Quantum computers can process massive, complex datasets more efficiently and effectively than classical computers

Quantum computers has tremendous applications

With time, the tech will get democratised and trickle down to the consumer

There is a huge wave of research currently being done in the field of Quantum Computing. This research might just be the pioneering technological breakthrough that will enhance our future beyond what we can comprehend. Before we talk about what it is, lets get a bit of context.

Putting his pulse on the emerging trends, Gordon Moore, founder of Intel, figured that computing power would increase in power and decrease in cost exponentially with time. This became the basis of what is known as Moores Law, a golden rule for the electronics industry, and clarion call innovation. Since then Moores law has barely faltered in its unrelenting march. However, computing is now en route to hitting a wall.

Moores law is slowing down Computing power isnt increasing as quickly as it used to. Classical computers are turning out to be inefficient at solving many new problems like optimising multiple variables for decisions or simulating complex models.

These problems need computers to flip through multiple solutions and make myriad computations. Classical computers arent able to compute as quickly as these problems demand because they have to compute sequentially, or with limited parallelism

Most believe the way to overcome this barrier is by inventing a completely new paradigm of computing quantum computing.

What exactly is Quantum computing? Simply put, Quantum computers can process massive, complex datasets more efficiently and effectively than classical computers. In Classical computers, data for information processing is encoded into binary digits (bits) and have a value or state of either a 0 or 1.

In quantum computing, data is encoded in quantum bits (qubits) which can have values of 0, 1, or any quantum superposition of the two-qubit states. What this means is the bit can be both 0 and 1 at the same time.

Lets use a simple example to illustrate the potential. Imagine you have just gone grocery shopping and have bought 4 items of varying size. You also have one bag to place all four into. One has to select the most optimum way to fill the bag as to not damage the groceries.

Assuming you have no knowledge of which combination works and how the items interact with each other, it only makes sense for you to try all possible arrangements one by one and see which one gives you the best results.

But going through each arrangement one by one will take time, since there are 24 possible arrangements. What if you could have 24 helpers who could simultaneously fill up 24 bags with one of the arrangements and shout out the result to you?

Then you could find the optimal arrangement in the time of essentially filling one bag. Thats what a quantum computer allows you to do. It allows you to access all possible states and variables parallelly and not just sequentially.

I believe this power of Quantum Computers has tremendous applications. Over the next 5 decades, I believe we will reach an inflexion point of qubit capability. The initial machines will be accessible to enterprises, which will spawn an ancillary industry of complementary tools that provide easier interfaces to computers through classical computers.

With time, the tech will get democratised and trickle down to the consumer. An industry around QC software and algorithms will then have truly arrived.

As the number of qubits in quantum computers increase, we will first start seeing optimisation and data access problems being solved first. For example, with enough qubits, we could use quantum computers to assemble and sort through all possible gene variants parallelly and find all pairs of nucleotides the building blocks of DNA and sequence the genome in a very short period of time.

This would revolutionise the health industry as sequencing the DNAs at scale would allow us to understand our genetic makeup at a deeper level. The results of access to that kind of knowledge are unfathomable.

Next, through significant improvements in our quantum capacity, we will be able to use quantum computers for simulating complex systems and behaviours in near real-time and with high fidelity.

Imagine simulating the earths winds and waves with such accuracy so as to predict storms days before they come. Imagine simulating how the winds on a particular day would interact with a flight on a particular day and route it would allow us to measure turbulence, optimise flight paths, and better in advance.

Regardless of the path we take, Quantum Computing is here to stay. Its a key piece in the puzzle that is human growth. 10 years, 100 years, or maybe even a 1,000 years down, we will wonder how we lived without them.

Note: The views and opinions expressed are solely those of the author and does not necessarily reflect the views held by Inc42, its creators or employees. Inc42 is not responsible for the accuracy of any of the information supplied by guest bloggers.

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Quantum Computing: What You Need To Know - Inc42 Media

What's up next for quantum computing? Possibly weather forecasting and online dating.

