Amazing Science

At the Quantum World Congress recently IonQ announced two new systems (Forte Enterprise and Tempo) intended to be rack-mountable and deployable in a traditional data center. Yesterday, speaking at Tabor Communications HPC and AI on Wall Street conference, the company made a strong pitch for reaching quantum advantage in 2-3 years, using the new systems.

If you’ve been following quantum computing, you probably know that deploying quantum computers in the datacenter is a rare occurrence. Access to the vast majority NISQ era computers has been through web portals. The latest announcement from IonQ, along with somewhat similar announcement from neutral atom specialist QuEra in August, and increased IBM efforts (Cleveland Clinic and PINQ2) to selectively place on-premise quantum systems suggest change is coming to the market.

IonQ’s two rack-mounted solutions are designed for businesses and governments wanting to integrate quantum capabilities within their existing infrastructure. “Businesses will be able to harness the power of quantum directly from their own data centers, making the technology significantly more accessible and easy to apply to key workflows and business processes,” reported the company. IonQ is calling the new systems enterprise-grade. (see the official announcement.)

Snapshot of the new systems:

• “IonQ Forte Enterprise brings quantum computing to modern data centers: With a target performance of #AQ 35, IonQ Forte Enterprise is expected to further IonQ’s lead as the provider of the most powerful, commercially available quantum computer in the world. IonQ Forte Enterprise is designed for complex computational problems, including process optimization, quantum machine learning, correlation analysis, and pattern recognition. With today’s announcement, IonQ is streamlining these capabilities into a compact form factor that can be easily deployed across existing data center infrastructures.
• “IonQ Tempo enables commercial advantage capabilities for the most demanding use-cases: IonQ has revealed for the first time new details of its highly anticipated #AQ 64 enterprise-grade system, IonQ Tempo. Tempo is anticipated to be a commercial advantage system capable of delivering substantial business value for today’s use cases. An #AQ 64-based Tempo system would far exceed what can be simulated with classical computers and GPUs, and provide a computational space 536 million times larger than even IonQ Forte Enterprise, an astonishing leap in computational power.”

IonQ reported its Forte Enterprise system will be introduced in 2024 and the Temp on 2025.

Speaking at Tabor’s HPC and AI on Wall Street conference, Philip Farah, VP strategic partnerships, and Bob Fletcher, director, enterprise sales, jointly reviewed IonQ’s progress and plans for delivering quantum advantage. It’s thought that financial services will be among the first industry sectors to deploy quantum computers.

With the world’s first exascale supercomputing system now open to full user operations, research teams are harnessing Frontier’s power and speed to tackle some of the most challenging problems in modern science.

The HPE Cray EX system at the Department of Energy’s Oak Ridge National Laboratory debuted in May 2022 as the fastest computer on the planet and first machine to break the exascale barrier at 1.1 exaflops, or 1.1 quintillion calculations per second. That’s more calculations per second than every human on Earth could perform in four years.

Frontier remains in the number one spot on the May 2023 TOP500 rankings, with an updated HPL, or high-performance Linpack, score of 1.194 exaflops. The increase of .092 exaflops, or 92 petaflops, is equivalent to the eighth most powerful supercomputer in the world on the TOP500 List. Engineers at the Oak Ridge Leadership Computing Facility, which houses Frontier and its predecessor Summit, expect that Frontier’s speeds could ultimately top 1.4 exaflops, or 1.4 quintillion calculations per second.

In addition to the updated HPL number, the Frontier team has improved the High-Performance Linpack-Mixed Precision Benchmark, HPL-MxP, to nearly 10 EF. Frontier’s HPL-MxP performance is now 9.950 EF, or 9.95 quintillion flops per second improved from 7.9 exaflops in November 2022. “Frontier represents the culmination of more than a decade of hard work by dedicated professionals from across academia, private business and the national laboratory complex through the Exascale Computing Initiative to realize a goal that once seemed barely possible,” said Doug Kothe, ORNL’s associate laboratory director for computing and computational sciences. “This machine will shrink the timeline for discoveries that will change the world for the better and touch everyone on Earth.”

