A few weeks ago, I woke up unusually early in the morning in Brooklyn, got in my car, and headed up the Hudson River to the small Westchester County community of Yorktown Heights. There, amid the rolling hills and old farmhouses, sits the Thomas J. Watson Research Center, the Eero Saarinen-designed, 1960s Jet Age-era headquarters for IBM Research.

Deep inside that building, through endless corridors and security gates guarded by iris scanners, is where the companys scientists are hard at work developing what IBM director of research Dario Gil told me is the next branch of computing: quantum computers.

I was at the Watson Center to preview IBMs updated technical roadmap for achieving large-scale, practical quantum computing. This involved a great deal of talk about qubit count, quantum coherence, error mitigation, software orchestration and other topics youd need to be an electrical engineer with a background in computer science and a familiarity with quantum mechanics to fully follow.

I am not any of those things, but I have watched the quantum computing space long enough to know that the work being done here by IBM researchers along with their competitors at companies like Google and Microsoft, along with countless startups around the world stands to drive the next great leap in computing. Which, given that computing is a horizontal technology that touches everything, as Gil told me, will have major implications for progress in everything from cybersecurity to artificial intelligence to designing better batteries.

Provided, of course, they can actually make these things work.

The best way to understand a quantum computer short of setting aside several years for grad school at MIT or Caltech is to compare it to the kind of machine Im typing this piece on: a classical computer.

My MacBook Air runs on an M1 chip, which is packed with 16 billion transistors. Each of those transistors can represent either the 1 or 0 of binary information at a single time a bit. The sheer number of transistors is what gives the machine its computing power.

Sixteen billion transistors packed onto a 120.5 sq. mm chip is a lot TRADIC, the first transistorized computer, had fewer than 800. The semiconductor industrys ability to engineer ever more transistors onto a chip, a trend forecast by Intel co-founder Gordon Moore in the law that bears his name, is what has made possible the exponential growth of computing power, which in turn has made possible pretty much everything else.

But there are things classic computers cant do that theyll never be able to do, no matter how many transistors get stuffed onto a square of silicon in a Taiwan semiconductor fabrication plant (or fab, in industry lingo). And thats where the unique and frankly weird properties of quantum computers come in.

Instead of bits, quantum computers process information using qubits, which can represent 0 and 1 simultaneously. How do they do that? Youre straining my level of expertise here, but essentially qubits make use of the quantum mechanical phenomenon known as superposition, whereby the properties of some subatomic particles are not defined until theyre measured. Think of Schrdingers cat, simultaneously dead and alive until you open its box.

A single qubit is cute, but things get really exciting when you start adding more. Classic computing power increases linearly with the addition of each transistor, but a quantum computers power increases exponentially with the addition of each new reliable qubit. Thats because of another quantum mechanical property called entanglement, whereby the individual probabilities of each qubit can be affected by the other qubits in the system.

All of which means that the upper limit of a workable quantum computers power far exceeds what would be possible in classic computing.

So quantum computers could theoretically solve problems that a classic computer, no matter how powerful, never could. What kind of problems? How about the fundamental nature of material reality, which, after all, ultimately runs on quantum mechanics, not classical mechanics? (Sorry, Newton.) Quantum computers simulate problems that we find in nature and in chemistry, said Jay Gambetta, IBMs vice president of quantum computing.

Quantum computers could simulate the properties of a theoretical battery to help design one that is far more efficient and powerful than todays versions. They could untangle complex logistical problems, discover optimal delivery routes, or enhance forecasts for climate science.

On the security side, quantum computers could break cryptography methods, potentially rendering everything from emails to financial data to national secrets insecure which is why the race for quantum supremacy is also an international competition, one that the Chinese government is pouring billions into. Those concerns helped prompt the White House earlier this month to release a new memorandum to architect national leadership in quantum computing and prepare the country for quantum-assisted cybersecurity threats.

Beyond the security issues, the potential financial upsides could be significant. Companies are already offering early quantum-computing services via the cloud for clients like Exxon Mobil and the Spanish bank BBVA. While the global quantum-computing market was worth less than $500 million in 2020, International Data Corporation projects that it will reach $8.6 billion in revenue by 2027, with more than $16 billion in investments.

