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    A massive cavern beneath a West Antarctic glacier is teeming with life

    The coastal plain of the Kamb Ice Stream, a West Antarctic glacier, hardly seems like a coast at all. Stand in this place, 800 kilometers from the South Pole, and you see nothing but flat ice extending in every direction. The ice is some 700 meters thick and stretches for hundreds of kilometers off the coastline, floating on the water. On clear summer days, the ice reflects the sunlight with such ferocity that it inflicts sunburn in the insides of your nostrils. It might seem hard to believe, but hidden beneath this ice is a muddy tidal marsh, where a burbling river wends its way into the ocean.

    Until recently, no human had ever glimpsed that secret landscape. Scientists had merely inferred its existence from the faint reflections of radar and seismic waves. But in the closing days of 2021, a team of scientists from New Zealand melted a narrow hole through the glacier’s ice and lowered in a camera. They had hoped that their hole would intersect with the river, which they believed had melted a channel up into the ice — a vast water-filled cavity, nearly tall enough to hold the Empire State Building and half as long as Manhattan. On December 29, Craig Stevens finally got his first look inside. It is a moment that he will always remember.

    Stevens is a physical oceanographer with New Zealand’s National Institute of Water and Atmospheric Research in Wellington. He spent 90 anxious minutes that day in Antarctica with his head buried ostrich-style under a thick down jacket to block the sunlight that would otherwise obscure his computer monitor. There, he watched live video from the camera as it descended into the hole. Icy circular walls scrolled past, reminiscent of a cosmic wormhole. Suddenly, at a depth of 502 meters, the walls widened out.

    Stevens shouted for a colleague to halt the winch lowering the camera. He stared at the screen as the camera rotated idly on its cable. Its floodlights raked across a ceiling of glacial ice — a startling sight — scalloped into delicate crests and waves. It resembled the dreamy undulations that might take millennia to form in a limestone cavern.

    The Kamb Ice Stream is located on the coast of West Antarctica and flows into the Ross Ice Shelf, a slab of floating ice hundreds of meters thick. The site of the newly discovered cavern is shown as a yellow box.A. WHITEFORD ET AL/JOURNAL OF GEOPHYSICAL RESEARCH: EARTH SURFACE 2022

    “The interior of a cathedral,” says Stevens. A cathedral not only in beauty, but also in size. As the winch restarted, the camera journeyed downward for another half hour, through 242 meters of sunless water. Bits of reflective silt stirred up by currents streamed back down like snowflakes through the black void.

    Stevens and his colleagues spent the next two weeks lowering instruments into the void. Their observations revealed that this coastal river has melted a massive, steep-walled cavern cutting as far as 350 meters up into the overlying ice. The cavern extends for at least 10 kilometers and appears to be boring inland, farther upstream, into the ice sheet with each passing year.

    This cavity offers researchers a window into the network of subglacial rivers and lakes that extends hundreds of kilometers inland in this part of West Antarctica. It’s an otherworldly environment that humans have barely explored and is laden with evidence of Antarctica’s warm, distant past, when it was still inhabited by a few stunted trees.

    Researchers got their first glimpse into the hidden landscape in late 2021, when they drilled through 500 meters of ice and lowered in instruments to observe the cavern below (borehole shown).C. STEVENS/NIWA (CC BY-ND)

    One of the biggest surprises came as the camera reached bottom that day. Stevens gazed in disbelief as dozens of orange blurs swam and darted on his monitor — evidence that this place, roughly 500 kilometers from the open, sunlit ocean, is nonetheless bustling with marine animals.

    Seeing them was “just complete shock,” says Huw Horgan, a glaciologist formerly at the Victoria University of Wellington who led the drilling expedition.

    Horgan, who recently moved to ETH Zurich, wants to know how much water is flowing through the cavern and how its growth will impact the Kamb Ice Stream over time. Kamb is unlikely to fall apart anytime soon; this part of West Antarctica is not immediately threatened by climate change. But the cavern might still offer clues to how subglacial water could affect more vulnerable glaciers.

    What’s beneath Antarctica’s ice sheet?

    Scientists have long surmised that a veneer of liquid water sits beneath much of the ice sheet covering Antarctica. This water forms as the bottom of the ice slowly melts, several penny-thicknesses per year, due to heat seeping from the Earth’s interior. In 2007, Helen Amanda Fricker, a glaciologist at the Scripps Institution of Oceanography in La Jolla, Calif., reported evidence that this water pools into large lakes beneath the ice and can flood quickly from one lake to another (SN: 6/17/06, p. 382).

    Fricker was looking at data from NASA’s Ice, Cloud and Land Elevation Satellite, or ICESat, which measures the height of the ice surface by reflecting a laser off of it. The surface at several spots in West Antarctica seemed to bob up and down, rising and falling by as much as nine meters over a couple of years. She interpreted these active spots as subglacial lakes. As they filled and then spilled out their water, the overlying ice rose and fell. Fricker’s team and several others eventually found over 350 of these lakes scattered around Antarctica, including a couple dozen beneath Kamb and its neighboring glacier, the Whillans Ice Stream.

    The lakes provoked great interest because they were expected to harbor life and might provide insights about what sorts of organisms could survive on other worlds — deep within the ice-covered moons of Jupiter and Saturn, for instance. The layers of sediment in Antarctica’s lakes might also offer glimpses into the continent’s ancient climate, ecosystems and ice cover. Teams funded by Russia, the United Kingdom and the United States attempted to drill into subglacial lakes. In 2013, the U.S.-led team succeeded, melting through 800 meters of ice and tapping into a reservoir called Subglacial Lake Whillans. It was teeming with microbes, 130,000 cells per milliliter of lake water (SN: 9/20/14, p. 10).

    Horgan helped map Lake Whillans before drilling began. But by the time the lake was breached, he and others were becoming intrigued with another facet of the subglacial landscape — the rivers thought to carry water from one lake to another, and eventually to the ocean.

    Finding these hidden rivers requires complicated guesswork. Their flow paths are influenced not only by the subglacial topography, but also by differences in the thickness of the overlying ice. Water moves from places where the ice is thick (and the pressure high) to places where it is thinner (and the pressure lower) — meaning that rivers can sometimes run uphill.

    By 2015, scientists had mapped the likely paths of several dozen subglacial rivers. But drilling into them still seemed farfetched. The rivers are narrow targets and their exact locations often uncertain. But around that time, Horgan got a lucky break.