Nvidia has emerged as the clear leader in powering the early days of the AI revolution, but rivals big and small are looking to close the gap. Heavyweights such as Advanced Micro Devices (AMD) and Intel are spending billions of dollars to enhance their AI offerings, while startups are attracting investors eager to get into the next possible chip giant. Meanwhile, cloud-computing companies such as Amazon and Google are developing their own chips and are trying to become bigger players in this area.

Researchers are working out ways to ignore the noise and leverage the power of qubits to advance quantum computing.

The synthetic galaxy catalog will help test Roman's capabilities and foster collaboration with the Rubin project. 

Combining Wolfram Alpha and GPT-3 could potentially create a powerful tool that could answer complex questions and generate human-like text. It could be used to provide in-depth explanations and discussions on a wide range of topics, and could be used to engage in natural-sounding conversations.

Accessing Wolfram|Alpha's computational knowledge with ChatGPT--an ideal combination of precise computation with human-like expression of ideas. Stephen Wolfram explains how.

It’s always amazing when things suddenly “just work”. It happened to us with Wolfram|Alpha back in 2009. It happened with our Physics Project in 2020. And it’s happening now with OpenAI’s ChatGPT. Stephen Wolfram has been tracking neural net technology for about 43 years and he finds the performance of ChatGPT thoroughly remarkable. Suddenly, there is a system that can successfully generate text about almost anything—that’s very comparable to what humans might write. It’s impressive, and useful and its success is probably going to tell us some very fundamental things about the nature of human thinking.

But while ChatGPT is a remarkable achievement in automating the doing of major human-like things, not everything that’s useful to do is quite so “human like”. Some of it is instead more formal and structured. And indeed one of the great achievements of our civilization over the past several centuries has been to build up the paradigms of mathematics, the exact sciences—and, most importantly, now computation—and to create a tower of capabilities quite different from what pure human-like thinking can achieve.

Stephen Wolfram himself has been deeply involved with the computational paradigm for many decades, in the singular pursuit of building a computational language to represent as many things in the world as possible in formal symbolic ways. His goal has always been to build a system that can “computationally assist”—and augment—what I and others want to do. He thinks about things as a human but can also immediately use Wolfram Language and Wolfram|Alpha to tap into a kind of unique “computational superpower” that lets him perform all sorts of beyond-human things. It’s a tremendously powerful way of working and not just important for humans but equally, if not more, important for human-like AIs as well—immediately giving them computational knowledge superpowers, that leverage the non-human-like power of structured computation and structured knowledge.

We’ve just started exploring what this means for ChatGPT. But it’s pretty clear that wonderful things are now possible. Wolfram|Alpha does something very different from ChatGPT, in a very different way. But they have a common interface: natural language. And this means that ChatGPT can “talk to” Wolfram|Alpha just like humans do—with Wolfram|Alpha turning the natural language it gets from ChatGPT into precise, symbolic computational language on which it can apply its computational knowledge power.

For decades there’s been a dichotomy in thinking about AI between “statistical approaches” of the kind ChatGPT uses, and “symbolic approaches” that are in effect the starting point for Wolfram|Alpha. But now—thanks to the success of ChatGPT—as well as all the work we’ve done in making Wolfram|Alpha understand natural language—there’s finally the opportunity to combine these to make something much stronger than either could ever achieve on their own.

A new way to explore topological superconductivity.

A new way to combine two materials with special electrical properties -- a monolayer superconductor and a topological insulator -- provides the best platform to date to explore an unusual form of superconductivity called topological superconductivity. The combination could provide the basis for topological quantum computers that are more stable than their traditional counterparts.