Dan Patterson, a Senior Producer for CBS News and CNET, interviewed futurist Isaac Arthur about what's next for quantum computing. The following is an edited transcript of the interview.

Isaac Arthur: It's always hard to guess with computers, and we're a little bit spoiled by Moore's Law from the fifties and sixties just taking us from these really simple devices to what we have nowadays.

We do not want to make the same mistake we made with, for instance, nuclear fission and fusion where we got the development in 20 years and just assume the next one will get to us in another 20.

Quantum computing might be many decades before we see any real major progress, but at the moment, we have made quite a few major leaps and actually are doing some real calculations with this.

SEE:Managing AI and ML in the enterprise 2020 (free PDF) (TechRepublic)

We have a whole bunch of problems in terms of making it better, though. The biggest one is actually getting the right answer out of it. As an example, if we were using the random source before--let's say I locked somebody inside a quantum box with a phone book, and I told them, 'I want you to find a phone number, and if you call this correct phone number and here's the phone number in this book, someone's going to come by and let you out of this box.'

That person is then given a random number generator, and we shut the box, and they search. A whole bunch of different quantum ghosts of them appear, searching various pages, but the one who finds the right one calls that, and the person comes and opens the door. That's one example of a data extraction, though that would never work in actual reality because quantum doesn't do both on the macroscopic scale, but you can get errors from things like that.

First, imagine one of these quantum people searching that page didn't call the right number, but instead accidentally called a pizza delivery place that showed up and opened the door to deliver a pizza. Now, we have a wrong answer. We have things like this happen with quantum computing where we have an error, in terms of the data. We used to have this with normal computing too, but we solved it fairly early on.

This is probably going to be a lot harder to do, and in many ways, it's the hardest part other than actually keeping all of these protocols entangled. It's not just trying to keep one particle like this. We have to keep several thousand potentially--or millions--all entangled with each other simultaneously. This also allows them to be at just a hair above absolute zero temperature-wise. And then, of course, we have our third problem that has to be overcome, which is the software.

SEE: Augmented reality for business: Cheat sheet (free PDF) (TechRepublic)

All this runs on algorithms being had on class computers fed into these things, and those algorithms are the only way that we still have to do a lot of work on to improve them because we're not quite using the original pure algorithms like Shor's [algorithm], but ones we've had to adapt along the way. Those are kind of three areas--the software and the hardware areas are the ones that are going to really control limitations on advancing.

How much bigger can we make the entangled system? How well can we actually pull the right answer, and how do we actually get the right algorithms to ask the right question, as well?

What we tend to think--you know, with the modern phone and the laptop--that this would be something you have at your home, that you'd have a quantum computer, but in fact you probably never actually have a quantum computer in someone's house. They have to be run at such very low temperatures. Even though they are very small devices in terms of the entanglement, there's so much associated equipment that isn't likely to get too miniaturized. Most likely, you would always have class computers, and people access it through the cloud, and you'd just buy time--or get time--on a quantum computer that you will link up to.

The thing that we're most likely--for one individual person to use, would probably be something like encryption, but for stuff that we would actually get to see on our computer would probably be stuff like weather forecasting, for instance. It has a lot of options to allow us to do way better weather forecasting than we do now.

There are a lot of other examples in terms of the science; there are great things. It might finally let us model how the lifestyle of abiogenesis in the deep oceans, which is one of those examples where our models can't really be. We have approximation algorithms that we use to cover these really huge numbers, but they don't really seem to be up to snuff for covering things like those chemical interactions in the early deep oceans, and then those same algorithms, ironically enough, would be the kind of things we'd use for dating services in terms of finding the most optimal match for a person based on not just a simplified number of traits.

We have to simplify traits, normally. Here, we could actually have a thousand different traits with a thousand different subtypes, and a quantum computer could actually match up and optimize all of those. And then of course, there's the possibility of using election modeling.

