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Global Medical and Lab Refrigerator Market 2021 Industry Analysis, Size, Share, Strategies and Forecast to 2027 The Oxford Spokesman – The Oxford…

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Global Medical and Lab Refrigerator Market 2021 Industry Analysis, Size, Share, Strategies and Forecast to 2027 The Oxford Spokesman - The Oxford...

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5 Ways To Stay Younger And More Creative As You Get Older – Forbes

5 ways to develop a younger mindset

There is a fountain of youth: it is your mind, your talents, the creativity you bring to your life and the lives of people you love. When you learn to tap this source, you will truly have defeated age - Sophia Loren

With life expectancy steadily rising in most countries around the world, the number of people aged sixty-five years or older will rise sharply over the next two decades. And you might already be one of them.

Certainly, were already seeing far more centenarians and near-centenarians than ever before. Just a few weeks ago, the world was collectively awed by nonagenarian William Shatners trip to space as the worlds foray into space. Not long after, there were numerous news stories about Dr. Manfred Steiner earning his third doctorate at the age of 89. But there are hardly outliers. More than ever, we are seeing other examples of seniors in their eighties and beyond continuing to enrich the world with their achievements instead of settling into the decrepit old age far too many people pessimistically see in their own future.

But what is it that makes these super seniors so vibrant and active? Though it is easy to attribute this ability to stay young to good genes alone, the opposite seems to be the case. A ground-breaking Danish study published in 1995 examined more than 2500 twin pairs born between 19870 and 1900 and concluded that genetics only played a modest role (at best) in human longevity. Instead, non-genetic factors, including lifestyle choices and environmental stress appear far more important in determining how long people can remain active and healthy over time.

Perhaps as importantly, the attitudes that people have towards growing older are often shaped by the kind of negative stereotypes too many of us have. These stereotypes often result from cultural expectations as well as the experiences people have dealing with their aging parents and grandparents. Along with affecting how people treat older adults, these stereotypes can also make us pessimistic about our own aging and what we will be capable of as we grow older. According to Stereotype Embodiment Theory,

people who internalize their own negative beliefs about aging are more prone to physical and mental health problems as well as becoming less productive as they age. A conclusion borne out by recent research.

This can include the belief that we are somehow doomed to become less creative and, presumably, less productive with time. Granted, this point remains controversial with many physicists, computer scientists, musicians, and even artists doing their most prominent work before they hit midlife (or younger). And yet, there are prominent exceptions: J.R. Tolkien was 62 when he wrote the first volume of the Lord of the Rings, prominent physicist Sir William Crookes was 68 when he began cutting-edge research into radioactivity, while Bertrand Russells work as a writer, academician, and peace activist continued until he was almost 100.

And this is just the tip of the iceberg when you consider that people over the age of 65 represent the fastest growing age group internationally, largely due to the major medical advances of the past few decades. As I have noted in prior articles, the biological limits of our own lifespans are being radically altered and new breakthroughs may push the upper limits of human longevity even further in decades to come.

But, there is more to aging than taking stock of your grey hairs and wrinkles. Along with physical aging, there is also psychological aging, something I have already covered previously on Forbes. Also known as subjective aging, our own research has demonstrated that a lower psychological age is linked to better mental and physical health. While true physical rejuvenation isnt available (at least so far), it is also possible to make yourself feel younger, something that is an important feature of successful aging. Here are just a few suggestions you can try, and you are welcome to come up with your own suggestions:

Set ambitious longevity goals for yourself, along with fosterity the optimism you will need to achieve these goals. Our research into using deep learning techniques to predict human psychological and subjective age shows that people who are more optimistic about the future of their health and longevity, expect to live to the age that is substantially longer than average in their country, and of their health and expect to stay in good health or even improve in the next 10 years and beyond. But, what might happen if you imagined yourself living to 120 years or longer and spending those extra years being healthy and productive? Even if medical technology fails to give you those added years, the positive mindset this optimism will produce can have valuable benefits in its own right.

Science is not standing still. Huge progress was made in science and technology in the past decade alone and you should expect to live much longer and healthier. Learn as much as you can about the recent progress in aging research and tart making your own plans for an extended future. Some books you can start with include popular non-fiction books like David Sinclair's Lifespan: Why We Ageand Why We Don't Have To,

Peter Diamandis's books, The Future Is Faster Than You Think, and Bold. You can also take a look at Sergey Young's book The Science and Technology of Growing Young. These are just a few of the books already available on what is already a hot topic in science and many more will become available soon enough.

Take a psychological aging test and try to develop a longevity mindset. Go to young.ai and register for the app which can also be downloaded onto your Android or iPhone. By answering a few simple questions about your medical history and syncing information from your medical tests or your FitBit or Apple Watch, you can receive age estimates based on your different measures. This includes estimates of lifestyle age based on your response to health survey questions, mind age based on your psychological survey responses, or blood and heart age based on biomedical data. There is even a photo age feature estimating age based on face appearance alone! Use the data the app provides to develop an action plan for staying younger.

Develop friendships with younger people and avoid the retirement peer pressure that might motivate you to act your age and just settle for a comfortable retirement. As I noted in previous articles, humans are very good at adapting to radical changes, whether positive or negative. This hedonic treadmill can also cause many older adults to become complacent about their lives and correspondingly less flexible in terms of handling changes and the stress that comes with it. This means that the best way to stay younger and more creative is to avoid this age trap and take yourself out of your comfort zone. At least once in a while.

