is the exception."
"Rather than scaling up a new solution, we investigated whether we could leverage that capability and use these resources to create something similar, but with qubits."

When a transistor is turned on, it sucks in a bunch of electrons that allow current to flow, as Morton explains. Cooling the chip to a low temperature, on the other hand, slows this process down and allows researchers to watch the electrons reach the transistor one by one – "like seeing sheep walk into a field," Morton says. Instead of allowing all of the electrons to join, the researchers only permitted one electron to do so, which could then be separated and used as a qubit.

"We're hacking the qubit development process so that the same technology that makes a smartphone chip can be used to create quantum computers," Morton says.

Silicon chips have a major advantage over other quantum methods in terms of size. Because of the small size of electrons, the qubit density obtained with a silicon chip is effectively much higher; according to Morton, this would allow a single chip to pack millions of qubits, while a superconducting quantum computer would need an entire building for the same yield.

Furthermore, silicon chips have been tweaked and developed for decades, implying that quantum devices may rely on existing processes and fabrication plants. This will hasten the production of quantum processors while also lowering their costs.

To put it another way, rather than beginning from scratch, Quantum Motion recommends using the best of what already exists. "Plus, whenever the silicon industry progresses, you might benefit from advances in qubit technology," Morton says.

As exciting as the experiment is, silicon-based quantum computing is still in its infancy: Morton and his team have only isolated and measured the state of a single electron so far. The researchers intend to create a quantum gate by entangling two qubits on the chip in the next step.

Rather, Quantum Motion's results should be regarded as a roadmap for more effectively processing quantum chips by exploiting current manufacturing processes.

The results of the start-up are likely to draw the attention of larger rivals. Intel, for example, is becoming increasingly interested in the quantum possibilities that silicon chips provide. The Silicon Valley behemoth has teamed up with QuTech, a Dutch startup, to investigate the promise of silicon spin qubits.


In quantum computing, IonQ implements Algorithmic Qubits to battle Quantum Volume.

But, even more significantly, IonQ claims to be able to link smaller quantum systems to make them more realistic for data centers.

IonQ, a quantum computing startup, announced a roadmap for networking smaller quantum systems together, as well as a new metric called Algorithmic Qubits.

The basic Quantum Volume metrics introduced by IBM aren't going to cut it for IonQ in the long run. Quantum volume as a metric, according to IonQ, will be redundant soon because quantum computers will have numbers that are too big to fit on a screen.

IonQ CEO Peter Chapman wrote in a blog post that using Quantum Volume as a metric is equivalent to purchasing processors based on the number of transistors. IonQ is sponsored by Samsung's Catalyst Investment.

He told-

We introduce Algorithmic Qubits (AQ), which is defined as the largest number of effectively perfect qubits you can deploy for a typical quantum program. It's a similar idea to Quantum Volume but takes error-correction into account and has a clear, direct relationship to qubit count. In the absence of error-correction encoding, AQ = log2(QV), or inversely, QV = 2AQ. AQ represents the number of "useful" encoded qubits in a particular quantum computer and is a simple proxy for the ability to execute real quantum algorithms for a given input size.
AQ is generally smaller than the number of physical qubits. Hence, ignore vendors (and by extension, their roadmaps) that describe their systems purely by the number of physical qubits. A 72 qubit chip and a million qubit chip with 95% fidelity gates both have a QV of 8 and an AQ of 3. With that fidelity, only three qubits can be used for calculation, no matter the number of physical qubits.

IonQ's overall message is that focusing on the consistency of quantum logic gate operations would make systems more scalable and mainstream. It'll be fascinating to see how other vendors respond to AQ, but the metric squabbles highlight how young the industry is. Honeywell recently unveiled its quantum systems, and a slew of firms, including Google, IBM, and its System Q, Microsoft, Intel, Amazon Web Services, and others, are seeking commercialization in some form.

IBM's quantum computing roadmap, for example, predicts a 1,121-qubit device in 2023. The device would be 65 according to AQ.

  • IBM plans a 1,121-qubit quantum computing device for 2023.

  • Enterprises will buy Honeywell's System Model H1 quantum computer.

  • Quantum computers from Honeywell are now available: How will developers and use cases evolve?

  • Honeywell has launched quantum computing as a subscription service.

  • IonQ plans to use compact quantum computers that can be networked together in a data center. IonQ will achieve a broad quantum advantage by 2025, according to Chapman.


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