But none of that will be possible unless researchers can do the hard engineering work of turning a quantum computer from what is still largely a scientific experiment into a reliable industry.

Inside the Watson building, Jerry Chow who directs IBMs experimental quantum computer center opened a 9-foot glass cube to show me something that looked like a chandelier made out of gold: IBMs Quantum System One. Much of the chandelier is essentially a high-tech fridge, with coils that carry superfluids capable of cooling the hardware to 100th of a degree Celsius above absolute zero colder, Chow told me, than outer space.

Refrigeration is key to making IBMs quantum computers work, and it also demonstrates why doing so is such an engineering challenge. While quantum computers are potentially far more powerful than their classic counterparts, theyre also far, far more finicky.

Remember what I said about the quantum properties of superposition and entanglement? While qubits can do things a mere bit could never dream of, the slightest variation in temperature or noise or radiation can cause them to lose those properties through something called decoherence.

That fancy refrigeration is designed to keep the systems qubits from decohering before the computer has completed its calculations. The very earliest superconducting qubits lost coherence in less than a nanosecond, while today IBMs most advanced quantum computers can maintain coherence for as many as 400 microseconds. (Each second contains 1 million microseconds.)

The challenge IBM and other companies face is engineering quantum computers that are less error-prone while scaling the systems beyond thousands or even tens of thousands of qubits to perhaps millions of them, Chow said.

That could be years off. Last year, IBM introduced the Eagle, a 127-qubit processor, and in its new technical roadmap, it aims to unveil a 433-qubit processor called the Osprey later this year, and a 4,000-plus qubit computer by 2025. By that time, quantum computing could move beyond the experimentation phase, IBM CEO Arvind Krishna told reporters at a press event earlier this month.

Plenty of experts are skeptical that IBM or any of its competitors will ever get there, raising the possibility that the engineering problems presented by quantum computers are simply too hard for the systems to ever be truly reliable. Whats happened over the last decade is that there have been a tremendous number of claims about the more immediate things you can do with a quantum computer, like solve all these machine learning problems, Scott Aaronson, a quantum computing expert at the University of Texas, told me last year. But these claims are about 90 percent bullshit. To fulfill that promise, youre going to need some revolutionary development.

In an increasingly digital world, further progress will depend on our ability to get ever more out of the computers we create. And that will depend on the work of researchers like Chow and his colleagues, toiling away in windowless labs to achieve a revolutionary new development around some of the hardest problems in computer engineering and along the way, trying to build the future.

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Inside how IBMs engineers are designing quantum computers - Vox.com

The emerging technology of quantum computingcould revolutionize the fight against climate change, transforming the economics of decarbonization and becoming a major factor in limiting global warming to the target temperature of 1.5C (see sidebar What is quantum computing?).

Even though the technology is in the early stages of developmentexperts estimate the first generation of fault-tolerant quantum computing will arrive in the second half of this decadebreakthroughs are accelerating, investment dollars are pouring in, and start-ups are proliferating. Major tech companies have already developed small, so-called noisy intermediate-scale quantum (NISQ) machines, though these arent capable of performing the type of calculations that fully capable quantum computers are expected to perform.

Countries and corporates set ambitious new targets for reducing emissions at the 2021 United Nations Climate Change Conference (COP26). Those goals, if fully met, would represent an extraordinary annual investment of $4 trillion by 2030, the largest reallocation of capital in human history. But the measures would only reduce warming to between 1.7C and 1.8C by 2050, far short of the 1.5C level believed necessary to avoid catastrophic, runaway climate change.

Meeting the goal of net-zero emissions that countries and some industries have committed to wont be possible without huge advances in climate technology that arent achievable today. Even the most powerful supercomputers available now are not able to solve some of these problems. Quantum computing could be a game changer in those areas. In all, we think quantum computing could help develop climate technologies able to abate carbon on the order of 7 gigatons a year of additional CO2 impact by 2035, with the potential to bring the world in line with the 1.5C target.