Consider taking a few courses or even going for an entire degree at a university that requires group work and constant interaction with the younger people.

Join or start a new business, preferably in health or longevity. New business opportunities are springing up daily and this is a trend that can only go upwards in the years to come. Instead of focusing on retirement, you can think of new business opportunities for yourself. Many of these opportunities will stem from the growing number of over-65s living longer and more active lives. Start exercising your own creativity and plan out a business model that will revitalize your own life.

These are just a few suggestions to consider and you can likely come up with more with the right determination and a little creativity. Remember the words of Mark Strand who said that the future is always beginning now and start planning out your own future. It will be here faster than you think.

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5 Ways To Stay Younger And More Creative As You Get Older - Forbes

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2021 Best Insights From Quantum Computing Top Leaders Quantum Computing – Forbes

View of Cyborg hand holding Quantum computing concept with qubit and devices 3d rendering

QC Investment Today

Quantum Computing (QC) proof of concept (POC) projects are growing in Q4 2021 with commercialization pilots by 2025 and broader adoption before 2030.Accelerated digital transformation and digital reshaping from the pandemic is driving investments and early IPOs (ex. Q1 announcement by IonQ). In my daily engagements pro bono with global communities (across governments, industry, computing and research organizations, NGOs, UN agencies, innovation hubs, think tanks) of more than 60K CEOs, 30K investors , 10K innovation leaders, Im finding nearly 50% are planning pilots for QC in five years. Theres an understanding that the exponential lead provided by a breakthrough in QC warrants the early investment and learnings now since practical adoption will take years.

As a measure of progress and to stimulate collaboration/sharing in QC, the non-profit IEEE held their first Quantum Week in October 2020 and is holding their second conference IEEE Quantum Week 2021 October 18-22 2021. Ill provide a follow-up article after the conference.

Quantum Physics produces Quantum Effects from Quantum Mechanics providing Quantum Information Science that includes quantum computing, quantum communications, quantum sensing, quantum measurement, quantum safe cryptography and more. I often use QC as the general term for simplicity in this article to point to Quantum Effects-related to Quantum Information Science. Quantum Information Science is the better umbrella term.

Learn From the QC Top Leaders

In this article, I will highlight QC 2021 best insights from my chats with QC top leaders in 2021. The pro bono full video interviews can be found with the non-profits such as IEEE TEMS and ACM (see interviews series Stephen Ibaraki). IEEE is the largest non-profit electrical engineering organization and responsible for many of the global standards in use today in technology.

The QC interviewees include:

Michele Mosca: Co-founder, Institute for Quantum Computing, University of Waterloo; Founder of Quantum-Safe Canada and Quantum Industry Canada; Co-founder and CEO of the quantum-safe cybersecurity company, evolutionQ.

William Hurley, who goes by the name whurley: Innovator; Serial Entrepreneur; Founder & CEO Strangeworks, about Quantum Computing.

Scott Aaronson: David J. Bruton Centennial Professor of Computer Science at the University of Texas at Austin; recipient of ACM Prize in Computing; about theoretical computer science and quantum computing. The ACM prize is the second highest award from the ACM, which is the largest non-profit computing science organization.

Stefan Woerner: IBM Quantum Applications Research & Software Lead. Stefan is considered one of the top researchers in QC applications.

QC Top Leaders Best Pointers

Michele Mosca details quantum history and being at the founding of world leading physics and quantum research groups at University of Waterloo. We discuss the future of quantum, the probabilities of success timelines, and providing quantum risk assessment. In addition, Michele and his students have founded companies in this area thus the entrepreneurship journey is shared.

We discuss categories of quantum:

Quantum computing (QC), the focus on my January Forbes article where Google in 2019 and China in 2020 provided examples of Quantum Supremacy where problems are solved in seconds that would take thousands or billions of years on classical digital computers.

Quantum safe cryptography and designs to be safe from quantum enabled attacks. NIST (National Institute of Standards and Technology) working on QC standards. Encryption being vulnerable to quantum computing capabilities including where data can be stored and decrypted later by quantum computers.

Quantum communications where China is leading and also the UN agency ITU has programs such as Quantum Information Technology for Networks.

Quantum sensing providing ultrasensitive capabilities to detect underwater deposits and seismic events plus much more.

Willan Hurley whurley shares his experiences as a serial entrepreneur including having several startups exit within the same year. whurley then shares turning his attention to QC by authoring the book, Quantum Computing for Babies, and launching his startup Strangeworks. Strangeworks provides a platform with developer tools and systems management. In our chat, whurley states, I think if you look at IBM public roadmap, if you look at IBM Q, and Rigetti, and all of the companies and what they're doing Microsoft, Google; Google, even then announce it, they think they'll have their machine in 2029...and I think that they will actually do it before. So I predict Google will have a machine online, closer to the 2025, 2026 range...There's over 500 startups involving quantum right now today. When I started three years ago, they were like 12...And you're going to see a big inflection point driven by the government investment worldwide ... whurley talks about billions invested in France, Germany, China, USA ...you've got Norway, Finland, Russia, you've got everybody in this game now.