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Here's when we can expect the next major leap in quantum computing - TechRepublic

Sometimes youd be the only person in the world with this new piece of knowledge. Its a pretty wild feeling

Professor David Reilly

Its been said that quantum computing will be like going from candlelight to electric light in the way it will transform how we live. Quite a picture, but what exactly is quantum computing?

For the answer to that question, well have to visit a scale of existence so small that the usual rules of physics are warped, stretched and broken, and there are few layperson terms to lean on. Strap yourself in.

Luckily, we have a world-leading researcher in quantum computing, Professor David Reilly, to guide us. Most modern technologies are largely based on electromagnetism and Newtonian mechanics, says Reilly in a meeting room at the Universitys Nano Hub. Quantum computing taps into an enormous new area of nano physics that we havent harnessed yet.

With his youthful looks and laid-back demeanour, Reilly isnt how you might picture a quantum physicist. He has five Fender guitars (with not much time to play them), and a weakness for single malt Scotches. That said, science has never been far below the surface. As a child, he would pull apart flashlights to see how they worked. During his PhD years, knowledge was more important than sleep; he often worked past 3am to finish experiments.

Sometimes youd be the only person in the world with this new piece of knowledge. Its a pretty wild feeling. A good place to start the quantum computing story is with the humble transistor, which is simply a switch that allows, blocks or varies the flow of electricity, or more correctly, electrons. Invented in 1947, it replaced the large, energy-hungry vacuum tubes in radios and amplifiers, also finding its way into computers.

This off/on gate effect of transistors is the origin of the zeroes and ones idea in traditional (aka classical) computers. Ever-shrinking transistors are also how computers have gone from room-filing monsters to tiny devices in our pockets currently, just one square millimetre of computer chip can hold 100 million transistors.

Incredible, yes, but also unsustainable. With transistors now operating at the size of atoms, they literally cant get much smaller, and theyre now at a scale where the different, nanoscale laws of physics are warping and compromising their usefulness. At that scale, an electron stops behaving like a ball being stopped by the transistor gate, Reilly says. Its more like a wave. It can actually tunnel through or teleport to the other side, so the on/off effect is lost.

Quantum computing seeks to solve this problem, but it also promises a great leap forward. Its based on the idea that transistors can be replaced by actual atomic particles where the zeros and ones arent predicated on the flow or non-flow of electrons, but on the property or energy state of the atomic particle itself.

These particles can come from various sources (and are usually engineered in nanoscale devices) but theyre called collectively, qubits. Now things get trickier. Yes, tricker. Where a transistor can be either one or zero, its a weird fact of quantum physics, that a qubit can be one or zero at the same time, like a spinning coin that holds the possibility of both heads and tails.

For a single qubit, this doubles the one-andzero mechanism. And for every qubit added, the one/zero combinations increase exponentially.

Continued here:

Quantum computing at the nanoscale - News - The University of Sydney

As the director of a global research organization, I feel obligated to use all the resources of cutting-edge science and technology at our disposal to fight this scourge. As a father, I want a lasting solution, one that serves not just in this crisis, but the next. And, as an American and a Spaniard, with family in two hot spots, I want to help. Its as simple as that.

It started with a phone call to the White House on Tuesday, March 17, one that proved to be a catalytic moment for industry, academia and government to act together. This was the same week I received news from my mother that my cousin in Spain had tested positive for coronavirus. Shes a doctor and, just like all medical staff around the world right now, is on the front lines of the fight against this disease. This fight is personal for so many of us.

COVID-19 is deadly serious. This respiratory disease is triggered by a virus from the family of coronaviruses, which was identified in the 1960s but had never made such an assault on humanity. The virus prevents its victims from breathing normally, making them gasp for air. Fever, cough, a sore throat and a feeling of overwhelming fatigue and helplessness follow. Lucky ones recover within a few days; some show only mild or moderately severe symptoms. But some patients are not that lucky. Bulldozing its way through the body, the virus makes the lungs fill up with fluid, and may lead to a rapid death. No one is immune. While the elderly and those with underlying health conditions are more at risk, COVID-19 has taken the lives of people of all ages, some in seemingly good health. The disease is bringing our world to its knees.