    While examining a satellite photo of the Kamb Ice Stream, he noticed a wrinkle in the pixelated tapestry of the image. The wrinkle resembled a long, shallow trough in the surface of the ice, as if the ice had sagged from melting beneath. The trough sat several kilometers from the hypothetical path of one subglacial river. Horgan believed that it marked the spot where that river flowed over the coastal plain and spilled into the ice-covered sea.

    In 2016, while visiting the area for an unrelated research project, Horgan and his companions detoured briefly to the surface trough to take radar measurements. Sure enough, they found a void under the ice, filled with liquid water. Horgan began making plans to study it more closely. He would return twice in the next few years, once to map the river in detail and a second time to drill into it. What he found greatly exceeded his expectations.

    Scientists map a subglacial landscape

    Horgan and graduate student Arran Whiteford of the Victoria University of Wellington visited the lower Kamb Ice Stream to map the river in December 2019.

    After weeks on the Antarctic ice sheet, they’d grown accustomed to its monotonous flat landscape, their perception sensitized to even tiny ups and downs. In this context, the surface trough “looked like this massive chasm,” Whiteford says, “like an amphitheater” — even though it slanted no more dramatically than a rolling cornfield in Iowa.

    It was a week of scientific drudgery, towing the ice-penetrating radar behind a snowmobile along a series of straight, parallel lines that crisscrossed the trough to map the shape of the river channel under the ice.

    Horgan and Whiteford worked up to 12 hours per day, occasionally trading positions. One person drove the snowmobile, straining his thumb on the throttle to maintain a constant 8 kilometers per hour. Two sleds hissed along behind. One held a transmitter that fired radar waves into the glacier below; the other held an antenna that received the signal reflected back off the bottom of the ice. The second person rode on the sled with the antenna, his eyes on a bouncing laptop screen making sure that the radar was functioning.

    Researchers deploy instruments through a borehole into the water-filled cavern hidden beneath the Kamb Ice Stream.H. HORGAN

    Each evening they huddled in their tent, reviewing their radar traces. The river channel appeared far more dramatic than the gentle dip atop the ice suggested. Below their boots sat a vast water-filled cavern with steep sides like a train tunnel, 200 meters to a kilometer wide and cutting as much as 50 percent of the way up through the glacier. The more they looked, the more it resembled a river. “It kind of meanders downstream,” Whiteford says.

    All told, Whiteford made two weeklong visits to the trough, snowmobiling over from another camp 50 kilometers away. The first time he was accompanied by Horgan, and the second time by another graduate student, Martin Forbes.

    After returning home to New Zealand in January 2020, Whiteford examined a series of old satellite images. They showed that the surface trough — and hence, the cavern — had begun forming at least 35 years before, starting with a blip at the very mouth of the river, where it ran into the ocean. That blip had gradually lengthened, reaching progressively farther inland, or upstream. Whiteford and Horgan reported the observations in late 2022 in the Journal of Geophysical Research: Earth Surface — along with their theory about how the cavern formed.

    In other parts of Antarctica where the ice sheet protrudes off the coastline, scientists have found that the ice’s underside is often insulated from the ocean heat by a buoyant layer of colder, fresher meltwater. That protective layer is sometimes only a couple of meters thick. But Horgan and Whiteford suspect that the turbulence of the subglacial river flowing into the ocean stirs up that protective layer, causing seawater — a few tenths of a degree warmer than the subglacial water — to swirl up into contact with the ice. This causes an area of concentrated melt right at the river’s mouth, creating a small cavity where warm seawater can intrude further.

    In this way, says Horgan, the focal point of melting is “stepping back over time.” And the cavern gradually burrows farther upstream into the ice.

    Whiteford used a different set of satellite measurements — which measured the rate at which the ice’s surface sank over time — to determine how quickly the ice was melting in the cavern below. Based on this, he estimated that in the upstream end of the cavern, the ice (currently 350 to 500 meters thick over the channel) was melting and thinning 35 meters per year. That’s an astronomical rate. It’s 135 times what has been measured 50 kilometers southwest of the cavern, where the ice floats on the ocean. The water temperature is probably similar at both locations. But the turbulence caused by the river transfers the water’s heat far more efficiently into the ice.

    Horgan thinks that the cavern at Kamb also owes its dramatic height to another factor. Glaciers in this part of West Antarctica generally flow several hundred meters per year. So the melt caused by a flowing river beneath, over years or decades, would normally be spread out over a long swath of ice. This would erode a shallow channel rather than a deep cleft. But Kamb is an oddball. Around 150 years ago, it stopped moving almost entirely due to the cyclical interplay of melting and freezing at its base. It now creeps forward only about 10 meters per year. The melting is thus concentrated, year after year, in almost the same spot.

    Back in 2020, all of this was still conjecture. But if Horgan and his colleagues could return, drill into the cavern and lower instruments into it, they could confirm how it formed. By studying the water, sediment and microbes flowing out of it, they could also learn a lot about Antarctica’s vast subglacial landscape.

    The West Antarctic Ice Sheet covers an area three times the size of the Colorado River drainage basin, which sprawls across Arizona, Utah, Colorado and parts of four other states. To date, humans have observed only a tiny swath of this underworld, smaller than a basketball court — represented by several dozen narrow boreholes scattered across the region, where scientists have grabbed a bit of mud from the bottom or sometimes lowered in a camera.

    Horgan was eager to explore more. With New Zealand already melting boreholes through ice floating on the ocean, drilling into this coastal river seemed like a natural next step.

    How did the hidden cavern form?

    On December 4, 2021, a pair of caterpillar-tracked PistenBullys arrived at the place where Horgan and Whiteford had visited two years before. The tractors had traveled for 16 days from New Zealand’s Scott Base on the edge of the continent, growling across a thousand kilometers of floating ice as they towed a convoy of sleds packed with 90 metric tons of food, fuel and scientific gear. The convoy lumbered around to the upstream end of the valley and stopped.

    Workers erected a tent the size of a small aircraft hangar, and inside it, assembled a series of water heaters, pumps and a kilometer of hose — a machine called a hot water drill. Using shovels and a small mechanized scooper, they dumped 54 tons of snow into a tank and melted it. The workers then jetted that hot water through the hose, using it to melt a narrow hole, no wider than a dinner plate, through 500 meters of ice — and down through the domed ceiling of the cavern.