Scott Aaronson received the 2020 ACM Prize in Computing in April 2021 for his contributions to QC. In our chat, we talk about his work and his views on QC today and into the future. Its good to view our chat - as noted in the ACM prize citation, Aaronson helped develop the concept of quantum supremacy, which denotes the milestone that is achieved when a quantum device can solve a problem that no classical computer can solve in a reasonable amount of time. Aaronson established many of the theoretical foundations of quantum supremacy experiments. Such experiments allow scientists to give convincing evidence that quantum computers provide exponential speedups without having to first build a full fault-tolerant quantum computer. The ACM citation provides notable contributions with: Boson Sampling, Fundamental Limits of Quantum Computers, Classical Complexity Theory, his respected book on QC Quantum Computing Since Democritus and Scotts work Making Quantum Computing Accessible (ex. his popular blog.Shtetl Optimized).

Here are excerpts from my extensive chat with Stefan Woerner. The interview has been edited for clarity and brevity and I used AI to provide the transcript (which has limits). I recommend going directly to the video interview for our nuanced discussion.

Stephen Ibaraki

I ask how Stefan got into quantum computing.

Stefan Woerner

And then I started to look into how can we apply this to problems I looked into before, for example, in optimization or in finance, and it turned out that, that there are many things that can be done...quantum computing gave me a new toolbox to look at the problems that I studied already for quite a while and it opened up completely new directions. It also came with quite new challenges. But but I think it's extremely exciting for me. Now having this additional tools, additional possibilities to try to solve relevant problems and eventually have an impact with optimization or with Monte Carlo simulation and things like that.

Stephen Ibaraki

That's fascinating, your grandfather's sort of stimulating this interest in mathematics and sciences in general as well...And then in your early work, using mathematics, did you use supercomputers at that time in your optimization problems?

Stefan Woerner

We did some optimization on the cloud. And we used some cloud solvers. But these were not supercomputing. So our approach was more to try to find good formulations that are accessible by the solvers. We had, for example, writing our own simulations for supply chains that could be leveraged in an optimization setting.

Stephen Ibaraki

Quantum computing is still a mystery to a lot of people and especially to developers so there's more and more tools coming out. You have the IBM challenge to try to make it easier for the broader community to start experimenting with quantum computing. But before we delve into the tools you have and how you make it accessible for proof of concepts. Let's go back to basics, what is quantum computing?

Stefan Woerner

So quantum computing is a completely new computational paradigm where you leverage the laws of quantum mechanics. And that means if we now really go to the basics, classically, you have a bit, that's either zero or one. In quantum computing, you have the quantum bit qubit, which can be a superpositions of zero or one. And that sometimes this is explained like it's 50%, zero or 50%, one. But that's not 100% true; it's really like a superposition, it's this state in between, so you can think of it as a continuous variable, in a way a continuous value. If you have two qubits they can also be entangled. And in a way, this means that the state of two qubits can be correlated. So if you can, you can construct states that are perfectly correlated, where the state of the one qubit perfectly determines the state of the other qubit. So if the one [qubit] is zero, you know, the other one is also zero. And the other way around, if the one [qubit] is one, the other is also one. So this correlation of two particles, which are two qubits, this is something that's purely quantum mechanical. This doesn't exist in classical computing and classical electronics. And if you scale this, this means that the state space of a system of qubits scales exponentially. So that the state space to describe the system really scales extremely fast to something that's way beyond you can handle classically, that alone would not be enough. There's one more feature, let's call them interference. And you know that from sound or from water, you can have constructive and destructive interference where waves are adding up or they're cancelling out. And this is something that we leverage in quantum computing, as well. So you can have this high dimensional states, and then you can let them interfere. And that's what actually then amplifies probabilities of good solutions. Now, this also tells you one important thing, a way a quantum computer is working. And the way you program a quantum computer is completely different to how you would do this classically. Because you need to translate your problem now into something that's leveraging this interference in a way.

Stephen Ibaraki

There's this idea early on in quantum computing, where they're measuring the capabilities by the number of relatively stable, qubits, or logical qubits. And then IBM came up with this idea of quantum volume. They're saying maybe qubits is not a great way of representing the capability of a quantum computing. Can you explain IBM's concept of a quantum volume?

Stefan Woerner

Qubits that we built today, let's refer to them as physical qubits. They are noisy so they after a while they lose the state, the operations that we can can use to control their state or to modify the state are not perfect. So there's an error. And that means it's so difficult to really operate with these qubits until you really have to imagine here this is really trying to harness nature as its extreme. It's, in our case, superconducting qubits. So they are in a very cold environment and shielded from external disturbances and so on. These physical qubits, they're kind of fragile and you can have lots of qubits, but if they have very high error rates, you won't be able to use all of them. Because once you operated on all of them and entangled them, and so on, you introduce so much noise that you're not getting out anything meaningful anymore. So you really need to take into account the number of qubits, that is an important factor. But as you said, not the only one. But also the errors indicating the decoherence time. So how long the qubit keeps it state, and things like that. And now the quantum volume is a single number that's determined by some benchmarking circuit. So you run some operations on your quantum hardware, where you kind of know the result, or you can evaluate the result. And you can then say, whether this is above a certain threshold or not. And then if you can run this on a certain certain number of qubits and with a certain number of operations, and this determines the quantum volume. And so the quantum volume in a way determines how many qubits you can use with a certain number of operations, meaning that the number of operations that you run sequentially is about the number of qubits. But this is kind of benchmark. So the single number benchmark that puts on it, that takes into account the number of qubits and noise and all these factors that actually impact the power of a quantum computer. Now, this is for these physical qubits, then now looking forward, once we reach a certain size and a certain quality, then we can leverage error correction. And we can get to fault tolerant quantum computing. In here, we take many physical qubits, and we encode them as one logical qubit. So there's like an abstract and logical error correction layer on top of that. And this overhead is relatively large, so it's estimated that you need a few 100 to 1000 physical qubits to get to one logical qubit. And then this logical qubit has a significantly suppressed error. And then you can start to work with that, in this clean theoretic computational paradigm where you ignore more or less the noise from the hardware.