But we are resilient, and we are fighting back with all the tools we have, including some of the most sophisticated supercomputers we have ever built. These machinesmore than 25 U.S.-based supercomputers with more than 400 petaflops of computing powerare now available for free to scientists searching for a vaccine or treatment against the virus, through the COVID-19 High Performance Computing Consortium.

It was created with government, academia and industryincluding competitors, working side by side. IBM is co-leading the effort with the U.S. Department of Energy, which operates the National Laboratories of the United States. Google, Microsoft, Amazon and Hewlett Packard Enterprise have joined, as well as NASA, the National Science Foundation, Pittsburgh Supercomputing Center and six National LabsLawrence Livermore, Lawrence Berkeley, Argonne, Los Alamos, Oak Ridge and Sandia, and others. And then there are academic institutions, including MIT; Rensselaer Polytechnic Institute; the University of Texas, Austin; and the University of California, San Diego.

The supercomputers will run a myriad of calculations in epidemiology, bioinformatics and molecular modeling, in a bid to drastically cut the time of discovery of new molecules that could lead to a vaccine. Having received proposals from all over the world, we have already reviewed, approved and matched 15 projects to the right supercomputers. More will follow.

But just a few days ago none of this existed.

On March 17, I called Michael Kratsios, the U.S. governments chief technology officer. Embracing the potential of a supercomputing consortium, he immediately started mobilizing his team, including Jake Taylor, assistant director for quantum information science at the White House Office of Science and Technology Policy. Jake called major U.S. players that have high-performance computers and invited them on board. From the IBM side, Mike Rosenfield, whose team has designed and built multiple generations of world-leading supercomputers, partnered with RPI, MIT and the key computing leaders of the U.S. National Laboratories. The U.S. Department of Energy has been a partner from the very beginning, at the heart of it all.

Within 24 hours of that first call, collaborators outlined what it meant to be involved. We brainstormed how we would communicate to research labs worldwide what we could offer in terms of hardware, software and human experts, and how we would get them to submit proposals, and get those matched with just the right supercomputer.

Forty-eight hours passed. On Thursday, March 19, we set up the scientific review committee and the computing matching committee to manage proposals. At least one person from each of the members of the consortium had to be part of the process, all acting fairly and equally. From IBM, Ajay Royyuru joined the merit review committee; he is the leader of our Healthcare and Life Sciences research and together with his team has long been developing novel technologies to fight cancer and infectious diseases.

Ajay, too, has a personal stake in fighting back against COVID-19. In January, his elderly father passed away following a pulmonary illness. Ajay shares his house with his 82-year-old mother, and he worries about keeping her safe from this risk, just like so many of us worry about our parents. His extended family in India is now also confronting the unfolding of the pandemic.

On March 22, less than a week after the first discussion with Kratsios, the White House announced the consortium. Everyone knew that the clock was ticking.

It is still very early days, but Ajay and other reviewers can clearly see from the first wave of proposals that scientists are trying to attack the virus on all frontsfrom drug discovery and development with AI-led simulations to genomics, epidemiology and health systems response. We need to understand the whole life cycle of this virus, all the gearboxes that drive ithow it encounters and infects the host cell and replicates inside it, preventing it from producing vital particles. We need to know the molecular components, the proteins involved in the virus biochemistrythen to use computational modeling to see how we can interrupt the cycle. That's the standard scientific methodology of drug discovery, but we want to amplify it.

The virus has been exploding in humans for months now, providing an abundance of samples for computer modeling and analysis. Scientists are already depositing them into public data sources such as GenBank and Protein Data Bank. There are many unknowns and assumptionsbut, Ajay tells me, a lot of proposals involve using the available protein structures to try and come up with potential molecular compounds that could lead to a therapeutic or a vaccine.