    The sight of animals inside the cavern generated instant excitement among Horgan, Stevens and the other people at camp. But those first images were blurry, leaving people unsure of what the orange, bumblebee-sized critters actually were.

    Workers next lowered an instrument down the borehole to measure the water temperature and salinity inside the cavern. They found the top 50 meters of water colder and fresher than what lay below — confirming that seawater was flowing in along the bottom and a more buoyant mixture of saltwater and freshwater was flowing out along the top. The cavern, says Stevens, “is operating quite like an estuary.”

    But those measurements also presented a mystery: The water in the top of the cavern was only about 1 percent less salty than the seawater in its bottom, suggesting that the amount of freshwater flowing in through the river was “quite small,” says Stevens. It’s akin to a shallow creek that a young kid might splash around in. He and Horgan doubted that the turbulence caused by this small flow, even over 35 years, could melt the entire cavern — roughly a cubic kilometer of ice.

    A likely answer came from a set of samples collected from the floor of the cavern. Gavin Dunbar, a sedimentologist at the Victoria University of Wellington, lowered a hollow plastic cylinder down the hole in hopes of retrieving a core. As he and graduate student Linda Balfoort hoisted the cylinder back up, they found it streaked and filled with chocolaty mud — a strange sight in this world of pure white, where not a speck of rock or dirt can be seen for hundreds of kilometers.

    As Dunbar and Balfoort X-rayed and analyzed the cores months later, back in New Zealand, their peculiarities became obvious: They were unlike anything that Dunbar had ever encountered in this part of the world.

    Every core that Dunbar had ever seen from the seafloors near this part of Antarctica consisted of a chaotic jumble of sand, silt and gravel — a material called diamict, formed as the ice sheet advances and retreats over the seafloor, plowing and mixing it like a rototiller. But in these cores, Dunbar and Balfoort saw distinct layers. Bands of coarse, gravely material were interspersed with layers of fine, silty mud.

    That alternating pattern resembled samples from steep seafloor canyons off the coast of New Zealand, where earthquakes sometimes trigger underwater landslides that sweep for many kilometers downhill. Each flood deposits a single layer of chunky material.

    Dunbar believes that something similar happened under the Kamb Ice Stream, possibly in the last few decades. A series of fast-moving torrents gushed through the river channel carrying big gravelly chunks from somewhere upstream that later settled on the cavern floor. “Each of these [coarse layers] represents minutes to hours of sediment deposition” that occurred during a single flood, he says. And the fine, silty layers would have been laid down over years or decades in between the floods, when the river flowed languidly along.

    These subglacial floods could explain how this small river carved such a large cavern, Stevens says. Those floods could have been 100 to 1,000 times as large as the flow rates that were measured during the 2021–22 field season.

    No one knows when those events happened, but scientists using satellites to study subglacial lakes have spotted at least one candidate. In 2013, a lake 20 kilometers upstream from the cavern, called KT3, disgorged an estimated 60 million cubic meters of water — enough to fill 24,000 Olympic-sized swimming pools.

    Scientists would love to know whether that flood actually passed through this cavern. “Connecting this upstream to the lake system would be extremely cool,” says Matthew Siegfried, a glaciologist at the Colorado School of Mines in Golden, who coauthored one of the reports documenting the 2013 flood.

    Studying the outflow of this river could also answer other questions about the subglacial landscape upstream. “The vast majority of our knowledge of subglacial lakes comes from surface observations from space,” Siegfried says. But those satellite records, of ice bobbing up and down, permit only indirect estimates of how much water is flowing through. It’s possible, for example, that a lot of water passes through the lakes even when the ice above isn’t moving.

    Scientists could also learn about the subglacial landscape by studying the sediment washed downstream. When Dunbar and his colleagues examined the coarse material from their cores, they found it full of microscopic fossils: glassy shells of marine diatoms, needly spicules of sea sponges, and notched and spiky pollen grains of southern beech trees. These fossils represent the remains of a warmer world, 15 million to 20 million years ago, when a few stands of stunted, shrubby trees still clung to parts of Antarctica. Back then, the West Antarctic basin held a sea rather than an ice sheet, and this detritus settled on its muddy bottom. These old marine deposits underlie much of the West Antarctic Ice Sheet, and the few boreholes drilled so far suggest that the mix of fossils differs from one place to another. Those mixes could provide clues to how the flow of rivers changes over time.

    To uncover the nuance of what’s happening in the cavern “is mind-blowingly cool,” says Christina Hulbe, a glaciologist at the University of Otago in Dunedin, New Zealand, who has studied this region of Antarctica for nearly 30 years. “That’s the outlet for a massively big river system, if you think about it.”

    By studying the water, scientists could estimate the amount of organic carbon and other nutrients flowing out of the river into the ice-covered ocean. The landscape beneath the ice sheet appears to be rich in nutrients that might sustain oases of life in an otherwise famished biological desert.

    Scientists unveil an oasis of life

    Even as the cavern penetrates farther into the Kamb Ice Stream, it does not necessarily threaten the glacier’s stability. This part of the West Antarctic coastline is not considered vulnerable, because its shallow bed shields it from the deep, warm ocean currents that are causing rapid ice loss in other regions. But subglacial rivers pour out at many other points along the coastline, including some — like Thwaites Glacier, roughly 1,100 kilometers northeast of Kamb — where the ice is retreating rapidly (SN: 3/11/23, p. 8).

    Thwaites and nearby glaciers have collectively shed over 2,000 cubic kilometers of ice since 1992. They could eventually raise global sea levels by 2.3 meters if they collapse. Remote sensing studies have documented over a dozen low, squat shield volcanoes beneath this part of the ice sheet. The elevated geothermal heat flow, even from inactive volcanoes, is thought to cause high levels of melting under the ice sheet. That melting produces large amounts of subglacial water, which could render these glaciers even more vulnerable to human-caused climate change.

    Horgan believes that what scientists learn at Kamb could improve our understanding of how subglacial rivers impact those other, rapidly changing coastlines of Antarctica.

    But the most evocative discovery made at Kamb — in purely human terms — may be the blurry, orangish animals seen swarming near the bottom of the cavern. Stevens captured some clearer images a few days later and tentatively identified them as shrimp­like marine crustaceans called amphipods. To see so many of them here, Stevens says, “we really hadn’t expected that.”