Stephen Ibaraki

IBM, announcing their 1 million qubit roadmap by 2030. What does that roadmap mean? I know you've got some interim results that you're targeting: 2023, 2025, etc., 2030. What are the implications of this roadmap?

Stefan Woerner

So I think the next couple of years will be would be very, very exciting for different reasons. So the roadmap that we announced that says that, until 2023, we reach a quantum chip with more than 1000 qubits and also give some specifications on the error rates that these qubits should have because as I mentioned before, qubits, just the number of qubits doesn't mean too much. So the quality needs to be improved as well, to really get a more powerful quantum computer and so we get over 100 qubits. So currently we have 65 [qubits]. Last year we released 65 that can be accessed to the cloud this year, we plan to get to 127 I think, next year 433. And then after 2023 over 1000. And, in the roadmap and also the technical details, like what leads to this improvement, what are the changes that help us to grow these chips. Now getting to 1000 qubits is kind of an inflection point. And this is because, as I mentioned before, this is about the number that you need to to build a logical qubit. So that's where you can really start to study, fault tolerant quantum computing, maybe at a small scale. But, that will be then the first time this really can be investigated in depth. And then the next step to scale to the millions; also then to not have like more and more qubits on a single chip, but also go for example, you could imagine that you combine multiple chips with 1000 qubits. And that way, get a larger quantum computer. Well, that's from the technological development, this is extremely fascinating. And I think also, this path to the 1000 qubits will be extremely interesting for applications and algorithms, researchers like myself, because right now when we run algorithms on real hardware, and also when we simulate them classically, which is very, very expensive computationally, because it scales exponentially in a number of qubits...and once we scale to 60, 100, 400, 1000 cubits, this is really where we can see that the asymptotic behavior of these heuristics, so this is really where we can start to make forecasts about how they will perform for interesting problems. And I think that this will result in us getting a way better understanding of what we can do with near term quantum computers for optimization for machine learning, for things like that.

Stephen Ibaraki

Different companies and research groups come out with different claims; there's a group out of China recently came up with a claim that they've achieved some kind of quantum supremacy that it would take a supercomputer, over 2 billion years to do this kind of quantum problem of Gaussian boson sampling. Google, made some buzz, in 2019, where they released the Sycamore system, and they indicated, quantum supremacy on this quantum problem. It's not a practical problem, but really just to illustrate that it can do something that maybe supercomputers can't do. And yet IBM looked at that and said, maybe that's not as big of a breakthrough as you're indicating, because really, we can get that done on a supercomputer just by improving our algorithms to maybe a few days. So maybe it's not quantum supremacy is. So what is supremacy, what is quantum advantage? There's these words being thrown out, and what is it real?

Stefan Woerner

So we don't use quantum quantum supremacy for multiple reasons. One, is we don't believe that quantum computers will become superior to classical computers at any point. And so a quantum computer cannot speed up everything. A quantum computer can be used as an accelerator for some tasks. So I think it will always be a combination of classical and quantum computers that work in harmony to solve some problems. So it's not like you won't write your emails with a quantum computer [you will NOT be using quantum computers to write emails], you might solve some computationally heavy quantum chemical simulations or control optimization problems with a quantum computer. And now, what do we mean by quantum advantage? That's if you can do something with the help of a quantum computer that has some practical value. So I think what you mentioned are very nice experimental demonstrations. And important steps on the development of quantum computers. What we're looking for is really a practical value that has been achieved with a quantum computer. And I think that still is a bit out in the in the future.

Stephen Ibaraki

You're an expert in quantum computing, but there's different kinds of quantum computing. And what I mean by that: trapped ion concept, topological quantum computer that Microsoft has been chasing for some time, very low temperature spin, photonic, can you get into a summary of the different categories and why IBM has chosen your particular way of doing quantum computing?

Stefan Woerner

Trapped ions, spins, photonics, and also in superconducting they're different designs, we will look into superconducting qubits, because we think that's particularly in the near term, the most promising to scale...superconducting qubits are operating in very low temperature...about 50 milli Kelvin, which is, I think, 100 or 1000 times colder than outer space. So this really like just above the absolute zero temperature. And that, that sounds very challenging. But this, dilution refrigerators that get down to these temperatures, this is something that is actually quite reliable and well understood technology. So that first sounds like a big problem. But I think that's something that has been quite well understood. And if you have that solved, or if you have the environment where you can operate them, then you can process these chips, you can come up with different designs, the superconducting qubits, for example, at a larger scale than the spins. So I think, to get these near term systems, that there might be an advantage in processing, in fabricating them. And we came up with a design that is also accessible to error correction. So here's, that's an example where the theoretical research and error correction and the people who design the devices are really like collaborated very nicely because the design of the chip has been chosen such that it's good to manufacture it, and which has then let the error correction team to come up with new error correcting codes that can be run, eventually on this hardware. So these are all pieces that fit together that make us believe that we can scale this to 1000 cubits and then if we, for example, can connect larger chips also to the to the millions.