Quantum computing could help reduce emissions in some of the most challenging or emissions-intensive areas, such as agriculture or direct-air capture, and could accelerate improvements in technologies required at great scale, such as solar panels or batteries. This article offers a look at some of the breakthroughs the technology could permit and attempts to quantify the impact of leveraging quantum-computer technology that are expected become available this decade.

Quantum computing could bring about step changes throughout the economy that would have a huge impact on carbon abatement and carbon removal, including by helping to solve persistent sustainability problems such as curbing methane produced by agriculture, making the production of cement emissions-free, improving electric batteries for vehicles, developing significantly better renewable solar technology, finding a faster way to bring down the cost of hydrogen to make it a viable alternative to fossil fuels, and using green ammonia as a fuel and a fertilizer.

Addressing the five areas designated in the Climate Math Reportas key for decarbonization, we have identified quantum-computing use cases that can pave the way to a net-zero economy. We project that by 2035 the use cases listed below could make it possible to eliminate more than 7 gigatons of CO2 equivalent (CO2e) from the atmosphere a year, compared with the current trajectory, or in aggregate more than 150 gigatons over the next 30 years (Exhibit 1).

Exhibit 1

Batteries are a critical element of achieving zero-carbon electrification. They are required to reduce CO2 emissions from transportation and to obtain grid-scale energy storage for intermittent energy sources such as solar cells or wind.

Improving the energy density of lithium-ion (Li-ion) batteries enables applications in electric vehicles and energy storage at an affordable cost. Over the past ten years, however, innovation has stalledbattery energy density improved 50 percent between 2011 and 2016, but only 25 percent between 2016 and 2020, and is expected to improve by just 17 percent between 2020 and 2025.

Recent research has shown that quantum computing will be able to simulate the chemistry of batteries in ways that cant be achieved now. Quantum computing could allow breakthroughs by providing a better understanding of electrolyte complex formation, by helping to find a replacement material for cathode/anode with the same properties and/or by eliminating the battery separator.

As a result, we could create batteries with 50 percent higher energy density for use in heavy-goods electric vehicles, which could substantially bring forward their economic use. The carbon benefits to passenger EVs wouldnt be huge, as these vehicles are expected to reach cost parity in many countries before the first generation of quantum computers is online, but consumers might still enjoy cost savings.

In addition, higher-density energy batteries can serve as a grid-scale storage solution. The impact on the worlds grids could be transformative. Halving the cost of grid-scale storage could enable a step change in the use of solar power, which is becoming economically competitive but is challenged by its generation profile. Our modeling suggests that halving the cost of solar panels could increase their use by 25 percent in Europe by 2050 but halving both solar and batteries might increase solar use by 60 percent (Exhibit 2). Geographies without such a high carbon price will see even greater impacts.

Exhibit 2

Through the combination of use cases described above, improved batteries could bring about an additional reduction in carbon dioxide emissions of 1.4 gigatons by 2035.

Many parts of the industry produce emissions that are either extremely expensive or logistically challenging to abate.

Cement is a case in point. During calcination in the kiln for the process of making clinker, a powder used to make cement, CO2 is released from raw materials. This process accounts for approximately two-thirds of cement emissions.

Alternative cement-binding materials (or clinkers) can eliminate these emissions, but theres currently no mature alternative clinker that can significantly reduce emissions at an affordable cost.

There are many possible permutations for such a product, but testing by trial and error is time-consuming and costly. Quantum computing can help to simulate theoretical material combinations to find one that overcomes todays challengesdurability, availability of raw materials and efflorescence (in the case of alkali-activated binders). This would have an estimated additional impact of 1 gigaton a year by 2035.

Solar cells will be one of the key electricity-generation sources in a net-zero economy. But even though they are getting cheaper, they still are far from their theoretical maximum efficiency.