Superconductors -- used in powerful magnets, digital circuits, and imaging devices -- allow the electric current to pass without resistance, while topological insulators are thin films only a few atoms thick that restrict the movement of electrons to their edges, which can result in unique properties. A team led by researchers at Penn State describe how they have paired the two materials in a paper appearing Oct. 27th 2022 in the journal Nature Materials.

"The future of quantum computing depends on a kind of material that we call a topological superconductor, which can be formed by combining a topological insulator with a superconductor, but the actual process of combining these two materials is challenging," said Cui-Zu Chang, Henry W. Knerr Early Career Professor and Associate Professor of Physics at Penn State and leader of the research team. "In this study, we used a technique called molecular beam epitaxy to synthesize both topological insulator and superconductor films and create a two-dimensional heterostructure that is an excellent platform to explore the phenomenon of topological superconductivity."

In previous experiments to combine the two materials, the superconductivity in thin films usually disappears once a topological insulator layer is grown on top. Physicists have been able to add a topological insulator film onto a three-dimensional "bulk" superconductor and retain the properties of both materials. However, applications for topological superconductors, such as chips with low power consumption inside quantum computers or smartphones, would need to be two-dimensional.

In this paper, the research team stacked a topological insulator film made of bismuth selenide (Bi2Se3) with different thicknesses on a superconductor film made of monolayer niobium diselenide (NbSe2), resulting in a two-dimensional end-product. By synthesizing the heterostructures at very lower temperature, the team was able to retain both the topological and superconducting properties. "In superconductors, electrons form 'Cooper pairs' and can flow with zero resistance, but a strong magnetic field can break those pairs," said Hemian Yi, a postdoctoral scholar in the Chang Research Group at Penn State and the first author of the paper. "The monolayer superconductor film we used is known for its 'Ising-type superconductivity,' which means that the Cooper pairs are very robust against the in-plane magnetic fields. We would also expect the topological superconducting phase formed in our heterostructures to be robust in this way."

Short animations giving viewers a taste of the tactics behind misinformation can help to “inoculate” people against harmful content on social media when deployed in YouTube’s advert slot, according to a major online experiment led by the University of Cambridge.

Micron Technology says it has reached volume production of a 232-layer NAND flash-memory chip. It’s the first such chip to pass the 200-layer mark, and it’s been a tight race. Competitors are currently providing 176-layer technology, and some have said they are on track to follow Micron’s skyward move or already have working chips in hand.

The new Micron tech as much as doubles the density of bits stored per unit area versus competing chips, packing in 14.6 gigabits per square millimeter. Its 1-terabit chips are bundled into 2-terabyte packages, each of which is barely more than a centimeter on a side and can store about two weeks worth of 4K video. With 81 trillion gigabytes (81 zettabytes) of data generated in 2021 and International Data Corp. (IDC) predicting 221 ZB in 2026, “storage has to innovate to keep up,” says Alvaro Toledo, Micron’s vice president of data-center storage.

The move to 223 layers is a combination and extension of many technologies Micron has already deployed. To get a handle on them, you need to know the basic structure and function of 3D NAND flash. The chip itself is made up of a bottom layer of CMOS logic and other circuitry that’s responsible for controlling reading and writing operations and getting data on and off the chip as quickly and efficiently as possible. Improvements to this layer, such as optimizing the path data travels and reducing the capacitance of the chip’s inputs and outputs, yielded a 50 percent improvement in the data transfer rate to 2.4 Gb/s.

Above the CMOS are layers upon layers of NAND flash cells. Unlike other devices, Flash-memory cells are built vertically. They start as a (relatively) deep, narrow hole etched through alternating layers of conductor and insulator. Then the holes are filled with material and processed to form the bit-storing part of the device. It’s the ability to reliably etch and fill the holes through all those layers that’s a key limit to the technology. Instead of etching through all 232 layers in one go, Micron’s process builds them in two parts and stacks one atop the other. Even so, “it’s an astounding engineering feat,” says Alvaro. “That was one of the biggest challenges we overcame.”