    [embedded content]
    Video from a camera lowered into a hidden cavern beneath the Kamb Ice Stream showed animals, perhaps amphipods, swimming about. They may subsist in part on nutrients transported by a subglacial river.

    Microbes like those previously found under the ice sheet in Subglacial Lake Whillans are known to eke out a living even in harsh conditions. But animals are a different matter. The deepest seafloors on Earth sit only 10 or 11 kilometers from sunlight, and animal life in those places is generally scarce. But the animals in the cavern are thriving 500 kilometers from the nearest daylight, cut off from the photosynthesis that fuels most life on Earth.

    The amphipods and their supporting ecosystem must be subsisting on some other food source. But what? Observations in the Kamb ice cavern, combined with those at two other remote boreholes drilled in recent years, offer some tantalizing hints.

    In 2015, researchers pierced the ice at another site 250 kilometers from the cavern, where the Whillans Ice Stream lifts off its bed and floats. In that location, a thin sliver of seawater, just 10 meters deep, sits beneath 760 meters of ice. A remotely operated vehicle, or ROV, sent down the hole captured images of fish and amphipods.

    John Priscu, a microbial ecologist at Montana State University in Bozeman who was involved in the drilling at the site, believes that the glacier itself is sustaining this ecosystem. The bottom 10 meters of ice is packed with mud that had frozen onto the belly of the glacier many kilometers upstream. The mud had been dragged to its present location as the glacier oozed forward, 400 meters per year. As the ROV navigated about, bits of that muddy debris constantly rained down, released as the ice’s underside slowly melted. That debris is rich in organic matter — the rotting remains of diatoms and other phytoplankton that sank to the bottom millions of years ago when the world was warmer.

    “Those amphipods are swarming to the particulate matter,” Priscu says. “They’re sensing the organic matter falling out of that basal ice.” Or perhaps they may be eating the bacteria that live on those organics.

    Because the Kamb Ice Stream is barely moving, the supply of dirty ice moving toward the sea is small. But the river flowing into the ice cavern may deliver the same subglacial nutrients that are found in dirty ice. After all, the water’s journey through a series of subglacial lakes down to the river’s mouth may take years or decades. Throughout that time, the river absorbs nutrients from the organic-rich subglacial sediments.

    Indeed, when scientists drilled into Subglacial Lake Whillans in 2013, they found its water honey-colored — chock-full of life-sustaining iron, ammonium and organics. “What these lakes are pumping out may be a concentrated source of nutrients” for ecosystems along the dark coastline, says Trista Vick-Majors, a microbial ecologist at Michigan Technological University in Houghton who was involved in the drilling at Lake Whillans. She has estimated that the subglacial rivers flowing out from under Kamb and its neighboring glaciers may deliver 56,000 tons of organic carbon and other nutrients to this section of the coastline every year.

    More recently, in December 2019, a team from New Zealand led by Horgan and Hulbe drilled through the ice just 50 kilometers from the Kamb cavern, in a place where the Kamb Ice Stream floats on the ocean. There’s no dirty ice there and no nearby river outlets. The area resembled a famished seafloor desert; it was populated by single-celled microbes with little to eat, and few signs of animals were seen — only a few burrowing traces on the muddy bottom. Priscu sees this location as an exception that proves the point: Subglacial nutrients are the crucial energy source in this dark world under the floating ice, whether they are dragged forward on the undersides of glaciers or spilled out through subglacial rivers.

    The mud and water samples collected from the Kamb ice cavern may provide a new opportunity to test that theory. Craig Cary, a microbial ecologist at the University of Waikato in New Zealand, is analyzing DNA from those samples. He hopes to determine whether the microbes in the cavern belong to taxonomic groups that are known to subsist on ammonium, methane, hydrogen or other sources of chemical energy that originate from the subglacial sediments. That might reveal whether such sources support enough microbial growth to feed the animals observed there.

    The team also needs to measure the flow rate of the subglacial river that spills into the cavern, since that determines the nutrient supply. Stevens continues to monitor this thanks to a set of instruments left behind in the cavern.

    At the end of the trip, scientists including Craig Stewart (right) and Andrew Mullen (center) lowered instruments (a current meter is shown) into the cavern so they could continue monitoring it from afar.C. STEVENS/NIWA

    As people were packing up camp on January 11, 2022, workers pumped more hot water into the borehole, widening it to more than 35 centimeters — and creating a dangerous pitfall. Stevens and his colleagues donned climbing harnesses, clipped into safety ropes and approached the hole one last time. They lowered a series of cylinders the size of caulking guns down the hole. These devices continue to measure the temperature, salinity and water currents inside the cavern, sending the data 500 meters up a cable to a transmitter that beams it home via satellite once a day. That data will reveal how the river’s flow changes over time. With luck, the instruments might even detect a subglacial flood gushing through.

    “That would just be outstanding,” Horgan says. For many years, he had to content himself with seeing these rivers and lakes dimly, through the outlines of water on radar and satellite images. This is “one of the first times we’ve got to stand at a river mouth and observe it.” More

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    ChatGPT is still no match for humans when it comes to accounting