Stephen Ibaraki

I've been in computing for such a long time. And I remember in the early days, we would flick toggle switches, and program literally in binary code; we moved to assembler then we went to higher level languages. We got to a stage where you had abstraction of the hardware through an operating system; you can write to a more generic kind of code using a much higher level language and that made it much easier. So what is the work being done in that area in quantum computing, to abstract the hardware underneath from an operating standpoint; using toolkits?

Stefan Woerner

So, we just released the development roadmap earlier this year, which, addresses this to some extent, like how the stack will grow, how levels of abstraction will be included, whether this is for, like, pre defined quantum circuits, that you don't have to build the circuit yourself. But that, you know, there's hardware, a library of pre compiled for the hardware, pre compiled circuits, and it's like an optimized instruction set. And, things like that up to actual application services. And now, in terms of the actual languages, I think we are in a very interesting situation, which is a little bit different to what you explained before, because on the one side, we are at the stage of defining the new assembler standard, which is a quantum assembly language. But at the same time, we do have the classical languages, right, we do have a Python, for example, that we can embed all of this in. So we have a render situation that we can leverage the classical existing high level languages. And in this embed these new functionalities, we can write functions, classical functions, that compile or assemble or optimize some of these quantum stuff. And that, that allows us, for example, to build work on application modules. So you, I think, you mentioned qiskit before, qiskit is our open source Python framework, to program quantum computers to define quantum circuits to simulate quantum circuits and to also send them over the cloud to the real hardware. And within qiskit, we are building application modules. And here we're looking into the moment in four different application areas, there's optimization, there's natural sciences, there's machine learning, and finance. And the optimization module has been released the middle of last year. And what this does is it, it allows you to use a classical high level language to specify your optimization problem. Because that's something that has been solved, right, this is nothing quantum computing specific. Like classical optimization, subject matter experts knows how to define a optimization problem using different languages as for example, an IBM language, to model your problem. And now what the qiskit optimization module allows us to take this classical problem, and it automatically translates it into different different representations that are then accessible to different quantum optimization algorithms. So we on the one side, we still work on the assembly level. But on the other side, we have the classical language that does all the translations for us from a high level problem down to an actual circuit. And these, these optimization modules are built in such a way that it's very easy to get started. So you can, if you are like a subject matter expert in one of these domains, you can just download these modules, they are open source, and you can get the tutorials that actually allow you to use the quantum algorithm as a black box. So the entry barrier to run your first quantum optimization program on some illustrative example, is very low, forever. This whole thing is also built in such a modular and flexible way that you can use it as a black box, but you can open the black box, you can look at every level, you can tear it apart, you can replace different pieces by your own implementation, and see whether they improve, whether they change, how do they compare. So it's built in a way that is easy to get started. But that also really, really supports cutting edge research in these areas.

Stephen Ibaraki

But ultimately, if you want to have, mass proliferation, or usage, you will have to work at this much higher abstraction level. So it's easier for people to get involved. And I guess that's the reason behind the IBM challenge, right, to get people involved. And I read last year your two biggest communities who tried it, were in the data science area, and then financial services, but you also have people like high school students trying and completing the program. Can you talk about this challenge and what you're trying to do? And, and typically, what it involves maybe it's three or four stages of things that you put people through, and you actually get quite a few actually going through the entire program. So can you give us an example what that is like?

Stefan Woerner

This challenge was a collection of problems / tasks that people could apply and try to solve. And this included problems using qiskit, to solve an optimization problem. We have different difficulty...many people really reached the highest score... If I remember correctly, some people even reached the score where there was a little bit higher than anticipated. And that the challenge was one thing, but there was also a kid summer school, ...global summer school with, if I remember correctly, around four or 5000 participants globally. So we provide these educational offers, because it's really important, as you say, for people to be able to get into how this works, but what's different, to grow also the workforce in this in this area, because there will be an increasing demand. And I think, because it is so different, because it is still new, we just figured out the tip of the iceberg of what to use a quantum computer for or how to use a quantum computer to solve problems. So I think it will be extremely important to educate more people around quantum computing, and you see universities picking that up and coming up with new quantum computing curricula, and so on. And so this is important also to really leverage the full potential of this technology.

Stephen Ibaraki

Microsoft had a blog post where they indicated that it's really not suitable right now for for problems, which have a lot of data requirements, either data and in getting data in and getting data out, it's really more for certain kind of computational problems. And where you're really taking advantage of the unique capabilities within quantum computing. And you've indicated that as well, it's not a standalone; my iPhone isn't going to have a quantum computer in it; it's going to be in combination, or in hybrid form in some way. And you're seeing that with D-Wave, which has a piece of this quantum capability with their quantum annealing, but they have these hybrid systems. That leads to this question, what kind of industries are really suitable for quantum computing? What kind of problems are really suitable for quantum computing? What are the different categories where this whole quantum phenomena is being exploited right now? Or you think we'll have some major kind of advantages going into the future?