According to Toledo, there is a path toward even more layers in future NAND chips. “There are definitely challenges,” he says. But “we haven’t seen the end of that path.” In addition to adding more and more layers, NAND flash makers have been increasing the density of stored bits by packing multiple bits into a single device. Each of the Micron chip’s memory cells is capable of storing three bits per cell. That is, the charge stored in each cell produces a distinct enough effect to discern eight different states. Though 3-bit-per-cell products (called TLC) are the majority, four-bit products (called QLC) are also available. One QLC chip presented by Western Digital researchers at the IEEE International Solid State Circuits Conference earlier this year achieved a 15 Gb/mm2 areal density in a 162-layer chip. And Kioxia engineers reported 5-bit cells last month at the IEEE Symposium on VLSI Technology and Circuits. There has even been a 7-bit cell demonstrated, but it required dunking the chip in 77-kelvin liquid nitrogen.

When quantum-computing pioneer D-Wave releases its next-generation Advantage2 system in 2023 or 2024, the company expects its 7,000-qubit machine to be the most powerful quantum computer of its kind in the world. Now D-Wave is making an experimental prototype of Advantage2 immediately available for use over the cloud.

Classical computers switch transistors either on or off to symbolize data as ones or zeroes. In contrast, quantum computers use quantum bits, or “qubits.” Because of the strange nature of quantum physics, qubits can exist in a state called superposition, in which they are essentially both 1 and 0 at the same time. This phenomenon lets each qubit perform two calculations at once. The more qubits are quantum mechanically linked, or entangled, within a quantum computer, the greater its computational power can grow, in an exponential fashion.

The standard approach toward building quantum computers, called the gate model, involves arranging qubits in circuits and making them interact with each other in a fixed sequence. In contrast, D-Wave—based in Burnaby, B.C, Canada—has long focused on what are called annealing quantum computers. Quantum cousins of classical annealing computers, these machines find a lowest energy state by slowly cooling it down—in much the same way that metals and crystals are sometimes tempered so as to minimize imperfections. Quantum annealing machines, then, start off with a set of qubits whose interactions at their lowest energy state, called the ground state, represent the correct answer for a specific problem the researchers programmed it to solve.

The ideal application for annealing quantum computers may be solving optimization problems, says Emile Hoskinson, an experimental physicist and the director of quantum-annealing products at D-Wave. These seek to find the best answer from all possible solutions, such as mapping the fastest route from point A to point B.

For instance, imagine trying to find the lowest point on a vast landscape covered in hills and valleys. A classical computer might start at a random spot on the surface and look around for a lower spot to explore until it cannot walk downhill anymore. This approach can often get stuck in a “local minimum,” a valley that is not actually the very lowest point on the surface.

On the other hand, annealing quantum computers could make it possible to start at many spots on the surface at the same time, reducing the chance of becoming trapped in a local minimum. They can even essentially tunnel through a hill to see if there is a lower valley beyond it, or share data from multiple spots to find patterns that might lead to deeper points.

Founded in 1999, D-Wave bills itself as the world’s first commercial supplier of quantum computers. The company has longprovedcontroversial, with many critics over the years questioning whether its machines are any more powerful than regular computers.

Nevertheless, D-Wave has claimed its share of high-profile clients over the years. It sold its first quantum computing system, the 128-qubit D-Wave One, to Lockheed Martin in 2011, and shipped its 512-qubit D-Wave Two to NASA’s Quantum Artificial Intelligence Lab—launched in partnership with Google and the Universities Space Research Association—in 2013.

When D-Wave’s Advantage2 comes online next year or the year after, it will be the world’s most powerful annealing quantum computer, Hoskinson says. Now the company is releasing an experimental prototype of Advantage2, with all the core functionality of the full-scale product available for testing.