    Last month, OpenAI launched its newest AI chatbot product, GPT-4. According to the folks at OpenAI, the bot, which uses machine learning to generate natural language text, passed the bar exam with a score in the 90th percentile, passed 13 of 15 AP exams and got a nearly perfect score on the GRE Verbal test.
    Inquiring minds at BYU and 186 other universities wanted to know how OpenAI’s tech would fare on accounting exams. So, they put the original version, ChatGPT, to the test. The researchers say that while it still has work to do in the realm of accounting, it’s a game changer that will change the way everyone teaches and learns — for the better.
    “When this technology first came out, everyone was worried that students could now use it to cheat,” said lead study author David Wood, a BYU professor of accounting. “But opportunities to cheat have always existed. So for us, we’re trying to focus on what we can do with this technology now that we couldn’t do before to improve the teaching process for faculty and the learning process for students. Testing it out was eye-opening.”
    Since its debut in November 2022, ChatGPT has become the fastest growing technology platform ever, reaching 100 million users in under two months. In response to intense debate about how models like ChatGPT should factor into education, Wood decided to recruit as many professors as possible to see how the AI fared against actual university accounting students.
    His co-author recruiting pitch on social media exploded: 327 co-authors from 186 educational institutions in 14 countries participated in the research, contributing 25,181 classroom accounting exam questions. They also recruited undergrad BYU students (including Wood’s daughter, Jessica) to feed another 2,268 textbook test bank questions to ChatGPT. The questions covered accounting information systems (AIS), auditing, financial accounting, managerial accounting and tax, and varied in difficulty and type (true/false, multiple choice, short answer, etc.).
    Although ChatGPT’s performance was impressive, the students performed better. Students scored an overall average of 76.7%, compared to ChatGPT’s score of 47.4%. On a 11.3% of questions, ChatGPT scored higher than the student average, doing particularly well on AIS and auditing. But the AI bot did worse on tax, financial, and managerial assessments, possibly because ChatGPT struggled with the mathematical processes required for the latter type.
    When it came to question type, ChatGPT did better on true/false questions (68.7% correct) and multiple-choice questions (59.5%), but struggled with short-answer questions (between 28.7% and 39.1%). In general, higher-order questions were harder for ChatGPT to answer. In fact, sometimes ChatGPT would provide authoritative written descriptions for incorrect answers, or answer the same question different ways.
    “It’s not perfect; you’re not going to be using it for everything,” said Jessica Wood, currently a freshman at BYU. “Trying to learn solely by using ChatGPT is a fool’s errand.”
    The researchers also uncovered some other fascinating trends through the study, including: ChatGPT doesn’t always recognize when it is doing math and makes nonsensical errors such as adding two numbers in a subtraction problem, or dividing numbers incorrectly. ChatGPT often provides explanations for its answers, even if they are incorrect. Other times, ChatGPT’s descriptions are accurate, but it will then proceed to select the wrong multiple-choice answer. ChatGPT sometimes makes up facts. For example, when providing a reference, it generates a real-looking reference that is completely fabricated. The work and sometimes the authors do not even exist.That said, authors fully expect GPT-4 to improve exponentially on the accounting questions posed in their study, and the issues mentioned above. What they find most promising is how the chatbot can help improve teaching and learning, including the ability to design and test assignments, or perhaps be used for drafting portions of a project.
    “It’s an opportunity to reflect on whether we are teaching value-added information or not,” said study coauthor and fellow BYU accounting professor Melissa Larson. “This is a disruption, and we need to assess where we go from here. Of course, I’m still going to have TAs, but this is going to force us to use them in different ways.” More

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    Reinforcement learning: From board games to protein design

    Scientists have successfully applied reinforcement learning to a challenge in molecular biology.
    The team of researchers developed powerful new protein design software adapted from a strategy proven adept at board games like Chess and Go. In one experiment, proteins made with the new approach were found to be more effective at generating useful antibodies in mice.
    The findings, reported April 21 in Science, suggest that this breakthrough may soon lead to more potent vaccines. More broadly, the approach could lead to a new era in protein design.
    “Our results show that reinforcement learning can do more than master board games. When trained to solve long-standing puzzles in protein science, the software excelled at creating useful molecules,” said senior author David Baker, professor of biochemistry at the UW School of Medicine in Seattle and a recipient of the 2021 Breakthrough Prize in Life Sciences.
    “If this method is applied to the right research problems,” he said, “it could accelerate progress in a variety of scientific fields.”
    The research is a milestone in tapping artificial intelligence to conduct protein science research. The potential applications are vast, from developing more effective cancer treatments to creating new biodegradable textiles.

    Reinforcement learning is a type of machine learning in which a computer program learns to make decisions by trying different actions and receiving feedback. Such an algorithm can learn to play chess, for example, by testing millions of different moves that lead to victory or defeat on the board. The program is designed to learn from these experiences and become better at making decisions over time.
    To make a reinforcement learning program for protein design, the scientists gave the computer millions of simple starting molecules. The software then made ten thousand attempts at randomly improving each toward a predefined goal. The computer lengthened the proteins or bent them in specific ways until it learned how to contort them into desired shapes.
    Isaac D. Lutz, Shunzhi Wang, and Christoffer Norn, all members of the Baker Lab, led the research. Their team’s Science manuscript is titled “Top-down design of protein architectures with reinforcement learning.”
    “Our approach is unique because we use reinforcement learning to solve the problem of creating protein shapes that fit together like pieces of a puzzle,” explained co-lead author Lutz, a doctoral student at the UW Medicine Institute for Protein Design. “This simply was not possible using prior approaches and has the potential to transform the types of molecules we can build.”
    As part of this study, the scientists manufactured hundreds of AI-designed proteins in the lab. Using electron microscopes and other instruments, they confirmed that many of the protein shapes created by the computer were indeed realized in the lab.

    “This approach proved not only accurate but also highly customizable. For example, we asked the software to make spherical structures with no holes, small holes, or large holes. Its potential to make all kinds of architectures has yet to be fully explored,” said co-lead author Shunzhi Wang, a postdoctoral scholar at the UW Medicine Institute for Protein Design.
    The team concentrated on designing new nano-scale structures composed of many protein molecules. This required designing both the protein components themselves and the chemical interfaces that allow the nano-structures to self-assemble.
    Electron microscopy confirmed that numerous AI-designed nano-structures were able to form in the lab. As a measure of how accurate the design software had become, the scientists observed many unique nano-structures in which every atom was found to be in the intended place. In other words, the deviation between the intended and realized nano-structure was on average less than the width of a single atom. This is called atomically accurate design.
    The authors foresee a future in which this approach could enable them and others to create therapeutic proteins, vaccines, and other molecules that could not have been made using prior methods.
    Researchers from the UW Medicine Institute for Stem Cell and Regenerative Medicine used primary cell models of blood vessel cells to show that the designed protein scaffolds outperformed previous versions of the technology. For example, because the receptors that help cells receive and interpret signals were clustered more densely on the more compact scaffolds, they were more effective at promoting blood vessel stability.
    Hannele Ruohola-Baker, a UW School of Medicine professor of biochemistry and one of the study’s authors, spoke to the implications of the investigation for regenerative medicine: “The more accurate the technology becomes, the more it opens up potential applications, including vascular treatments for diabetes, brain injuries, strokes, and other cases where blood vessels are at risk. We can also imagine more precise delivery of factors that we use to differentiate stem cells into various cell types, giving us new ways to regulate the processes of cell development and aging.”
    This work was funded by the National Institutes of Health (P30 GM124169, S10OD018483, 1U19AG065156-01, T90 DE021984, 1P01AI167966); Open Philanthropy Project and The Audacious Project at the Institute for Protein Design; Novo Nordisk Foundation (NNF170C0030446); Microsoft; and Amgen. Research was in part conducted at the Advanced Light Source, a national user facility operated by Lawrence Berkeley National Laboratory on behalf of the Department of Energy
    News release written by Ian Haydon, UW Medicine Institute for Protein Design. More