Stefan Woerner

Let me get back to the first point you mentioned about data. Because I think that's important. I indicated at the beginning that quantum computers can solve some problems better and that's really important, not all problems. And big data problems are like, if the problem is not that the tasks you want to execute is computationally very complex, but that you want to run it on like a tremendously large data set. Then this is very likely not a quantum computing use case, because loading this large data to a quantum computer just has complexity of the size of the data. But then many of these large are many of these big data algorithms. Classical algorithms also have that complexity, like if you have a big data set and your complexity is quadratic in the data size, and this probably won't work. And that means that loading a big data set into a quantum computer. Well, and here we're talking most likely about a fault tolerant quantum computer will have the same complexity as doing a solving the problem you're interested in classically. So for some problems is just a fundamental limitation; the good example is Grover's search, which is sometimes illustrated as searching unstructured database. But the first thing you have to do is you have to load this database into a quantum computer. And when you load this, and you have to take every element, then you just stop when you found what you're looking for. So we don't load the full database to the quantum computer, but that you would have to search it. So these things can happen. I think particularly in quantum machine learning algorithms, often this fact is is not considered. And still there are some interesting theoretical results. But if you want to look into this, from an application point of view, you really need to analyze it end to end from loading the data to extracting the result. And only then you can make a statement about a potential practical quantum advantage. Now on your question in the industries, so we actually working with quite a lot of companies; I think in the IBM quantum network, we have over 130 members by now. And there's of course, the financial service sector we're working with; with JP Morgan Chase, Goldman Sachs, on things like options, pricing, the derivative pricing and credit risk analysis or risk analysis in general, also optimization, portfolio optimization, things like that. So, I think the financial service sector is an industry that has a lot of of interest in quantum computing, because it's a very compute intensive industry. And, for example, many, many things are done by a Monte Carlo simulation, where we might have some potential speed ups with quantum computing. But there's also a lot of optimization and also machine learning; if you think about credit card fraud, this is something that still causes a lot of costs for the credit card industry and if they could reduce the false positives, and they would significantly reduce costs and improve proof reputation, because no customer likes accidentally blocking of their credit card. So, this is one sector, then since quantum computing might speed up optimization problems, there eventually might be a use cases around logistics supply chain, all these things...I mean, the original idea for quantum computing as Feynman formulated, this was for simulating quantum systems...quantum chemistry, quantum physics, material science, ... and eventually use cases...life science, industry and chemical industry, this these are certainly use cases that might really have a large potential...We have a lot of activities around quantum chemistry. And how to eventually scale this to get to design new materials or to understand to how chemical reactions work to build new catalysts that allow to run some chemical reactions at ambient conditions where today we require lots of energy and so on.

Stephen Ibarakiand Stefan Woener

I ask for further POCs in the near term and Stefan provides added examples. Stefan also looks longer term. ...opens up completely new ways of doing business of doing, for example, financial product, if you have like real time risk tracking, which can also maybe even prevent different things because you can react way faster. So it can lead to a way more informed decision making in multiple businesses... I think quantum quantum computing has also the potential to solve some of the really big problems that society may face in the coming years, whether this is fertilizers for food, and so on, which can use a lot of energy these days. And so this is something where it might help and there are a couple of examples where / when nature does something extremely efficient, and humans have no clue how to reproduce that. And I think with quantum computing, once we really figured out how to build this hardware, and then also, there's a lot of open questions on the algorithms. This might give us a completely new lens to look at nature, to look at how things actually work. So I would imagine that this helps us also to really push the fundamental understanding of how the world actually works, eventually.

We explore areas: quantum cryptography, quantum encryption and decryption and Shor's algorithm, a quantum accelerator, quantum sensing, quantum communications, quantum gravimeters, 20 million qubits where Shor's algorithm becomes a real factor, and in breaking RSA encryption, quantum key distribution.

We get into a discussion about quantum inspired applications (apply the principles to solve real problems today, even though the quantum hardware isn't quite there yet. And when it's ready, it scales.) Stefan provides his insights including improvements to classical software, It's a nice term for classical algorithms. I think, in principle, it's very cool if quantum algorithm research can also inspire finding new classical algorithms. I think this can happen either by kind of de-quantizing, some quantum algorithms, as we have seen, in the last years that there is a, like a quantum algorithm that promises a certain advantage. And then people have found how to kind of mimic some of the of the core parts of this algorithm using some classical sampling techniques. And they could show similar performance. I mean, this is always a little bit disappointing if you try to show a quantum advantage with this algorithm. And then classical algorithms can beat that. But I think it's a pretty cool development. But it stays a classical algorithm...that is not to forget that it is just a classical algorithm. It doesn't give you any advantage from coming from quantum; it's a classical algorithm that has been designed by using some ideas that are coming from quantum computing, but it's based on classical computers. So it will not give you a quantum advantage because it's classical.

We get into philosophical discussions about new kinds of computing and on quantum effects including on consciousness.

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2021 Best Insights From Quantum Computing Top Leaders Quantum Computing - Forbes

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Biotechnology Market 2021: Report Aims to Outline and Forecast, Organization Sizes, Top Vendors Overview, Growth Factors, Industry Opportunity and End…

The global biotechnology market estimated to grow at a CAGR of 10.5% during the forecast period from 2018 to 2025. The market for biotechnology was valued at USD 218,012.1 Mn in 2017 and is estimated to reach US$ 471,336.4 Mn by 2025.