Thats already happening. Even before we formed the consortium, researchers at Oak Ridge National Laboratory and the University of Tennessee simulated 8,000 compounds and found 77 molecules that could potentially disarm the virus. But 77 is still a big number and running tests to find the correct molecule may take months. Here, my colleague Alessandro Curioni, an Italian chemist who heads IBM Research Europe and who had to self-isolate due to possible exposure to COVID-19, had an idea on how to speed things up.

In a conversation with European Commission executives in early March, Alessandro learned about an Italian pharmaceutical company, Domp Farmaceutici and the E.U.-financed project they were working on. Last week, he orchestrated a meeting between its scientists and Oak Ridge, suggesting to both parties that they submit a joint proposal to the consortium. Perhaps together, with the help of supercomputers, they can reduce the number of the promising compounds from 77 to 10, five and, finally, one.

Humanity has more tools at its disposal in this pandemic than ever before. With data, supercomputers and artificial intelligence, and in the future, quantum computing, we will create an era of accelerated discovery. The consortium is an example of a unique partnership approach, and it shows that the bigger the challenge, the more we need each other.

Read more about the coronavirus outbreakhere.

Read this article:

Inside the Global Race to Fight COVID-19 Using the World's Fastest Supercomputers - Scientific American

Heading into this year, I was passionate about getting a very important message in front of investors.

It was my thesis that the next decade would not only be the most innovative in history, but that the stock market would experience its largest gains ever.

The start of 2020 as brutal as it has been reminds me of 1990. Stocks had already been in a bull market for eight years when the ball dropped on 1989 and ushered in the new decade. Most experts believed the bull market was over and that the 1990s would be a lost decade.

Boy, were they wrong.

The 1990s went on to become one of the greatest decades for stocks. Little technology companies youd never heard of became household names. Dell Computer now Dell Technologies (NYSE:DELL) gained over 91,000% during the 1990s. The biggest gains of the decade were driven by a plethora of new technologies centered around the internet and personal computing.

Today, we are now three months into the 2020s and stocks are in a similar situation. We entered the decade on the back of a 10-year bull market, only to slide quickly into a bear market.

Most people dont realize this, but the Dow did something similar in 1990. It fell by 19.5% essentially a bear market to start the decade before powering higher. I believe we will look back at the current sell-off and see it as the last great buying opportunity before a booming decade.

The selling is creating buying opportunities in investment trends that wont slow due to the pandemic. In fact, they could even accelerate due to the changing global landscape. Lets take a look at a few

Robotics:According to the editorial board atScience Robotics, robots could be an alternative to humans when it comes to certain healthcare tasks. Especially during a pandemic. Robots could collect lab results, automate lab tests, and help disinfect the patient rooms. In China, it was deemed too dangerous for humans to make critical deliveries of medicine and food into infected areas, so the country turned to robots.

Genomics:The future of healthcare will be based on advances in both technology and medicine. The latter will rely on the field of genomics. In the coming decade, it will be the norm to have your genome sequenced. This information will help create personalized medicine for each patient, as well as help speed up the creation of new drugs.

The response to the coronavirus is a great example of how medicine is evolving. In 10 years, this type of pandemic would not be an issue. The combination of artificial intelligence (AI), quantum computing, and genomics would create a vaccine within days.

Tele-everything:The biggest change to our lives throughout this ordeal has been the self-isolation. Either through a government mandate or by choice, most people around the globe are staying inside and not going about their normal activities. This has led to a work-from-home, school-from-home, and even workout-from-home situation.

The companies behind this trend are here to stay. When the pandemic is over, people will go back to work, school, and their local fitness facilities. But the tele-everything trend is not going away. In some circumstances, it is more convenient than what we were accustomed to. Everything from software companies to one of my favorite stocks,Teladoc (NYSE:TDOC), to online education will be pushed to grow faster and quicker after the pandemic.

Even though a lot of the stocks in these trends have been beaten down in the last month, the trends themselves are not dead. Nothing I mean nothing in life goes straight up. When it comes to the stock market, the strongest trends will hit speed bumps but those bumps offer investors opportunities.

