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While it could be many years before quantum computing becomes a common presence in daily life, the technology already has been recruited to help search for life in deep space.

Quantum software company Zapata Computing is partnering with the U.K.-based University of Hull on research to evaluate Zapatas Orquestra quantum workflow platform, to enhance a quantum application designed to detect signatures of life in deep space.

Dr David Benoit, Senior Lecturer in Molecular Physics and Astrochemistry at the University of Hull, said the evaluation is not a controlled demonstration of features, but rather a project involving real-world data. We are looking at how Orquestra performs in actual workflows that use quantum computing to provide typical real-life data, he told Fierce Electronics via email. In this project, we are really aiming for real useful data rather than a demo of capabilities.

The evaluation will run for eight weeks before the team publishes an analysis of the research. It is expected to be the first of several collaborations between Zapata and the University of Hull for quantum astrophysics applications, the parties said. The news comes as several giants in quantum computing, including Google, IBM, Amazon and Honeywell, among others, were set to attend a White House forum hosted by the Biden administration to discuss evolving uses for quantum computing.

In some cases, researchers have turned to quantum computing to tackle projects that classical computers would take too long to complete, and the University of Hull is in a similar situation, Benoit said.

He further explained, The tests envisioned are still something that a classical computer can do, however the computational time required to obtain the solution has a factorial scaling, meaning that larger size applications are likely to take days/months/years to complete (along with a very large amount of memory). The quantum counterpart is able to solve those problems in a sub-factorial manner (potentially quartic scaling), but this doesnt necessarily mean its faster for all systems, just that the computational effort is much reduced for large systems. In this application, we are aiming for a scalable way of performing accurate calculations, and this is exactly what we can obtain using quantum computers.

Just how big is the task at hand? A statement from Zapata noted that in 2016 MIT researchers suggested a list of more than 14,000 molecules that could indicate signs of life in atmospheres of far-away exoplanets. However, little is currently known about how these molecules vibrate and rotate in response to infrared radiation generated by nearby stars. The University of Hull is trying to build a database of detectable biological signatures using new computational models of molecular rotations and vibrations.

Though fault tolerance and error correction remain a challenge for quantum computing models, Benoit said researchers are not concerned with the performance of such so-called Noisy Intermediate-Scale Quantum (NISQ) devices.

Our method actually uses the statistical nature of the noise/errors to try and obtain an accurate answer, so we take the fact that the results will be noisy as a useful thing, he said. Obviously, the better the error correction or the less noisy the device, the better the outcome. However, using Orquestra enables us to potentially switch platforms without having to re-implement large parts of the code, which means that as better hardware comes along, we can readily compute with it.

Benoit added that Orquestra will help researchers generate valuable insights from NISQ devices, and that researchers can build applications that use these NISQ devices today with the capacity to leverage the more powerful quantum devices of the future. The result should be extremely accurate calculations of the key variable defining atom-atom interactions electronic correlation and thus could improve scientists ability to detect the building blocks of life in space. This is particularly important because even simple molecules, such as oxygen or nitrogen, have complex interactions that require very accurate calculations.

RELATED: Even noisy quantum systems are revolutionary: Classiq CEO

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Zapata, University of Hull researchers take quantum computing to deep space - FierceElectronics

The potential of quantum computing is one of the focuses ofa summit in Genevathataimstoimprove the dialogue between diplomatsandthescientific communityto safeguard our collective welfare.Tworesearchersexplaintherewards and risks ofquantum computing.

Dorian Burkhalter

Thescientists, diplomats, captains of industry and investors gathering inGenevafor the first-ever summit of theScience and Diplomacy Anticipator (GESDA)External linkwill, among other lofty goals, discuss howpolicymakersshouldprepare forquantumcomputing, provide governance for it,and ensure thatitis accessible to all.But what are quantum computers, and whatwill they be able to do?

Quantum computersperform calculations byexploitingtheproperties ofquantummechanics, which describes thebehaviourofatoms andparticles at a subatomic scale,for example,howelectrons interact with each other.As quantum computersoperate onthe same set of rules asmolecules do,they are,for instance,much better suitedto simulate them than classical computers are.

Today, quantum computers are small and unreliable. They are not yet able to solve problems classical computers cannot.

There is still some uncertainty, but I don't see any reason to not be able to develop such a quantum computer, although it's a huge engineering challenge, says Nicolas Gisin, professor emeritus at the University of Genevaand at the Schaffhausen Institute of Technology,and an expert in quantum technologies.