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The global biotechnology market is segmented on the basis of technology and application and geography. On the basis of technology, the biotechnology market is segmented into DNA sequencing, fermentation, cell based assay, nanobiotechnology, chromatography, PCR technology, tissue engineering and regeneration and others. On the basis of application, the biotechnology market is segmented into industrial/bio processing, bioinformatics, food & agriculture, health, natural resource & environment and others.

The biotechnology market is aimed to describe, define and estimate the forecast for market size of the biotechnology till 2025. The report strategically analyzes macro and micro-markets to entail the major factors impacting the growth of the global biotechnology market. The market report for biotechnology is appropriate to cater the needs and demands of various stakeholders that include pharmaceutical, biotechnology and medical companies in the form of research services.

The major players operating in the biotechnology market include Thermo Fisher Scientific Inc., Merck KGaA, PerkinElmer, Inc., Agilent Technologies, Inc., F. Hoffmann-La Roche Ltd., Danaher, QIAGEN, BD, Bio-Rad Laboratories, Inc., Illumina, Inc. and among others. The global biotechnology market is highly competitive and driven by large number of novel product launches and approvals. For instance, in April 2017, Illumina, Inc. introduced BaseSpace Informatics Suite, used to accelerate genomic data analysis for sequence lab.

Research study is a highly acclaimed resource that investors, market contestants, and other people interested in this Biotechnology report can use to intensely position themselves in the global Biotechnology market. It mentions the recent developments structures, future growth plans, and other significant aspects of the business key participants that define their growth in the global Biotechnology market.

Biotechnology Market Important Factors:

Biotechnology Industry research report is a meticulous investigation of the current scenario of the Biotechnology global and regional market, which covers several industry dynamics. The Biotechnology market research report is a resource, which provides current as well as upcoming technical and financial details with market risk, growing demand and raw materials. The thorough analysis in this report enables investors, CEOs, regional traders, suppliers, top vendors to understand the market in a better way and based on that knowledge make well-informed decisions.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous…

London, June 03, 2020 (GLOBE NEWSWIRE) -- Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and machine learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

Theartificial intelligence in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to 2027 to reach $21.8 billion by 2027. The growth in this market is mainly driven by rising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semi-autonomous applications. In addition, consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain complementing growing industrial automation are further offering opportunities for vendors providing AI solutions in the supply chain industry. However, high deployment and operating costs and lack of infrastructure hinder the growth of the artificial intelligence in supply chain market.

In this study, the globalAI in supply chain market is segmented on the basis of component, application, technology, end user, and geography.

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Based on component, AI in supply chain market is broadly segmented into hardware, software, and services. The software segment commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increasing demand for AI-based platforms and solutions, as they offer supply chain visibility through software, which include inventory control, warehouse management, order procurement, and reverse logistics & tracking.

Based on technology, AI in supply chain market is broadly segmented into machine learning, computer vision, natural language processing, and context-aware computing. In 2019, the machine learning segment commanded the largest share of the overall AI in supply chain market. This growth can be attributed to the growing demand for AI-based intelligent solutions; increasing government initiatives; and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies

Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

Based on end user, artificial intelligence in supply chain market is broadly segmented into manufacturing, food & beverage, healthcare, automotive, aerospace, retail, and consumer packaged goods sectors. The retail sector commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increase in demand for consumer retail products.

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Based on geography, the global artificial intelligence in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. In 2019, North America commanded for the largest share of the global artificial intelligence in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

On the other hand, the Asia-Pacific region is projected to grow at the fastest CAGR during the forecast period. The high growth rate is attributed to rapidly developing economies in the region; presence of young and tech-savvy population in this region; and growing proliferation of internet of things (IoT); rising disposable income; increasing acceptance of modern technologies across several industries including automotive, manufacturing, and retail; and broadening implementation of computer vision technology in numerous applications. Furthermore, the growing adoption of AI-based solutions and services among supply chain operations, increasing digitalization in the region, and improving connectivity infrastructure are also playing a significant role in the growth of this market in the region.

The globalAI in supply chain market is fragmented in nature and is characterized by the presence of several companies competing for the market share. Some of the leading companies in the artificial intelligence in supply chain market are from the core technology background. These include IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google LLC (U.S.), and Amazon.com, Inc. (U.S.). These companies are leading the market owing to their strong brand recognition, diverse product portfolio, strong distribution & sales network, and strong organic & inorganic growth strategies. The other key players in the global artificial intelligence in supply chain market are Intel Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Samsung (South Korea), LLamasoft, Inc. (U.S.), SAP SE (Germany), General Electric (U.S.), Deutsche Post DHL Group (Germany), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), FedEx Corporation (U.S.), ClearMetal, Inc. (U.S.), Dassault Systmes (France), and JDA Software Group, Inc. (U.S.), among others.

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Artificial Intelligence (AI) in Supply Chain Market is projected to reach $21.8 billion by 2027, Growing at a CAGR of 45.3% from 2019- Meticulous...

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Global Resveratrol Market is projected to reach US$ 278.3 Million by 2033 at a moderate CAGR of 8% | Get In-depth Report by Future Market Insights,…

Future Market Insights Global and Consulting Pvt. Ltd.

According to Future Market Insights, the North American region is forecast to lead the resveratrol market in 2023 and is likely do so throughout the forecast period. United States is a promising market in this region, which was estimated to have acquired 17.3% market share in 2022.

