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Atom Computing

Atom Computing, a quantum computing company headquartered in Berkeley, California, seems to be on the fast track for funding.

This week Atom announced it had secured$60MSeries B round of financing led by Third Point Ventures. The round also included Prime Movers Lab and insiders Innovation Endeavors, Venrock, and Prelude Ventures.

Atom was founded in 2018 with $5M in seed funds by Benjamin Bloom and Jonathan King. Over two years, the duo used those funds to secretly staff and build a quantum computer with a unique technology. What set Atoms computer apart from other quantum machines was that it was the first quantum computer to use nuclear-spin qubits created from optically-trapped neutral atoms.

First-Generation Quantum Computer, Phoenix

In July 2021, Atom Computingreceived an additional $15M in Series A funding from investorsVenrock, Innovation Endeavors, and Prelude Ventures, plus three grants from the National Science Foundation.

According to a statement on Atom's press release by Rob Hays, Atom Computing's president and CEO, there was no shortage of investment interest. "We've seen a tremendous amount ofinvestor interest in what many are starting to believe is a more promising way to scale quantum computers neutral atoms, he said. Our technology advancements and this investment give us the runway to continue our focus on delivering the most scalable and reliable quantum computers."

Whats different about its technology

Most of todays quantum computers use two types of qubits, either superconducting (IBM & Google) or trapped-ion (Quantinum or IonQ). Amazon doesnt yet have a quantum computer, but it plans to build one using superconducting hardware. In contrast, Psi Quantum and Xanadu use photons of light that act as qubits.

Atom computing chose to use a different technology -nuclear-spin qubits made from neutral atoms.Phoenix, the name of Atoms first-generation, gate-based quantum computer platform, uses 100 optically trapped qubits.

These qubits are created from an isotope of Strontium, a naturally occurring element considered to be a neutral atom. Goingdeeper, neutral atoms have equal numbers of protons and electrons. However, isotopes of Strontium have varying numbers of neutrons. These differences in neutrons produce different energy levels in the atom that allow spin qubits to be created. Atom Computing uses the isotope Strontium-87 and takes advantage of its unique energy levels to create spin qubits.

It is important for qubits to remain in a quantum state long enough to complete running the quantum circuits. The time that a qubit retains its quantum state is called its coherence time. Neutral atom qubits have a longer coherence time than most other qubit technologies.

Lasers instead of wires are used for precision control of the strontium-87 qubits. Lasers eliminates wiring, which can create radiation and noise that negatively affects coherence.

There are many other technical reasons for using neutral atom spin qubits but beyond the scope of this article.

Second generation plans

Artist rendering of Atom Computings second-generation quantum

With its latest $60M Series B funding, Atom Computing plans to build a larger, second-generation neutral-atom quantum computer. Many additional qubits will give the system increased computational ability. Atom Computing is currently likely to have undisclosed customer trials and use cases in progress. However, we expect new and more significant use cases to be publicly announced once the new quantum system is operational.

Patrick Moorhead, president and chief analyst of Moor Insights and Strategy, said, Qubit coherence, fidelity, and scalability are essential factors for achieving quantum advantage. Atom Computing has already demonstrated that Phoenix, its first-generation 100+ nuclear-spin qubit quantum processor, has the potential to check all those boxes. With the additional $60M Series B funding, I believe Atom could build a large qubit, second-generation quantum system that either brings it to the edge of quantum advantage or possibly even achieves it.

Analyst notes:

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

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Atom Computing Plans To Build A Bigger And Better High-Tech Quantum Computer With Its Latest $60 Million Series B Funding - Forbes

After years of speculation, quantum computing is heresort of.

Physicists are beginning to consider how quantum computing could provide answers to the deepest questions in the field. But most arent getting caught up in the hype. Instead, they are taking what for them is a familiar tackplanning for a future that is still decades out, while making room for pivots, turns and potential breakthroughs along the way.

When were working on building a new particle collider, that sort of project can take 40 years, says Hank Lamm, an associate scientist at the US Department of Energys Fermi National Accelerator Laboratory. This is on the same timeline. I hope to start seeing quantum computing provide big answers for particle physics before I die. But that doesnt mean there isnt interesting physics to do along the way.

Classical computers have been central to physics research for decades, and simulations that run on classical computers have guided many breakthroughs. Fermilab, for example, has used classical computing to simulate lattice quantum chromodynamics. Lattice QCD is a set of equations that describe the interactions of quarks and gluons via the strong force.

Theorists developed lattice QCD in the 1970s. But applying its equations provedextremely difficult. Even back in the 1980s, many people said that even if they had an exascale computer [a computer that can perform a billion billion calculations per second], they still couldnt calculate lattice QCD, Lamm says.

But that turned out not to be true.