Todays solar cells rely on crystalline silicon and have an efficiency on the order of 20 percent. Solar cells based on perovskite crystal structures, which have a theoretical efficiency of up to 40 percent, could be a better alternative. They present challenges, however, because they lack long-term stability and could, in some varieties, be more toxic. Furthermore, the technology has not been mass produced yet.

Quantum computing could help tackle these challenges by allowing for precise simulation of perovskite structures in all combinations using different base atoms and doping, thereby identifying higher efficiency, higher durability, and nontoxic solutions. If the theoretical efficiency increase can be reached, the levelized cost of electricity (LCOE) would decrease by 50 percent.

By simulating the impact of cheaper and more efficient quantum-enabled solar panels, we see a significant increase in use in areas with lower carbon prices (China, for example). This is also true of countries in Europe with high irradiance (Spain, Greece) or poor conditions for wind energy (Hungary). The impact is magnified when combined with cheap battery storage, as discussed above.

This technology could abate an additional 0.4 gigatons of CO2 emissions by 2035.

Hydrogen is widely considered to be a viable replacement for fossil fuels in many parts of the economy, especially in industry where high temperature is needed and electrification isnt possible or sufficient, or where hydrogen is needed as a feedstock, such as steelmaking or ethylene production.

Before the 2022 gas price spikes, green hydrogen was about 60 percent more expensive than natural gas. But improving electrolysis could significantly decrease the cost of hydrogen.

Polymer electrolyte membrane (PEM) electrolyzers split water and are one way to make green hydrogen. They have improved in recent times but still face two major challenges.

Quantum computing can help model the energy state of pulse electrolysis to optimize catalyst usage, which would increase efficiency. Quantum computing could also model the chemical composition of catalysts and membranes to ensure the most efficient interactions. And it could push the efficiency of the electrolysis process up to 100 percent and reduce the cost of hydrogen by 35 percent. If combined with cheaper solar cells discovered by quantum computing (discussed above), the cost of hydrogen could be reduced by 60 percent (Exhibit 3).

Exhibit 3

Increased hydrogen use as a result of these improvements could reduce CO2 emissions by an additional 1.1 gigatons by 2035.

Ammonia is best known as a fertilizer, but could also be used as fuel, potentially making it one of the best decarbonization solutions for the worlds ships. Today, it represents 2 percent of total global final energy consumption.

For the moment, ammonia is made through the energy-intensive Haber-Bosch process using natural gas. There are several options for creating green ammonia, but they rely on similar processes. For example, green hydrogen can be used as a feedstock, or the carbon dioxide emissions that are caused by the process can be captured and stored.

However, there are other potential approaches, such as nitrogenase bioelectrocatalysis, which is how nitrogen fixation works naturally when plants take nitrogen gas directly from the air and nitrogenase enzymes catalyze its conversion into ammonia. This method is attractive because it can be done at room temperature and at 1 bar pressure, compared with 500C at high pressure using Haber-Bosch, which consumes large amounts of energy (in the form of natural gas) (Exhibit 4).

Exhibit 4

Innovation has reached a stage where it might be possible to replicate nitrogen fixation artificially, but only if we can overcome challenges such as enzyme stability, oxygen sensitivity, and low rates of ammonia production by nitrogenase. The concept works in the lab but not at scale.

Quantum computing can help simulate the process of enhancing the stability of the enzyme, protecting it from oxygen and improving the rate of ammonia production by nitrogenase. That would result in a 67 percent cost reduction over todays green ammonia produced through electrolysis, which would make green ammonia even cheaper than traditionally produced ammonia. Such a cost reduction could not only lessen the CO2 impacts of the production of ammonia for agricultural use but could also bring forward the breakeven for ammonia in shippingwhere it is expected to be a major decarbonization optionforward by ten years.

Using quantum computing to facilitate cheaper green ammonia as a shipping fuel could abate an additional CO2 by 0.4 gigatons by 2035.

Carbon capture is required to achieve net zero. Both types of carbon capturepoint source and directcould be aided by quantum computing.

Point-source carbon capture allows CO2 to be captured directly from industrial sources such as a cement or steel blast furnace. But the vast majority of CO2 capture is too expensive to be viable for now, mainly because it is energy intense.