“Our broad portfolio of enterprise customers—such as Volkswagen, Save-on-Foods, Denso, Toyota, BBVA, NEC, Accenture, and Lockheed Martin—have built hundreds of early quantum applications in diverse areas such as resource scheduling, mobility, logistics, drug discovery, portfolio optimization, manufacturing processes, and much more,” Hoskinson says. “We expect the Advantage2 can be used to address problems with even greater complexity. When we talk about addressing a broader swath of problems, we mean the improved technology can tackle problems that are larger, more complex, and that cover a wider range of use cases across different verticals. It will address the same problems as before, but better and faster.”

The prototype holds more than 500 superconducting flux qubits. The new design of the qubits enables what D-Wave calls its Zephyrtopology, which supports 20-way interqubit connectivity, up from 15 in the company’s prior generation, Advantage.

Medical schools are using augmented reality in their curricula to give students hands-on learning opportunities. Medical students can use augmented reality to see and apply theories throughout their training.

National Supercomputing Center in Wuxi builds a yet another supercomputer that claims exascale performance. After the U.S. government imposed crippling sanctions against select Chinese high-tech and supercomputer companies through 2019 and 2020, firms like Huawei had to halt chip development; it is impossible to build competitive processors without access to leading-edge nodes. But Jiangnan Computing Lab, which develops Sunway processors, and National Supercomputing Center in Wuxi kept building new supercomputers and recently even submitted results of their latest machine for the Association for Computing Machinery's Gordon Bell prize.

The new Sunway supercomputer built by the National Supercomputing Center in Wuxi (an entity blacklisted in the U.S.) employs around feature approximately 19.2 million cores across 49,230 nodes, reports Supercomputing.org. To put the number into context, Frontier, the world's highest-performing supercomputer, uses 9472 nodes and consumes 21 MW of power. Meanwhile, the National Supercomputing Center in Wuxi does not disclose power consumption of its latest system.

Interestingly, the new supercomputer seems to be based on the already known 390-core Sunway processor that derive from the Sunway SW26010 CPUs and have been around since 2021. Therefore, the new system increased the number of processors, but not their architectural efficiency, so its power consumption is likely to be gargantuan. Meanwhile, actual performance of the machine is unknown, since scaling out has its limits even in the supercomputer world.

The National Supercomputing Center in Wuxi has not disclosed performance numbers of its new supercomputer, and it is hard to make any estimations about its performance at this point. The reason why we called it ‘exascale’ is because its predecessor, the Sunway Oceanlite from 2021, was estimated to offer compute performance of around 1 ExaFLOPS.

Meanwhile, engineers revealed the workload that it used the machine for. Apparently, the the group created a new code for large whirlpool simulations to address compressible currents in turbo machinery. They applied it to NASA’s grand challenge problem using an advanced unstructured solver for a high-pressure turbine sequence with 1.69 billion mesh components and 865 billion degrees of freedom (judged by the number of variables). Given how complex the simulation is, it is likely that the machine is indeed quite powerful. Meanwhile, there is no word whether the simulation was conducted with FP64 precision, or precision was sacrificed for the sake of performance.

The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. Recently, the Google team and international collaborators introduced a method for reconstructing music from brain activity alone, captured using functional magnetic resonance imaging (fMRI). This approach uses either music retrieval or the MusicLM music generation model conditioned on embeddings derived from fMRI data. The generated music resembles the musical stimuli that human subjects experienced, with respect to semantic properties like genre, instrumentation, and mood. The scientists investigate the relationship between different components of MusicLM and brain activity through a voxel-wise encoding modeling analysis. Furthermore, they analyze which brain regions represent information derived from purely textual descriptions of music stimuli.

OpenAI today announced the general availability of GPT-4, its latest text-generating model, through its API. Starting this afternoon, all existing OpenAI API developers “with a history of successful payments” can access GPT-4. The company plans to open up access to new developers by the end of this month, and then start raising availability limits after that “depending on compute availability.