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    AI system can generate novel proteins that meet structural design targets

    MIT researchers are using artificial intelligence to design new proteins that go beyond those found in nature.
    They developed machine-learning algorithms that can generate proteins with specific structural features, which could be used to make materials that have certain mechanical properties, like stiffness or elasticity. Such biologically inspired materials could potentially replace materials made from petroleum or ceramics, but with a much smaller carbon footprint.
    The researchers from MIT, the MIT-IBM Watson AI Lab, and Tufts University employed a generative model, which is the same type of machine-learning model architecture used in AI systems like DALL-E 2. But instead of using it to generate realistic images from natural language prompts, like DALL-E 2 does, they adapted the model architecture so it could predict amino acid sequences of proteins that achieve specific structural objectives.
    In a paper to be published in Chem, the researchers demonstrate how these models can generate realistic, yet novel, proteins. The models, which learn biochemical relationships that control how proteins form, can produce new proteins that could enable unique applications, says senior author Markus Buehler, the Jerry McAfee Professor in Engineering and professor of civil and environmental engineering and of mechanical engineering.
    For instance, this tool could be used to develop protein-inspired food coatings, which could keep produce fresh longer while being safe for humans to eat. And the models can generate millions of proteins in a few days, quickly giving scientists a portfolio of new ideas to explore, he adds.
    “When you think about designing proteins nature has not discovered yet, it is such a huge design space that you can’t just sort it out with a pencil and paper. You have to figure out the language of life, the way amino acids are encoded by DNA and then come together to form protein structures. Before we had deep learning, we really couldn’t do this,” says Buehler, who is also a member of the MIT-IBM Watson AI Lab.

    Joining Buehler on the paper are lead author Bo Ni, a postdoc in Buehler’s Laboratory for Atomistic and Molecular Mechanics; and David Kaplan, the Stern Family Professor of Engineering and professor of bioengineering at Tufts.
    Adapting new tools for the task
    Proteins are formed by chains of amino acids, folded together in 3D patterns. The sequence of amino acids determines the mechanical properties of the protein. While scientists have identified thousands of proteins created through evolution, they estimate that an enormous number of amino acid sequences remain undiscovered.
    To streamline protein discovery, researchers have recently developed deep learning models that can predict the 3D structure of a protein for a set of amino acid sequences. But the inverse problem — predicting a sequence of amino acid structures that meet design targets — has proven even more challenging.
    A new advent in machine learning enabled Buehler and his colleagues to tackle this thorny challenge: attention-based diffusion models.

    Attention-based models can learn very long-range relationships, which is key to developing proteins because one mutation in a long amino acid sequence can make or break the entire design, Buehler says. A diffusion model learns to generate new data through a process that involves adding noise to training data, then learning to recover the data by removing the noise. They are often more effective than other models at generating high-quality, realistic data that can be conditioned to meet a set of target objectives to meet a design demand.
    The researchers used this architecture to build two machine-learning models that can predict a variety of new amino acid sequences which form proteins that meet structural design targets.
    “In the biomedical industry, you might not want a protein that is completely unknown because then you don’t know its properties. But in some applications, you might want a brand-new protein that is similar to one found in nature, but does something different. We can generate a spectrum with these models, which we control by tuning certain knobs,” Buehler says.
    Common folding patterns of amino acids, known as secondary structures, produce different mechanical properties. For instance, proteins with alpha helix structures yield stretchy materials while those with beta sheet structures yield rigid materials. Combining alpha helices and beta sheets can create materials that are stretchy and strong, like silks.
    The researchers developed two models, one that operates on overall structural properties of the protein and one that operates at the amino acid level. Both models work by combining these amino acid structures to generate proteins. For the model that operates on the overall structural properties, a user inputs a desired percentage of different structures (40 percent alpha-helix and 60 percent beta sheet, for instance). Then the model generates sequences that meet those targets. For the second model, the scientist also specifies the order of amino acid structures, which gives much finer-grained control.
    The models are connected to an algorithm that predicts protein folding, which the researchers use to determine the protein’s 3D structure. Then they calculate its resulting properties and check those against the design specifications.
    Realistic yet novel designs
    They tested their models by comparing the new proteins to known proteins that have similar structural properties. Many had some overlap with existing amino acid sequences, about 50 to 60 percent in most cases, but also some entirely new sequences. The level of similarity suggests that many of the generated proteins are synthesizable, Buehler adds.
    To ensure the predicted proteins are reasonable, the researchers tried to trick the models by inputting physically impossible design targets. They were impressed to see that, instead of producing improbable proteins, the models generated the closest synthesizable solution.
    “The learning algorithm can pick up the hidden relationships in nature. This gives us confidence to say that whatever comes out of our model is very likely to be realistic,” Ni says.
    Next, the researchers plan to experimentally validate some of the new protein designs by making them in a lab. They also want to continue augmenting and refining the models so they can develop amino acid sequences that meet more criteria, such as biological functions.
    “For the applications we are interested in, like sustainability, medicine, food, health, and materials design, we are going to need to go beyond what nature has done. Here is a new design tool that we can use to create potential solutions that might help us solve some of the really pressing societal issues we are facing,” Buehler says.
    This research was supported, in part, by the MIT-IBM Watson AI Lab, the U.S. Department of Agriculture, the U.S. Department of Energy, the Army Research Office, the National Institutes of Health, and the Office of Naval Research. More

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    Quantum entanglement could make accelerometers and dark matter sensors more accurate