One possible solution: novel solvents, such as water-lean and multiphase solvents, which could offer lower-energy requirements, but it is difficult to predict the properties of the potential material at a molecular level.

Quantum computing promises to enable more accurate modeling of molecular structure to design new, effective solvents for a range of CO2 sources, which could reduce the cost of the process by 30 to 50 percent.

We believe this has significant potential to decarbonize industrial processes, which could lead to additional decarbonization of up to 1.5 gigatons a year, including cement. If the cement clinker approach described above is successful, this would still have an effect of 0.5 gigatons a year, due to fuel emissions. In addition, alternative clinkers may not be available in some regions.

Direct-air capture, which involves sucking CO2 from the air, is a way to address carbon removals. While the Intergovernmental Panel on Climate Change says this approach is required to achieve net zero, it is very expensive (ranging from $250 to $600 per ton a day today) and even more energy intensive than point-source capture.

Adsorbents are best suited for effective direct-air capture and novel approaches, such as metal organic frameworks, or MOFs, have the potential to greatly reduce the energy requirements and the capital cost of the infrastructure. MOFs act like a giant spongeas little as a gram can have a surface area larger than a football fieldand can absorb and release CO2 at far lower temperature changes than conventional technology.

Quantum computing can help advance research on novel adsorbents such as MOFs and resolve challenges arising from sensitivity to oxidation, water, and degradation caused by CO2.

Novel adsorbents that have a higher adsorption rate could reduce the cost of technology to $100 per ton of CO2e captured. This could be a critical threshold for uptake, given that corporate climate leaders such as Microsoft have publicly announced an expectation to pay $100 a ton long term for the highest-quality carbon removals. This would lead to an additional CO2reduction of 0.7 gigatons a year by 2035.

Twenty percent of annual greenhouse-gas emissions come from agricultureand methane emitted by cattle and dairy is the primary contributor (7.9 gigatons of CO2e, based on 20-year global-warming potential).

Research has established that low-methane feed additives could effectively stop up to 90 percent of methane emissions. Yet applying those additives for free-range livestock is particularly difficult.

An alternative solution is an antimethane vaccine that produces methanogen-targeting antibodies. This method has had some success in lab conditions, but in a cows gutchurning with gastric juices and foodthe antibodies struggle to latch on to the right microbes. Quantum computing could accelerate the research to find the right antibodies by precise molecule simulation instead of a costly and long trial-and-error method. With estimated uptake determined according to data from the US Environmental Protection Agency, we arrive at carbon reduction of up to an additional 1 gigaton a year by 2035.

Another prominent use case in agriculture is green ammonia discussed as a fuel above, where todays Haber-Bosch process uses large amounts of natural gas. Using such an alternative process could have an additional impact of up to 0.25 gigatons a year by 2035, replacing current conventionally produced fertilizers.

There are many more ways that quantum computing could be applied to the fight against climate change. Future possibilities include identification of new thermal-storage materials, high-temperature superconductors as a future base for lower losses in grids, or simulations to support nuclear fusion. Use cases arent limited to climate mitigation, but can also apply to adaptation, for example, improvements in weather prediction to give greater warning of major climatic events. But progress on those innovations will have to wait because first-generation machines will not be powerful enough for such breakthroughs (see sidebar Methodology).

The leap in CO2 abatement could be a major opportunity for corporates. With $3 to $5 trillion in value at stake in sustainability, according to McKinsey research, climate investment is an imperative for big companies. The use cases presented above represent major shifts and potential disruptions in these areas, and they are associated with huge value for players who take the lead. This opportunity is recognized by industry leaders who are already developing capabilities and talent.

Nevertheless, quantum technology is in the early stage and comes with the risks linked to leading-edge technology development, as well as tremendous cost. We have highlightedthe stage of the industry in the Quantum Technology Monitor. The risk to investors can be mitigated somewhat through steps such as onboarding technical experts to run in-depth diligence, forming joint investments with public entities or consortia, and investing in companies that bundle various ventures under one roof and provide the necessary experience to set up and scale these ventures.