"Millions of developers have requested access to the GPT-4 API since March, and the range of innovative products leveraging GPT-4 is growing every day,” OpenAI wrote in a blog post. “We envision a future where chat-based models can support any use case.” GPT-4 can generate text (including code) and accept image and text inputs — an improvement over GPT-3.5, its predecessor, which only accepted text — and performs at “human level” on various professional and academic benchmarks. Like previous GPT models from OpenAI, GPT-4 was trained using publicly available data, including from public webpages, as well as data that OpenAI licensed. The image understanding capability isn’t available to all OpenAI customers just yet. OpenAI’s testing it with a single partner, Be My Eyes, to start with. But it hasn’t indicated when it’ll open it up to the wider customer base.

It’s worth noting that, as with even the best generative AI models today, GPT-4 isn’t perfect. It “hallucinates” facts and makes reasoning errors, sometimes with confidence. And it doesn’t learn from its experience, failing at hard problems such as introducing security vulnerabilities into code it generates. In the future, OpenAI says that it’ll allow developers to fine-tune GPT-3 and GPT-3.5 Turbo, one of its other recent but less capable text-generating models, with their own data, as has long been possible with OpenAI’s other text-generating models. That capability should arrive later this year, according to OpenAI.

GPT-3.5 Turbo, DALL·E and Whisper APIs are also generally available, and we are releasing a deprecation plan for older models of the Completions API, which will retire at the beginning of 2024.

Scientists have used cesium atoms on a solid-state semiconductor to create artificial atoms and create synthetic molecules.

In this exclusive preview of groundbreaking, unreleased technology, former Apple designer and Humane cofounder Imran Chaudhri envisions a future where AI enables our devices to "disappear." He gives a sneak peek of his company's new product -- shown for the first time ever on the TED stage -- and explains how it could change the way we interact with tech and the world around us. Witness a stunning vision of the next leap in device design.

A new technique greatly reduces the error in an optical neural network, which uses light to process data instead of electrical signals. With their technique, the larger an optical neural network becomes, the lower the error in its computations. This could enable them to scale these devices up so they would be large enough for commercial uses.

As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up.

An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy.

However, these analog devices are prone to hardware errors that can make computations less precise. Microscopic imperfections in hardware components are one cause of these errors. In an optical neural network that has many connected components, errors can quickly accumulate.

Even with error-correction techniques, due to fundamental properties of the devices that make up an optical neural network, some amount of error is unavoidable. A network that is large enough to be implemented in the real world would be far too imprecise to be effective.

MIT researchers have overcome this hurdle and found a way to effectively scale an optical neural network. By adding a tiny hardware component to the optical switches that form the network’s architecture, they can reduce even the uncorrectable errors that would otherwise accumulate in the device.

Their work could enable a super-fast, energy-efficient, analog neural network that can function with the same accuracy as a digital one. With this technique, as an optical circuit becomes larger, the amount of error in its computations actually decreases.  

“This is remarkable, as it runs counter to the intuition of analog systems, where larger circuits are supposed to have higher errors, so that errors set a limit on scalability. This present paper allows us to address the scalability question of these systems with an unambiguous ‘yes,’” says lead author Ryan Hamerly, a visiting scientist in the MIT Research Laboratory for Electronics (RLE) and Quantum Photonics Laboratory and senior scientist at NTT Research.

Hamerly’s co-authors are graduate student Saumil Bandyopadhyay and senior author Dirk Englund, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), leader of the Quantum Photonics Laboratory, and member of the RLE. The research is published today in Nature Communications.

Moore’s Law predicts that the number of transistors per device will double every two years. Moore’s Law is and always has been driven by innovation. The above figure illustrates the number of transistors per device as we look to the past, the present and the future. For the first 40 years, the gains came primarily from innovations in our process. Going forward, gains will come from innovations in both process and packaging. INTEL's processes will continue to deliver historic density improvements, while its 2D and 3D stacking technologies give architects and designers more tools to increase the number of transistors per device. As the designers look forward to innovative technologies such as High NA, RibbonFET, PowerVia, Foveros Omni and Direct, and others, INTEL sees no end to innovation and therefore currently no end to Moore’s Law.