NEWARK, Del, May 09, 2023 (GLOBE NEWSWIRE) -- The estimated worth of the worldwide resveratrol market in 2022 was US$ 118.60 Million. Sales of dietary supplements is likely to increase to US$ 278.3 Million by 2033 thanks to shifting customer tastes, with a CAGR of 8% predicted for the forecast period of 2023 to 2033.

The growing demand for nutritional supplements among consumers is one of the main factors driving up the price of resveratrol on the international market. The market is expected to grow due to the product's high anti-antioxidant content and phenolic activities, which are popular with older consumers and sportsmen, as well as the increased prevalence of health issues including cardiovascular diseases. The increasing usage of nutraceuticals by the populace and the increasing reliance of the beauty sector on resveratrol all have an effect on the market's growth.

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The use of this substance has been associated with a wide range of implications, including anti-angiogenic, estrogen-like effects, skin-whitening, anti-aging, collagen I activation, and its capacity to protect cells from oxidative damage and UV radiations-mediated cell death. It is well-liked in dermatology as well as cosmetology due to its ability to penetrate the skin barrier and its anti-aging properties.

Resveratrol has experienced extraordinary penetration in developed North American economies due to growing consumer demand for natural and healthy products. Consumers are turning to resveratrol supplementation for its antioxidant as well as anti-inflammatory qualities. Cardiovascular issues have increased in frequency in the United States as a result of the people's sedentary lifestyles, poor eating patterns, and hectic schedules. Resveratrol is a common ingredient in dietary supplements, which is anticipated to fuel the growth of the resveratrol market over the forecasted time period due to its multiple health benefits.

Story continues

Key Takeaways:

The global resveratrol market is expected to be valued at US$ 123 Million by 2023.

From 2018 to 2022, the market demand expanded at a CAGR of 3.1%.

In 2022, the United States market for resveratrol accounted for about 17.3% of the global market share.

The Indian resveratrol market to experience a rapid CAGR of 14% from 2023 to 2033.

By product type, the extract or plant-based resveratrol was estimated to acquire more than 40% share in 2022.

By Isomer, the trans-resveratrol had acquired a market share of 87%.

Resveratrol is gaining traction owing to its health benefits and having antioxidant, anti-inflammatory and anti-aging properties, says an analyst at FMI.

Competitive Landscape:

Prominent players in the resveratrol market are:

DSM Nutritionals

Evolva

Endurance Product Company

Great Forest Biomedical

Laurus Labs Limited

JF-NATURAL

Sabinsa Corporation

Resvitale LLC

Shanghai Natural Bio-engineering Co., Ltd.

Some key developments in this market are:

In 2021, Lonza, a worldwide manufacturing partner to the pharmaceutical, biotechnology, and nutrition sectors, declared it is likely to invest to build its production capabilities for pharmaceutical products at its facility in Guangzhou, China.

The fill as well as finish production line is likely to provide clinical trial and commercialization batches in China as well as local and international clients. In keeping with Lonza's objective to provide clients integrated end-to-end solutions, the launch of drug product manufacturing at the Guangzhou (CN) facility is likely to provide customers with a unified drug substance as well as drug product manufacturing service option.

More Valuable Insights Available:

Future Market Insights offers an unbiased analysis of the global resveratrol market, providing historical data for 2018 to 2022 and forecast statistics from 2023 to 2033.

To understand opportunities in the resveratrol market, the market is segmented on the basis of resveratrol by product (extract, fermentation, synthetic), form (powder and liquid), isomer (trans-resveratrol and cis-resveratrol), end-use and across five major regions (North America, Latin America, Europe, Asia Pacific and Middle East & Africa).

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Key Segments Profiled:

By Product:

Extract

Fermentation

Synthetic

By Form:

By Isomer:

Trans-Resveratrol

Cis-Resveratrol

By End Use:

Skin Care

Cream & Lotion

Scrub Exfoliator

Cleanser & Toner

Balm & Butter

Serum & Mask

Makeup Remover

Others

Hair Care

Shampoo

Conditioner

Essential Oil

Hair Color

Hair Stylist Products

Hair Oil

Makeup

Facial Makeup

Eye Makeup

Lip Makeup

Nail Makeup

Bath Care

Shower Products

Liquid Bath Products

Bath Additives

Bar Soaps

Fragrance

Tools

Dietary Supplements

Pharmaceuticals

Others

Table of Content (ToC):

1. Executive Summary | Resveratrol Market

1.1. Global Market Outlook

1.2. Demand-side Trends

1.3. Supply-side Trends

1.4. Technology Roadmap Analysis

1.5. Analysis and Recommendations

2. Market Overview

2.1. Market Coverage / Taxonomy

2.2. Market Definition / Scope / Limitations

3. Market Background

3.1. Market Dynamics

3.1.1. Drivers

3.1.2. Restraints

3.1.3. Opportunity

3.1.4. Trends

3.2. Scenario Forecast

3.2.1. Demand in Optimistic Scenario

3.2.2. Demand in Likely Scenario

3.2.3. Demand in Conservative Scenario

3.3. Opportunity Map Analysis

3.4. Product Life Cycle Analysis

3.5. Supply Chain Analysis

3.5.1. Supply Side Participants and their Roles

3.5.1.1. Producers

3.5.1.2. Mid-Level Participants (Traders/ Agents/ Brokers)

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Global Resveratrol Market is projected to reach US$ 278.3 Million by 2033 at a moderate CAGR of 8% | Get In-depth Report by Future Market Insights,...

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