In addition, governments have an important role to play by creating programs at universities to develop quantum talent and by providing incentives for quantum innovation for climate, particularly for use cases that today do not have natural corporate partners, such as disaster prediction, or that arent economical, such as direct-air capture. Governments could start more research programs like the partnership between IBM and the United Kingdom, the collaboration between IBM and Fraunhofer-Gesellschaft, the publicprivate partnership Quantum Delta in the Netherlands, and the collaboration between the United States and the United Kingdom. By tapping into quantum computing for sustainability, countries will accelerate the green transition, achieve national commitments, and get a head start in export markets. But even with those measures, the risk and expense remain high (Exhibit 5).

Exhibit 5

Here are some questions corporates and investors need to ask before taking a leap into quantum computing.

Is quantum computing relevant for you?

Determine whether there are use cases that can potentially disrupt your industry or your investments and address the decarbonization challenges of your organization. This article has highlighted anecdotal use cases across several categories to showcase the potential impact of quantum computing, but weve identified more than 100 sustainability-relevant use cases where quantum computing could play a major role. Quickly identifying use cases that are applicable to you and deciding how to address them can be highly valuable, as talent and capacity will be scarce in this decade.

How do I approach quantum computing now, if it is relevant?

Once you have engaged on quantum computing, building the right kind of approach, mitigating risk and securing access to talent and capacity are key.

Because of the high cost of this research, corporates can maximize their impact by forming partnerships with other players from their value chains and pooling expense and talent. For example, major consumers of hydrogen might join up with electrolyzer manufacturers to bring down the cost and share the value. These arrangements will require companies to figure out how to share innovation without losing competitive advantage. Collaborations such as joint ventures or precompetitive R&D could be an answer. We also foresee investors willing to support such endeavors to potentially remove some of the risk for corporates. And there are large amounts of dedicated climate finance available, judging by pledges made at COP26 that aim to reach the target of $100 billion a year in spending.

Do I have to start now?

While the first fault-tolerant quantum computer is several years away, it is important to start development work now. There is significant prework to be done to get to a maximal return on the significant investment that application of quantum computing will require.

Determining the exact parameters of a given problem and finding the best possible application will mean collaboration between application experts and quantum-computing technicians well versed in algorithm development. We estimate algorithm development would take up to 18 months, depending on the complexity.

It will also take time to set up the value chain, production, and go-to-market to ensure they are ready when quantum computing can be deployed and to fully benefit from the value created.

Quantum computing is a revolutionary technology that could allow for precise molecular-level simulation and a deeper understanding of natures basic laws. As this article shows, its development over the next few years could help solve scientific problems that until recently were believed to be insoluble. Clearing away these roadblocks could make the difference between a sustainable future and climate catastrophe.

Making quantum computing a reality will require an exceptional mobilization of resources, expertise, and funds. Only close cooperation between governments, scientists, academics, and investors in developing this technology can make it possible to reach the target for limiting emissions that will keep global warming at 1.5C and save the planet.

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Quantum computing just might save the planet - McKinsey

Written by Jon Harper May 19, 2022 | FEDSCOOP

U.S. Special Operations Command is worried about the future threat from adversaries quantum technologies, and officials are trying to get out ahead of the problem.

Improving intelligence fusion through real-time data integration is a key pillar of SOCOMs plans for digital transformation. That data must not only be gathered, fused and transferred to the appropriate end users; it also has to be secured a challenge that will grow with the development of quantum computing capabilities.

How do we get after the way those bits and bytes interact with each other and create the intelligence that we need, while at the same time protecting that data, you know, ensuring that the data is trustworthy? Thomas Kenney, chief data officer at Special Operations Command, said Thursday at the SOFIC conference.

Heres a really interesting aspect of this that were looking at today because we know in a few years this is going to become really important by some accounts, were less than eight years away from quantum cryptography being able to break the non-quantum cryptography that we have today We need an answer for that, he said.

When the technology is ready for prime time, officials say it could be a game changer.