In summary, when we consider all the various process and advanced packaging innovations, there are numerous options available to continue to double the number of transistors per device at the cadence demanded by our customers. Moore’s Law only stops when innovation stops, and innovation continues unabated at Intel in process, packaging and architecture. We remain undeterred in our aspiration to deliver approximately 1 trillion transistors in a single device by 2030.

A new AI program observed physical phenomena and uncovered relevant variables—a necessary precursor to any physics theory.

All physical laws are described as mathematical relationships between state variables. These variables give a complete and non-redundant description of the relevant system. However, despite the prevalence of computing power and artificial intelligence, the process of identifying the hidden state variables themselves has resisted automation. Most data-driven methods for modeling physical phenomena still rely on the assumption that the relevant state variables are already known. A longstanding question is whether it is possible to identify state variables from only high-dimensional observational data.

Scientists now created a principle for determining how many state variables an observed system is likely to have, and what these variables might be. They were able to demonstrate the effectiveness of this approach using video recordings of a variety of physical dynamical systems, ranging from elastic double pendulums to fire flames. Without any prior knowledge of the underlying physics, our algorithm discovers the intrinsic dimension of the observed dynamics and identifies candidate sets of state variables.

Github repository is here

Researchers at Simon Fraser University have made a crucial breakthrough in the development of quantum technology. Their research, published in the science journal Nature, describes their observations of over 150,000 silicon 'T centre' photon-spin qubits, an important milestone that unlocks immediate opportunities to construct massively scalable quantum computers and the quantum internet that will connect them.

Quantum computing has enormous potential to provide computing power well beyond the capabilities of today's supercomputers, which could enable advances in many other fields, including chemistry, materials science, medicine and cybersecurity. In order to make this a reality, it is necessary to produce both stable, long-lived qubits that provide processing power, as well as the communications technology that enables these qubits to link together at scale.

Past research has indicated that silicon can produce some of the most stable and long-lived qubits in the industry. Now the research published by Daniel Higginbottom, Alex Kurkjian, and co-authors provides proof of principle that T centres, a specific luminescent defect in silicon, can provide a 'photonic link' between qubits. This comes out of the SFU Silicon Quantum Technology Lab in SFU's Physics Department, co-led by Stephanie Simmons, Canada Research Chair in Silicon Quantum Technologies and Michael Thewalt, Professor Emeritus. "This work is the first measurement of single T centresin isolation, and actually, the first measurement of any single spin in silicon to be performed with only optical measurements," says Stephanie Simmons.

"An emitter like the T centre that combines high-performance spin qubits and optical photon generation is ideal to make scalable, distributed, quantum computers, because they can handle the processing and the communications together, rather than needing to interface two different quantum technologies, one for processing and one for communications," Simmons says. In addition, T centres have the advantage of emitting light at the same wavelength that today's metropolitan fibre communications and telecom networking equipment use.

"With T centres, you can build quantum processors that inherently communicate with other processors," Simmons says. "When your silicon qubit can communicate by emitting photons (light) in the same band used in data centres and fiber networks, you get these same benefits for connecting the millions of qubits needed for quantum computing."

Developing quantum technology using silicon provides opportunities to rapidly scale quantum computing. The global semiconductor industry is already able to inexpensively manufacture silicon computer chips at scale, with a staggering degree of precision. This technology forms the backbone of modern computing and networking, from smartphones to the world's most powerful supercomputers. "By finding a way to create quantum computing processors in silicon, you can take advantage of all of the years of development, knowledge, and infrastructure used to manufacture conventional computers, rather than creating a whole new industry for quantum manufacturing," Simmons says. "This represents an almost insurmountable competitive advantage in the international race for a quantum computer."