12th Apr 2022
Quantum computing is taking flight. The nascent industry has been diligently working its way toward more expansive quantum-computing chips since the mid-2000s, with leaps forward aplenty.
In 2019, Google’s quantum computer, named ‘Sycamore’ performed a complex mathematical calculation in 200 seconds, which would have taken the world’s most powerful standard supercomputer, IBM’s Summit, some 10,000 years to perform1 (IBM later countered this claim, saying it would have taken Summit two and a half days).
The following year it was Quantum computing company Honeywell’s turn to break boundaries. In July the company’s System Model H1 became the first to achieve the Quantum Volume 1024 benchmark2, a metric standard set by IBM to measure the performance of a quantum computer.
In November an important new milestone was reached: the launch of IBM’s 127 qubit Quantum Eagle3 marks the unveiling of the first chip with qubits in the triple figures.
Not only is this achievement remarkable in itself, it also sets IBM on track to achieve its target of completing a 1000+ qubit chip in 2023, and out front in a crowded field of quantum computing companies all racing toward a shared goal of revolutionizing computing – and potentially the world – as we know it.
The Eagle has landed
IBM’s progress as a quantum computing company has been steady. Within the last decade, IBM has seen success with the five qubit Quantum Canary, the 27 qubit Quantum Falcon, and the 65 qubit Quantum Hummingbird.
But the Eagle represents a real step up in terms of capability, as Dr Dario Gil, Senior Vice President and Director of Research at IBM explained in the Eagle’s launch video4: “For the first time in history, we’ve entered the realm where a classical supercomputer can no longer fully simulate the behavior of a quantum chip. Eagle will let us explore truly uncharted computational territory,” he said.
Quantum computing is set apart from classical computing in its use of qubits to carry information. Classical bits are binary – that is, they represent either a definite 0 or a definite 1, and are therefore either ‘on’ or ‘off’, like the flicking of a light switch. Not so with qubits5.
This is because in the weird world of quantum physics, particles demonstrate what is known as ‘superposition’. Rather than flipping directly from on to off, the qubit can be in an intermediate state of both on and off at the same time, measured on a sliding scale of probability (a concept popularized by the ‘Schrodinger’s Cat’ thought experiment6, which postulated a cat that was both alive and dead concurrently). This allows qubits to be in varying states simultaneously, which in turn vastly increases the computing power available, as multiple calculations can be run concurrently.
“We hope to use Eagle to explore the realm of quantum advantage, where quantum computers can tackle problems faster and with fewer resources than classical computers,” Gil said. “And quantum advantage might be coming sooner than you think. By integrating quantum with our classical high-performance computing resources, we will have a powerful combination that will open an entirely new path to study physics, chemistry, and machine learning.”
Eagle offers improvements in scale, quality and speed
However, while qubits bring obvious advantages in terms of computing power, they also pose problems. For a start, they must be kept completely isolated as any outside interaction can cause them to effectively ‘forget’ their information and make errors. This creates a lot of noise in the system.
And whereas error detection in classical computing is overcome by having multiple copies of each bit, this solution is not available to quantum computing engineers, as the laws of physics make it impossible to replicate a qubit.
This means that the computing power of a quantum computer isn’t only down to how many qubits it has, but is also reliant on the cleanliness of the data and error detection or elimination.
IBM had already made some progress in these areas. The 27 qubit Falcon, which used a heavy hex qubit arrangement to place the qubits on the corners and edges of hexagrams, reduced errors caused by interference between the qubits. The following iteration, the 65 qubit Hummingbird, introduced multiplexing – reading multiple qubits with a single wire, reducing the number of components needed to work the device.
With Eagle, IBM built upon these innovations by introducing 3D integration, placing chip components and wiring on multiple physical levels, which in turn paves the way for the next generations of quantum chips.
But in addition to scaling up the number of qubits, IBM has also been investing in two further metrics. Eagle represents an upgrade in quality, by using cutting edge qubit fabrication techniques, control electronics, and software. The company also has a focus on speed, seamlessly integrating classical software workflows to maximize the number of quantum circuits it can run per second.