Data may very easily be decrypted by a capability that has a quantum decrypt capability, Kenney warned.

The time is now to be thinking about that problem before adversaries have already acquired that capability, he added.

Technology developers are putting a lot of effort into quantum computing, he noted, highlighting the implications of quantum processing.

One of the really interesting tenants of quantum computing is that you can compute multiple outcomes simultaneously. And when you think about the speed of battle and where were going to, that ability will be absolutely essential, Kenney said.

Quantum computing is being played with right now. And we look at where were going for quantum cryptography, we need about a factor of 1,000 qubits to be able to get to that next level, he said.

A qubit is a computing unit that leverages the principle of superposition the ability of quantum systems to exist in two or more states simultaneously to encode information, the Congressional Research Service explained in a recent report on the technology.

Whereas a classical computer encodes information in bits that can represent binary states of either 0 or 1, a quantum computer encodes information in qubits, each of which can represent 0, 1, or a combination of both at the same time. As a result, the power of a quantum computer increases exponentially with the addition of each qubit, according to CRS.

Being able to have multiple outcomes calculated at the same time on a battlefield thats happening extremely fast is going to be mission essential to us. Are the technologies there today? Maybe not. But they certainly need to be there in the future, so its something that were taking a look at, Kenney said.

Earlier this month, President Biden signed two new policy directives aimed at advancing U.S. quantum technologies and the ability to defend U.S. infrastructure against the threat posed by quantum computers.

See more here:
Special Operations Command trying to prepare for quantum computing threat - FedScoop

A collaboration looks to analyze the applicability of quantum computational algorithms to metal forming applications modeling.

Car manufacturer BMW and quantum computing technology developer Pasqal have entered a new phase of collaboration to analyze the applicability of quantum computational algorithms to metal forming applications modeling.

The automotive industry is one of the most demanding industrial environments, and quantum computing could solve some of the key design and manufacturing issues. According to a report by McKinsey, automotive will be one of the primary value pools for quantum computing, with a high impact noticeable by about 2025. The consulting firm also expects a significant economic impact of related technologies for the automotive industry, estimated at $2 billion to $3 billion, by 2030.

Volkswagen Group led the way with the launch of a dedicated quantum computing research team back in 2016.

BMW has been working with Pasqal since 2019 to develop quantum enhanced methods for chemistry and materials-science in the field of battery R&D, Benno Broer, CCO at Pasqal, told EE Times Europe .

The current collaboration, however, follows the BMW Group Quantum Computing Challenge in late 2021. The contest focused on four specific challenges where quantum computing could offer an advantage over classical computational methods, and Qu&Co was the winner in the category Simulation of material deformation in the production process. Qu&Co and Pasqal later merged their businesses, combining Qu&Cos robust portfolio of algorithms with Pasqals full-stack neutral-atom system to accelerate the quantum path to commercial applications. The united business is known as Pasqal and located in Paris.

The reason we were chosen is because our proprietary method to solve complex differential equations is currently the only realistic method to solve such problems on near-term quantum processors, said Broer. The material deformation problems we will now work on with BMW Group are governed by such differential equations.

Pascal said its team of researchers has developed a digital-analog implementation of its quantum methods, tailored for its neutral-atom quantum processors, which makes these applications 30 times more efficient than on competing superconducting quantum processors.

When asked to provide more details on this digital-analog approach, Broer explained, Our approach requires us to create a significant amount of quantum entanglement between our qubits. Intuitively: the more entanglement we create the more powerful (more accurate) our method becomes. In a fully digital implementation, we create this entanglement by applying 2-qubit gate operations (which entangle 2 qubits). In the digital-analog version of the algorithm, we replace this entangling operation by an analog operation, which is a multi-qubit operation. The replacement of the 2-qubit gates by this analog multi-qubit operation makes the method much more efficient, and at the same time more noise robust.

The result is that we can generate much more entanglement in the time we have before the quantum processor becomes decoherent (it loses its quantumness due to the inherent noise in all current day quantum processors). And again: More entanglement means a more powerful solver.