    The “spooky action at a distance” that once unnerved Einstein may be on its way to being as pedestrian as the gyroscopes that currently measure acceleration in smartphones.
    Quantum entanglement significantly improves the precision of sensors that can be used to navigate without GPS, according to a new study in Nature Photonics.
    “By exploiting entanglement, we improve both measurement sensitivity and how quickly we can make the measurement,” said Zheshen Zhang, associate professor of electrical and computer engineering at the University of Michigan and co-corresponding author of the study. The experiments were done at the University of Arizona, where Zhang was working at the time.
    Optomechanical sensors measure forces that disturb a mechanical sensing device that moves in response. That motion is then measured with light waves. In this experiment, the sensors were membranes, which act like drum heads that vibrate after experiencing a push. Optomechanical sensors can function as accelerometers, which can be used for inertial navigation on a planet that doesn’t have GPS satellites or within a building as a person navigates different floors.
    Quantum entanglement could make optomechanical sensors more accurate than inertial sensors currently in use. It could also enable optomechanical sensors to look for very subtle forces, such as identifying the presence of dark matter. Dark matter is invisible matter believed to account for five times more of the mass in the universe than what we can sense with light. It would tug on the sensor with gravitational force.
    Here’s how entanglement improves optomechanical sensors:
    Optomechanical sensors rely on two synchronized laser beams. One of them is reflected from a sensor, and any movement in the sensor changes the distance that the light travels on its way to the detector. That difference in distance traveled shows up when the second wave overlaps with the first. If the sensor is still, the two waves are perfectly aligned. But if the sensor is moving, they create an interference pattern as the peaks and troughs of their waves cancel each other out in places. That pattern reveals the size and speed of vibrations in the sensor.

    Usually in interferometry systems, the further the light travels, the more accurate the system becomes. The most sensitive interferometry system on the planet, the Laser Interferometer Gravitational-Wave Observatory, sends light on 8-kilometer journeys. But that’s not going to fit in a smartphone.
    To enable high accuracy in miniaturized optomechanical sensors, Zhang’s team explored quantum entanglement. Rather than splitting the light once so that it bounced off a sensor and a mirror, they split each beam a second time so that the light bounced off two sensors and two mirrors. Dalziel Wilson, an assistant professor of optical sciences at the University of Arizona, along with his doctoral students Aman Agrawal and Christian Pluchar, built the membrane devices. These membranes, just 100 nanometers — or 0.0001 millimeters — thick, move in response to very small forces.
    Doubling the sensors improves the accuracy, as the membranes should be vibrating in sync with each other, but the entanglement adds an extra level of coordination. Zhang’s group created the entanglement by “squeezing” the laser light. In quantum mechanical objects, such as the photons that make up light, there is a fundamental limit on how well the position and momentum of a particle can be known. Because photons are also waves, this translates to the phase of the wave (where it is in its oscillation) and its amplitude (how much energy it carries).
    “Squeezing redistributes the uncertainty, so that the squeezed component is known more precisely, and the anti-squeezed component carries more of the uncertainty. We squeezed the phase because that is what we needed to know for our measurement,” said Yi Xia, a recent Ph.D. graduate from Zhang’s lab at the University of Arizona and co-corresponding author of the paper.
    In squeezed light, the photons are more closely related to one another. Zhang contrasted what happens when the photons go through a beam splitter with cars coming to a fork in the freeway.

    “You have three cars going one way and three cars going the other way. But in quantum superposition, each car goes both ways. Now the cars on the left are entangled with the cars on the right,” he said.
    Because the fluctuations in the two entangled beams are linked, the uncertainties in their phase measurements are correlated. As a result, with some mathematical wizardry, the team was able to get measurements that are 40% more precise than with two unentangled beams, and they can do it 60% faster. What’s more, the precision and speed is expected to rise in proportion to the number of sensors.
    “It is envisioned that an array of entanglement-enhanced sensors will offer orders-of-magnitude performance gain over existing sensing technology to enable the detection of particles beyond the present physical model, opening the door to a new world that is yet to be observed,” said Zhang.
    The team’s next steps are to miniaturize the system. Already, they can put a squeezed-light source on a chip that is just half a centimeter to a side. They expect to have a prototype chip with the squeezed-light source, beam splitters, waveguides and inertial sensors within a year or two.
    The study was funded by the Office of Naval Research, National Science Foundation, Department of Energy and Defense Advanced Research Projects Agency. More

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    Versatile, high-speed, and efficient crystal actuation with photothermally resonated natural vibrations

    Mechanically responsive molecular crystals are extremely useful in soft robotics, which requires a versatile actuation technology. Crystals driven by the photothermal effect are particularly promising for achieving high-speed actuation. However, the response (bending) observed in these crystals is usually small. Now, scientists from Japan address this issue by inducing large resonated natural vibrations in anisole crystals with UV light illumination at the natural vibration frequency of the crystal.
    Every material possesses a unique natural vibration frequency such that when an external periodic force is applied to this material close to this frequency, the vibrations are greatly amplified. In the parlance of physics, this phenomenon is known as “resonance.” Resonance is ubiquitous in our daily life, and, depending on the context, could be deemed desirable or undesirable. For instance, musical instruments like the guitar relies on resonance for sound amplification. On the other hand, buildings and bridges are more likely to collapse under an earthquake if the ground vibration frequency matches their natural frequency.
    Interestingly, natural vibration has not received much attention in material actuation, which relies on the action of mechanically responsive crystals. Versatile actuation technologies are highly desirable in the field of soft robotics. Although crystal actuation based on processes like photoisomerisation and phase transitions have been widely studied, these processes lack versatility since they require specific crystals to work. One way to improve versatility is by employing photothermal crystals, which show bending due to light-induced heating. While promising for achieving high-speed actuation, the bending angle is usually small ( More

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    Two qudits fully entangled