Within the past 10 to 15 years, researchers have discovered the algorithms needed to make their calculations more manageable, while learning to understand theoretical errors and how to ameliorate them. These advances have allowed them to use a lattice simulation, a simulation that uses a volume of a specified grid of points in space and time as a substitute for the continuous vastness of reality.

Lattice simulations have allowed physicists to calculate the mass of the protona particle made up of quarks and gluons all interacting via the strong forceand find that the theoretical prediction lines up well with the experimental result. The simulations have also allowed them to accurately predict the temperature at which quarks should detach from one another in a quark-gluon plasma.

The limit of these calculations? Along with being approximate, or based on a confined, hypothetical area of space, only certain properties can be computed efficiently. Try to look at more than that, and even the biggest high-performance computer cannot handle all of the possibilities.

Enter quantum computers.

Quantum computers are all about possibilities. Classical computers dont have the memory to compute the many possible outcomes of lattice QCD problems, but quantum computers take advantage of quantum mechanics to calculate differently.

Quantum computing isnt an easy answer, though. Solving equations on a quantum computer requires completely new ways of thinking about programming and algorithms.

Using a classical computer, when you program code, you can look at its state at all times. You can check a classical computers work before its done and trouble-shoot if things go wrong. But under the laws of quantum mechanics, you cannot observe any intermediate step of a quantum computation without corrupting the computation; you can observe only the final state.

That means you cant store any information in an intermediate state and bring it back later, and you cannot clone information from one set of qubits into another, making error correction difficult.

It can be a nightmare designing an algorithm for quantum computation, says Lamm, who spends his days trying to figure out how to do quantum simulations for high-energy physics. Everything has to be redesigned from the ground up. We are right at the beginning of understanding how to do this.

Quantum computers have already proved useful in basic research. Condensed matter physicistswhose research relates to phases of matterhave spent much more time than particle physicists thinking about how quantum computers and simulators can help them. They have used quantum simulators to explore quantum spin liquid states and to observe a previously unobserved phase of matter called aprethermal time crystal.

The biggest place where quantum simulators will have an impact is in discovery science, in discovering new phenomena like this that exist in nature, says Norman Yao, an assistant professor at University of California Berkeley and co-author on the time crystal paper.

Quantum computers are showing promise in particle physics and astrophysics. Many physics and astrophysics researchers are using quantum computers to simulate toy problemssmall, simple versions of much more complicated problems. They have, for example, used quantum computing to test parts of theories of quantum gravity or create proof-of-principle models, like models of theparton showers that emit from particle colliderssuch as the Large Hadron Collider.

"Physicists are taking on the small problems, ones that they can solve with other ways, to try to understand how quantum computing can have an advantage, says Roni Harnik, a scientist at Fermilab. Learning from this, they can build a ladder of simulations, through trial and error, to more difficult problems.

But just which approaches will succeed, and which will lead to dead ends, remains to be seen. Estimates of how many qubits will be needed to simulate big enough problems in physics to get breakthroughs range from thousands to (more likely) millions. Many in the field expect this to be possible in the 2030s or 2040s.

In high-energy physics, problems like these are clearly a regime in which quantum computers will have an advantage, says Ning Bao, associate computational scientist at Brookhaven National Laboratory. The problem is that quantum computers are still too limited in what they can do.

Some physicists are coming at things from a different perspective: Theyre looking to physics to better understand quantum computing.

John Preskill is a physics professor at Caltech and an early leader in the field of quantum computing. A few years ago, he and Patrick Hayden, professor of physics at Stanford University, showed that if you entangled two photons and threw one into a black hole, decoding the information that eventually came back out via Hawking radiation would be significantly easier than if you had used non-entangled particles. Physicists Beni Yoshida and Alexei Kitaev then came up with an explicit protocol for such decoding, and Yao went a step further, showing that protocol could also be a powerful tool in characterizing quantum computers.

We took something that was thought about in terms of high-energy physics and quantum information science, then thought of it as a tool that could be used in quantum computing, Yao says.

That sort of cross-disciplinary thinking will be key to moving the field forward, physicists say.

Everyone is coming into this field with different expertise, Bao says. From computing, or physics, or quantum information theoryeveryone gets together to bring different perspectives and figure out problems. There are probably many ways of using quantum computing to study physics that we cant predict right now, and it will just be a matter of getting the right two people in a room together.

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Going beyond the exascale | symmetry magazine - Symmetry magazine

Baidu Research's AI-centric "Top 10 Tech Trends in 2022" report has outlined the Middle Kingdom megacorp's predictions for technology over the coming year.

Baidu CTO Haifeng Wang describes AI as a "key driving force of innovation and development," thanks to rapidly evolving core technologies, cross-domain connectivity, and expanding applications.