Pasqals digital-analog approach is described in more detail in the blogpost, Neutral Atom Quantum Computing for Physics-Informed Machine Learning .

The simulations will run in Pasqals facilities over a six-month period.

As to when the first car models optimized with Pasqals simulations will hit the roads, Broer said it is too early to tell. What we can say is that Pasqal expects to be able to showcase the first industry relevant quantum advantage with our differential equation solvers in 2024. We cannot yet guarantee that those first quantum advantage showcases will be for the application of materials deformation.

Real-world applications of these simulations include crash testing and accelerated development of new, lighter, stronger parts and materials that ensure passenger safety while reducing emissions and development costs, the company said.

The reduction in development costs that Pasqals simulations may allow BMW to achieve cannot be quantified at this point of time, said Broer. In general, we see a trend towards replacing costly and time-consuming build-and-test cycles in automotive R&D with digital research (creating digital twins of the car or car parts). The financial benefit related to this should be quantified in both the cost saved for the physical build-and-test process, the cost of the material saved (using less metal while maintaining the same structural strength), and perhaps most importantly the significantly improved time-to-market of a new generation of cars.

He added, Our quantum methods provide the required extra computational power to enable accurate digital twin type simulations of larger and more complex parts of a car or perhaps someday a full car.

Pasqals quantum computational simulation, now applied to cars, could be used for other sectors. For each new class of differential equation problems, Broer said Pasqal has to parameterize its quantum algorithms to be able to solve that specific class. Once we can solve the problem of material deformation, we can use these solvers to also tackle problems outside of this field where the differential equations have a similar structure.

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Quantum computing takes on automotive design and manufacturing - Embedded

Growing up as a scholar-athlete in Texas, Seun Omonije already knew plenty of football moves before he arrived in New Haven nearly four years ago.

But Yale taught the 22-year-old graduating senior from Silliman College a new move the quantum pivot.

In 2020, after the Ivy League canceled the football season due to public health concerns over COVID-19, Omonije, a wide receiver, was able to shift more of his attention to his computer science major. That decision, he says, led him to new friendships, new research opportunities, and a clear path to a career in quantum computing.

I made a pivot, Omonije says. I still trained my hardest, physically, but I kept doing more coding, kept learning new quantum concepts. I found ways to apply myself to other things.

Omonije is a founding board member of the Yale Undergraduate Quantum Computing (YuQC) group whose 2021 Quantum Coalition Hack with Stanford attracted 2,100 entrants from 80 countries and he served as a teaching assistant for a software engineering course on campus.

He has completed a pair of software engineering internships at Google, one working remotely with Google Cloud in Los Angeles and the other with Google Quantum AI in the San Francisco Bay area. Omonije helped build the first tool for 3D visualizations of quantum circuits among other models in quantum theory using Python and Typescript. After graduation, hell be moving to Los Angeles and taking a full-time job at Google Quantum AI.

He credits Yale for his quantum cred.

The faculty and coursework exposed me to so many areas of fundamental quantum theory and computation, and ways I can apply those concepts, Omonije says. I was actually doing research on quantum control systems and quantum software. Yale showed me what was possible in quantum computing.

Now that hes nearly finished his senior year a year in which injuries kept him off the playing field during games Omonije has begun to reflect on his Yale experience. He said he is grateful for the friendships hes made among his football teammates, his close-knit community at Silliman, and his fellow students at YuQC. Likewise, hes glad to have had the chance to sample so much that Yale had to offer, including fine arts and performing arts.

When I came to Yale, I didnt know what I wanted to do or who I wanted to be, he says. Im proud of the personal growth I experienced, that through all of the stuff that didnt go the way I planned, I still accomplished so many things.

All things considered, this is just the beginning of my journey, and Im looking forward to putting my head down and getting to work on this next chapter of life.

Originally posted here:
Seun Omonije took the gridiron-to-quantum-computing route - Yale News

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Quantum Computing Market Innovative Strategy by 2030 | Toshiba Corporation, Nippon Telegraph And Telephone Corporation (NTT), Quantum Circuits Inc ...