    In the world of computing, we typically think of information as being stored as ones and zeros — also known as binary encoding. However, in our daily life we use ten digits to represent all possible numbers. In binary the number 9 is written as 1001 for example, requiring three additional digits to represent the same thing.
    The quantum computers of today grew out of this binary paradigm, but in fact the physical systems that encode their quantum bits (qubit) often have the potential to also encode quantum digits (qudits), as recently demonstrated by a team led by Martin Ringbauer at the Department of Experimental Physics at the University of Innsbruck. According to experimental physicist Pavel Hrmo at ETH Zurich: “The challenge for qudit-based quantum computers has been to efficiently create entanglement between the high-dimensional information carriers.”
    In a study published in the journal Nature Communications the team at the University of Innsbruck now reports, how two qudits can be fully entangled with each other with unprecedented performance, paving the way for more efficient and powerful quantum computers.
    Thinking like a quantum computer
    The example of the number 9 shows that, while humans are able calculate 9 x 9 = 81 in one single step, a classical computer (or calculator) has to take 1001 x 1001 and perform many steps of binary multiplication behind the scenes before it is able to display 81 on the screen. Classically, we can afford to do this, but in the quantum world where computations are inherently sensitive to noise and external disturbances, we need to reduce the number of operations required to make the most of available quantum computers.
    Crucial to any calculation on a quantum computer is quantum entanglement. Entanglement is one of the unique quantum features that underpin the potential for quantum to greatly outperform classical computers in certain tasks. Yet, exploiting this potential requires the generation of robust and accurate higher-dimensional entanglement.
    The natural language of quantum systems
    The researchers at the University of Innsbruck were now able to fully entangle two qudits, each encoded in up to 5 states of individual Calcium ions. This gives both theoretical and experimental physicists a new tool to move beyond binary information processing, which could lead to faster and more robust quantum computers.
    Martin Ringbauer explains: “Quantum systems have many available states waiting to be used for quantum computing, rather than limiting them to work with qubits.” Many of today’s most challenging problems, in fields as diverse as chemistry, physics or optimisation, can benefit from this more natural language of quantum computing.
    The research was financially supported by the Austrian Science Fund FWF, the Austrian Research Promotion Agency FFG, the European Research Council ERC, the European Union and the Federation of Austrian Industries Tyrol, among others. More

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    Quantum computer applied to chemistry

    There are high expectations that quantum computers may deliver revolutionary new possibilities for simulating chemical processes. This could have a major impact on everything from the development of new pharmaceuticals to new materials. Researchers at Chalmers University have now, for the first time in Sweden, used a quantum computer to undertake calculations within a real-life case in chemistry.
    “Quantum computers could in theory be used to handle cases where electrons and atomic nuclei move in more complicated ways. If we can learn to utilise their full potential, we should be able to advance the boundaries of what is possible to calculate and understand,” says Martin Rahm, Associate Professor in Theoretical Chemistry at the Department of Chemistry and Chemical Engineering, who has led the study.
    Within the field of quantum chemistry, the laws of quantum mechanics are used to understand which chemical reactions are possible, which structures and materials can be developed, and what characteristics they have. Such studies are normally undertaken with the help of super computers, built with conventional logical circuits. There is however a limit for which calculations conventional computers can handle. Because the laws of quantum mechanics describe the behaviour of nature on a subatomic level, many researchers believe that a quantum computer should be better equipped to perform molecular calculations than a conventional computer.
    “Most things in this world are inherently chemical. For example, our energy carriers, within biology as well as in old or new cars, are made up of electrons and atomic nuclei arranged in different ways in molecules and materials. Some of the problems we solve in the field of quantum chemistry are to calculate which of these arrangements are more likely or advantageous, along with their characteristics,” says Martin Rahm.
    A new method minimises errors in the quantum chemical calculations
    There is still a way to go before quantum computers can achieve what the researchers are aiming for. This field of research is still young and the small model calculations that are run are complicated by noise from the quantum computer’s surroundings. However, Martin Rahm and his colleagues have now found a method that they see as an important step forward. The method is called Reference-State Error Mitigation (REM) and works by correcting for the errors that occur due to noise by utilising the calculations from both a quantum computer and a conventional computer.

    “The study is a proof-of-concept that our method can improve the quality of quantum-chemical calculations. It is a useful tool that we will use to improve our calculations on quantum computers moving forward,” says Martin Rahm.
    The principle behind the method is to first consider a reference state by describing and solving the same problem on both a conventional and a quantum computer. This reference state represents a simpler description of a molecule than the original problem intended to be solved by the quantum computer. A conventional computer can solve this simpler version of the problem quickly. By comparing the results from both computers, an exact estimate can be made for the amount of error caused by noise. The difference between the two computers’ solutions for the reference problem can then be used to correct the solution for the original, more complex, problem when it is run on the quantum processor. By combining this new method with data from Chalmers’ quantum computer Särimner* the researchers have succeeded in calculating the intrinsic energy of small example molecules such as hydrogen and lithium hydride. Equivalent calculations can be carried out more quickly on a conventional computer, but the new method represents an important development and is the first demonstration of a quantum chemical calculation on a quantum computer in Sweden.
    “We see good possibilities for further development of the method to allow calculations of larger and more complex molecules, when the next generation of quantum computers are ready,” says Martin Rahm.
    Quantum computer built at Chalmers
    The research has been conducted in close collaboration with colleagues at the Department of Microtechnology and Nanoscience. They have built the quantum computers that are used in the study, and helped perform the sensitive measurements that are needed for the chemical calculations.

    “It is only by using real quantum algorithms that we can understand how our hardware really works and how we can improve it. Chemical calculations are one of the first areas where we believe that quantum computers will be useful, so our collaboration with Martin Rahm’s group is especially valuable,” says Jonas Bylander, Associate Professor in Quantum Technology at the Department of Microtechnology and Nanoscience.
    More about the research
    Read the article Reference-State Error Mitigation: A Strategy for High Accuracy Quantum Computation of Chemistry in the Journal of Chemical Theory and Computation.
    The article is written by Phalgun Lolur, Mårten Skogh, Werner Dobrautz, Christopher Warren, Janka Biznárová, Amr Osman, Giovanna Tancredi, Göran Wendin, Jonas Bylander, and Martin Rahm. The researchers are active at Chalmers University of Technology.
    The research has been conducted in cooperation with the Wallenberg Centre for Quantum Technology (WACQT) and the EU-project OpensuperQ. OpensuperQ connects universities and companies in 10 European countries with the aim of building a quantum computer, and its extension will contribute further funding to researchers at Chalmers for their work with quantum chemical calculations.
    *Särimner is the name of a quantum processor with five qubits, or quantum bits, built by Chalmers within the framework of the Wallenberg Center for Quantum Technology (WACQT). Its name is borrowed from Nordic mythology, in which the pig Särimner was butchered and eaten every day, only to be resurrected. Särimner has now been replaced by a larger computer with 25 qubits and the goal for WACQT is to build a quantum computer with 100 qubits that can solve problems far beyond the capacity of today’s best conventional super-computers. More