Quantum computerscouldhelp solvesome of the worlds most pressing problems. They couldaccelerate thediscovery ofmaterials for longer-lasting batteries,bettersolar panels, andnew medicaltreatments.They could also break current encryptionmethods, meaning that information secure today maybecomeat risk tomorrow.

For private companies, winning the race to develop reliable and powerful quantum computers means reaping large economic rewards. For countries, it means gaining a significant national security advantage.

Gisinsaysquantum computers capable of simulating new molecules could be 5-10 years away, while more powerful quantum computers that can break encryption could become a reality in 10-20 years.

The pace at whichthesetechnologies develop will depend on the level of investments made.Large technology firms such as IBM, Microsoft, and Googleare all developing quantum computers, while the US, China,and Europeareinvestingheavilyinquantum technologies.

Anticipating the arrival ofthesetechnologies isimportant,because you play through different scenarios, and some you may like,some you may not like,says HeikeRiel, IBM Fellow at IBMResearch in Zurich.Then you can also think of what type of regulations you may need,or what type of research you need to foster.

TheSwiss governmentis a supporter oftheGESDAfoundationwhichorganisedits first summit in Geneva fromOctober 7-9.The conferencebringstogetherscientists, diplomats, andother stakeholders to discussfuturescientific developmentsandtoanticipate their impacton society.

To work well, scientists needfavourableframeworks. There is definitely a back and forth between science and diplomacy, and science and politics, because diplomacy can also advance science, Riel says.

Politicians and diplomatsare responsible forcreatingopportunities for researchers to collaborate across borders. Initiatives and funding aimed at addressingspecifictechnical problems influence the directionofresearchefforts.

The fact that Switzerland is outside of the European research framework is an absurdity for everyone because this is just going to harm both Switzerland and Europe, Gisin says. It would be really important that Europe and Switzerland understand that we will both benefit if we talk together more and collaborate more.

Since July 2021, Switzerland haslimited accessto Horizon Europe, the European Unions flagship funding program for research and innovation due to a breakdown in negotiations on regulating bilateral relations.

Many of ourproblemstodaysuch as climate change or the Covid-19 pandemicare globalin nature.Getting governments across the world to agree to work togetheronsolutions is not easy, but researcherscan help.

The research communitylikes to worktogether globally, and this collaboration has helped historically to overcome certainbarriers, Riel says, emphasising the importance of communication in this regard.

Researchers working togetheron a global scaleduring the pandemichasled to vaccines being developed atarecord-breakingspeed.During the Cold Warat theEuropean Organization for Nuclear Research (CERN) in Geneva,Sovietscientistsremained involvedin projectswhich allowedforsomecommunicationto take place.

In science, we have a common ground and it's kind of universal; the scientists in the UnitedStates, Canada, Australia,Europeand China, they all work on the same problems, they all try to solve the same technical issues, Riel says.

Scientists also have an important role to play to inform and share facts with both policymakers and the public, even if politicians cannotrely solely on scientific evidence when making decisions. The challenges of communicatingfact-based evidencehavebeen laid bare during the pandemic.

I think it's very important that we also inform the society of what we are doingthat it's not a mystery thatscares people, Riel says.

Ultimately,to successfullyaddress global challenges scientists,diplomats and politicians willhave towork together.

It's really a cooperation between the global collaboration of the scientists and the global collaboration of the diplomats to solve the problems together, Riel says.

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How science and diplomacy inform each other - SWI swissinfo.ch - swissinfo.ch

Intel's Loihi 2 is the company's second generation neuromorphic computing chip, a technology that's ... [+] designed to function like a digital representation of a biological brain complete with neurons and synapses.

If you follow the latest trends in the tech industry, you probably know that theres been a fair amount of debate about what the next big thing is going to be. Odds-on favorite for many has been augmented reality (AR) glasses, while others point to fully autonomous cars, and a few are clinging to the potential of 5G. With the surprise debut of Amazons Astro a few weeks back, personal robotic devices and digital companions have also thrown their hat into the ring.

However, while there has been little agreement on exactly what the next thing is, there seems to be little disagreement that whatever it turns out to be, it will be somehow powered, enabled, or enhanced by artificial intelligence (AI). Indeed, the fact that AI and machine learning (ML) are our future seems to be a foregone conclusion.

Yet, if we do an honest assessment of where some of these technologies actually stand on a functionality basis versus initial expectations, its fair to argue that the results have been disappointing on many levels. In fact, if we extend that thought process out to what AI/ML were supposed to do for us overall, then we start to come to a similarly disappointing conclusion.

To be clear, weve seen some incredible advancements in many areas that AI has powered. Advanced analytics, neural network training, and other related fields (where large chunks of data are used to find patterns, learn rules, and then apply them) have been huge benefactors of existing AI approaches.