It's no surprise that the list focuses on AI given Baidu's business domain. The Beijing-based company's search engine captures over 70 per cent of the Chinese market while also developing other products, particularly AI research and cloud computing. The research arm takes a deeper look at its associated technologies. Think Google but Chinese.

A similar list was produced by Alibaba's DAMO Academy earlier this month. Alibaba's business features e-commerce, cloud computing, AI, and fintech products among others. DAMO is its research arm.

Although DAMO's list features AI, it does so with less focus than Baidu Research, resulting in both overlapping predictions and completely different takes.

Not mentioned by Baidu Research but included in DAMO's list were the use of silicon photonic chips for high-speed data transmission and replacing electronic chips in computing, and soft-bodied robotics whose flexibility and enhanced perception could potentially change the course of manufacturing industry.

Three areas Baidu Research covered that Alibaba did not are autonomous driving, inclusivity, and the integration of quantum hardware and software. The pair had similar thoughts when it comes to foundation models, AI's effect on science, and privacy computing. Space exploration, humans merging with robots, and sustainability factors made both company's lists, albeit with different approaches or outlooks.

Given that Baidu operates an L4 autonomous taxi service in a 60km2 area of Beijing, it's not surprising that the list might refer to robocars as "the next generation of vehicles that will give rise to a revolution in transportation," although the company doesn't give too much detail on what that will look like.

Baidu said AI will become more inclusive, but in a convoluted way: small and medium-sized enterprises will be able to access open-source platforms to improve their businesses, thus supporting more diverse sources of innovation.

Already becoming widely implemented, Baidu Research thinks the technology should continue to improve by increasing qubits in scale and reducing or adapting to noise.

Users will start having access to more back-end options on cloud-native quantum computing platforms, gradually proving commercial potential for the integration of quantum hardware and software.

"Government agencies, academia and the industry will collaborate more closely to build high-quality quantum devices and cultivate quantum technology talent, taking the first steps toward creating a quantum computing industry chain," Baidu Research predicted.

Both the trend lists featured the self-supervised large-scale models trained on big data known as foundation models, and both predicted the world will see smaller ones as research and development shifts from attempts to increase the model size toward practical deployment.

"For example, built upon cross-modal large models, AI will drive the adoption of AIGC (AI Generated Content), which promises to spur creative cognition, improve content diversity and reduce production costs," said Baidu Research.

AI for science and medical uses is already pervasive. Baidu Research reckons it will further fuse data-driven and theoretical deduction and play a greater role in furthering fundamental discoveries.

In particular, the research arm expects AI-powered computational biology, spurred on by the success of mRNA vaccine technology, will be on the rise. Precision medicine will have an uptake as costs decline.

In an environment where security and compliance has become critical, thanks in part to global regulations, Baidu Research said privacy computing is not far off from development in areas like computational biology, financial analysis, and data regulations. Additionally, in the long-run, privacy computing technology may "push encrypted data circulation and computing by default," thereby creating an "infrastructure for user confidence."

Similarly, Alibaba said advanced algorithms for homomorphic encryption, which enables calculations on data without decrypting it, will hit a critical point reducing the need for computing power to support the encryption. The emergence of data trust entities to assist in sharing the sets across organizations was also mentioned.

Baidu Research also thinks AI is going to take hold in outer space, with sensor-loaded decision-making autonomous spacecraft roaming around extra-terrestrial planets carrying robotic arms, not only conducting scientific studies, but also mining moons and planets for resources, empowered by lessons learned in construction machinery automation to excavate continuously 24 hours a day. Oh yeah, and these spacebots are self-repairing as they build space laboratories and analyse big data. Really, they do everything.

Alibaba's space predictions of the future were less imaginative, focusing on the very real demand for increased satellite connectivity and how they can improve through increased interconnectivity.

With social distancing kickstarting human digital presence, and continuing to be a necessity for many for the foreseeable future, Baidu Research said humans will continue to morph into avatars and robots. Of course, AI will play a role as it infiltrates and replicates human senses and XR (extended reality) improves.

As cyborg tech becomes the norm, more platforms will want in on the market. As a result, the world will see more virtual and real mashup experiences, both in consumer situations and professional life, not only reinforcing the digital economy, but also merging with the "real" economy.

Alibaba addressed the concept of human and machine mashups, but kept it simple, with XR glasses being what drives the success of technologies such as cloud-edge computing, network communications, and digital twins.

As for going green, Baidu painted a picture of AI equipment operating in a more sustainable manner. For instance, AI processors with higher computing power and lower energy consumption will continue to be invented, but also AI technology can be applied to tasks like improving energy efficiency ratios.

Meanwhile, Alibaba's green predictions centre around how AI can help connect the dots of things like renewable energy. For instance, "intelligent scheduling using deep learning techniques could optimise scheduling policies across energy sources like wind, solar, and hydroelectric."

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AI-flavored predictions for 2022 from Baidu The Register - The Register