Osprey and Condor are on the horizon
As advanced as it is, the Eagle is still a little way off from delivering on the promises of quantum computing.
“The current state of the art is that no experiment has demonstrated quantum advantage for practical tasks yet,” physicist Chao-Yang Lu, who is leading the quantum computing team at the University of Science and Technology of China (USTC), told Nature7.
Quantum engineer Andrew Dzurak at the University of New South Wales told the magazine he believed that real world problem solving would start to come in with 1000+ qubit chips.
“It’s hoped that some useful and even commercially valuable problems can be solved using quantum computers in this thousand-to-million-qubit range,” he said. “But to do really paradigm-shifting stuff, you are going to need millions of physical qubits.”
IBM is under no illusions that it can rest on its laurels. The Eagle is very much viewed merely as the next stepping stone to bigger and more complex quantum computing systems. This year it expects to release the 433 qubit Quantum Osprey, with the 1,121 Quantum Condor appearing on the scene next year.
“We think of Condor as an inflection point, a milestone that marks our ability to implement error correction and scale up our devices, while simultaneously complex enough to explore potential Quantum Advantages—problems that we can solve more efficiently on a quantum computer than on the world’s best supercomputers,” the company stated in its roadmap8 for scaling quantum technology, published in September 2020.
Invest in Quantum computing
These advances mean that quantum computers capable of solving real world problems are well within reach. The team at IBM is already working on a cooled “super-fridge” system capable of housing a hypothetical million qubit quantum computer, and are already thinking beyond.
“Ultimately, we envision a future where quantum interconnects link dilution refrigerators each holding a million qubits, like the intranet links supercomputing processors, creating a massively parallel quantum computer capable of changing the world,” the company said.
Given that IBM has already met significant milestones such as the Eagle within the expected timeframe, the company is on track to reach its goal of creating a fault-tolerant quantum computer by 2030.
Google has set itself a similar goal, writing on its blog last May that it intends to “build a useful, error-corrected quantum computer” within this decade9, and unveiling a new campus in Santa Barbara, California for that purpose.
And IBM, Google and Honeywell have all made their quantum technologies available to partner companies and institutions, further broadening the scope for innovation in this field.
This makes 2022 a great year to invest in quantum computing. But if you are not sure which are the best quantum computing stocks to buy, Defiance has taken the hard work out of investing in this exciting technology for you, with the QTUM ETF. The fund comprises equity securities of companies at the forefront of research and development of quantum computing, the materials required to build and house them, and those involved in quantum-powered innovation in artificial intelligence and machine learning.
1 “Google wants to build a useful quantum computer by 2029”, J Porter, May 19, 2021 https://www.theverge.com/2021/5/19/22443453/google-quantum-computer-2029-decade-commercial-useful-qubits-quantum-transistor
2 Quantum Milestone: 16-Fold Increase in Performance in a Year. July 2020. https://www.honeywell.com/us/en/news/2021/07/quantum-milestone-16-fold-increase-in-performance-in-a-year
3 “IBM Quantum breaks the 100‑qubit processor barrier,” J Chow, O Dial & J Gambetta; November 16, 2021, https://research.ibm.com/blog/127-qubit-quantum-processor-eagle
4 “The IBM Quantum Summit Keynote with Darío Gil, Senior Vice President and Director of IBM Research,” Accessed on January 6, 2021 https://www.theverge.com/2021/5/19/22443453/google-quantum-computer-2029-decade-commercial-useful-qubits-quantum-transistor
5 “What is a qubit?” https://www.quantum-inspire.com/kbase/what-is-a-qubit/
6 “What is Schrödinger’s Cat?” B Clegg. https://www.sciencefocus.com/science/what-is-schrodingers-cat/
7 “First quantum computer to pack 100 qubits enters crowded race,” P Ball, November 19, 2021 , https://www.nature.com/articles/d41586-021-03476-5
8 “IBM’s roadmap for scaling quantum technology,” J Gambetta, September 15, 2020 https://research.ibm.com/blog/ibm-quantum-roadmap
9 “Unveiling our new Quantum AI campus”, E. Lucerno, May 18, 2021. https://blog.google/technology/ai/unveiling-our-new-quantum-ai-campus/