At the same time, if we look at an application like autonomous driving, it seems increasingly clear that just pushing more and more data into algorithms that crank out ever refined, yet still flawed, ML models isnt really working. Were still years away from true Level 5 autonomy, and, given the number of accidents and even deaths that efforts like Teslas AutoPilot have led to, its probably time to consider another approach.

Similarly, though we are still at the dawn of the personal robotics age, its easy to imagine how the conceptual similarities between autonomous cars and robots will lead to conceptually similar problems in this new field. The problem, ultimately, is that there is simply no way to feed every potential scenario into an AI training model and create a predetermined answer on how to react for any given situation. Randomness and unexpected surprises are simply too strong an influence.

Whats needed is a type of computing that can really think and learn on its own and then adapt its learning to those unexpected scenarios. As crazy and potentially controversial as that may sound, thats essentially what researchers in the field of neuromorphic computing are attempting to do. The basic idea is to replicate the structure and function of the most adaptable computing/thinking device we know ofthe human brainin digital form. Following the principles of basic biology, neuromorphic chips attempt to re-create a series of connected neurons using digital synapses that send electrical pulses between them, much as biological brains do.

Its an area of academic research thats been around for a few decades now, but only recently has it started to make real progress and gain more attention. In fact, buried in the wave of tech industry announcements that have been made over the last few weeks was news that Intel had released the second generation of its neuromorphic chip, named Loihi 2, along with a new open-source software framework for it that theyve dubbed Lava.

To put realistic expectations around all of this, Loihi 2 is not going to be made commercially availableits termed a research chipand the latest version offers 1 million neurons, a far cry from the approximately 100 billion found in a human brain. Still, its an extremely impressive, ambitious project that offers 10x the performance, 15x the density of its 2018-era predecessor (its built on the companys new Intel 4 chip manufacturing process technology), and improved energy efficiency. In addition, it also provides better (and easier) means of interconnecting its unique architecture with other more traditional chips.

Intel clearly learned a great deal from the first Loihi, and one of the biggest realizations was that software development for this radically new architecture is extremely hard. As a result, another essential part of the companys news was the debut of Lava, an open-source software framework and set of tools that can be used to write applications for Loihi. The company is also offering tools that can simulate its operation on traditional CPUs and GPUs so that developers can create code without having access to the chips.

Whats particularly fascinating about how neuromorphic chips operate is that, despite the fact they function in a dramatically different fashion from both traditional CPU computing and parallel GPU-like computing models, they can be used to achieve some of the same goals. In other words, neuromorphic chips like Loihi 2 can provide the desired outcomes that traditional AI is shooting for, but in a significantly faster, more energy efficient, and less data intensive way. Through a series of event-based spikes that occur asynchronously and trigger digital neurons to respond in various waysmuch as a human brain operates (vs. the synchronous, structured processing in CPUs and GPUs)a neuromorphic chip can essentially learn things on the fly. As a result, its ideally suited for devices that must react to new stimuli in real-time.

These capabilities are why these chips are so appealing to those designing and building robots and robotic-like systems, which autonomous driving cars essentially are. Bottom line is that it could take commercially available neuromorphic chips to power the kind of autonomous cars and personal robots of our science fiction-inspired dreams.

Of course, neuromorphic computing isnt the only new approach to advancing the world of technology. Theres also a great deal of work being done in the more widely discussed world of quantum computing. Like quantum computing, the inner workings of neuromorphic computing are extraordinarily complex and, for now, primarily seen as research projects for corporate R&D labs and academic research. Unlike quantum, however, neuromorphic computing doesnt require the extreme physical challenges (temperatures near absolute zero) and power requirements that quantum currently does. In fact, one of the many appealing aspects of neuromorphic architectures is that theyre designed to be extremely low power, making them suitable for a variety of mobile or other battery-powered applications (like autonomous cars and robots).

Despite recent advancements, its important to remember that commercial application of neuromorphic chips is still several years away. However, its hard not to get excited and intrigued by a technology that has the potential to make AI-powered devices truly intelligent, instead of simply very well-trained. The distinction may seem subtle, but ultimately, its that kind of new smarts that well likely need in order to make some of the next big things really happen in a way that we can all appreciate and imagine.

Disclosure: TECHnalysis Research is a tech industry market research and consulting firm and, like all companies in that field, works with many technology vendors as clients, some of whom may be listed in this article.

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Is Neuromorphic Computing The Answer For Autonomous Driving And Personal Robotics? - Forbes