Let me be straight with you. If you want to be wealthy, you must buy into high-growth, long-term trends. And to make the BIG money you need to buy when everyone else is selling.

That day is today.

Matthew McCall left Wall Street to actually help investors by getting them into the worlds biggest, most revolutionary trends BEFORE anyone else. The power of being first gave Matts readers the chance to bank +2,438% in Stamps.com (STMP), +1,523% in Ulta Beauty (ULTA) and +1,044% in Tesla (TSLA), just to name a few. Click here to see what Matt has up his sleeve now. Matt does not directly own the aforementioned securities.

Link:

3 High-Growth Trends to Invest In Now - Investorplace.com

Design engineers' jobs are starting to look a lot different than just a decade ago.

Most of us likely engage in mechanical engineering design on a daily basis, but whats the future for this crucial field?

Since the invention of CAD software, mechanical design has been revolutionized to its core. However, there are quite a lot of things about the process that are rather rudimentary. We still have to manually input constraints for parts that may seem obvious.

We can still make one minor mistake that can corrupt our whole model. Software is becoming smarter and smarter, but for the most part, the mechanical engineer is still where the innovation and the skill lies. What happens though, when programs become generative; when the mechanical engineers office dissolves and design moves into the future? Lets take a deeper look. (Dont worry, youre still going to have a job.)

CAD programs, the foundation of mechanical design, were largely pushed forward by innovative code and programming. That has done wonders for the programs abilities, but it also means that CAD has evolved into a largely keyboard-oriented skill. Given that this is commonplace, you may not find anything odd about this fact.

What keyboard-based mechanical design does, however, is limit the designer to technical ability and knowledge of the specific software. There will always be a place for this, but computers will soon be able to allow freeform mechanical design within the confines of reality. This means that while, as engineers, we may be smart enough to input design constraints, we simply wont have to. It opens up the age for pure engineering.

The echoing of this future reality has already been occurring. The age of touch screen computers has brought more natural mechanical design interface. Moving forward, it will likely be virtual reality and quantum computing that brings mechanical design into its ultimate realization for the engineer.

RELATED: TOP 3 JOBS FOR THE HIGHEST MECHANICAL ENGINEERING SALARY

When you think about mechanical design as more of a skilled work form given the tools coming in the future, mechanical engineers may soon have more options of where and how we work. We wont be restricted to cubicles, rather we can be technical artists designing in virtual spaces or even on the job site imagine that.

Any engineer actively engaged in any technical field today feasibly understands how significant simulation has become in the modern design process. This stretches twofold, from simulations improved capability to provide us with practically useful data and its increased use in the design process. Fully appreciating this modern design tool change requires that we look deeper into the state of simulation integrated CAD.

Diving deeper into simulation in the modern design process grants us a better look at what might be to come for our daily life as engineers.

Simulations in terms of computer models like FEA in relation to CAE is a fairly new capability. Simulation simply defined as the use of predictive or practical models to prepare and access future designs dates back a little further. We can trace the desire for simulation essentially back to the beginning of engineering, but its modern usage began during the world wars and the space age. More refined simulation models were used in the Manhattan Project to model nuclear explosions and the design of the rockets used in the early space missions. Of course, all of this simulation was done on paper and involved discrete mathematics, Navier-Stokes equations, and finite element analysis, among many other formulas.

Diverging from the mathematical roots of simulation, physical simulation was also used in the design process of the Apollo landing capsules. Astronauts alongside engineers were used to test processes for launch, landing, and usage of all of the Apollo hardware. These simplistic hardware simulations are the early beginnings of simulation tools that allow for usagecases and event analysis. During the height of the space age, Simula-67, the first simulation-centric programming language was developed which lead the way for modern computer simulation software.

Since these early days where the mathematics of simulation was refined, simulation solidified into a vital tool for engineers.

In the last several years, simulation has been ingrained into our CAE tools, like our mechanical design software. Beyond simple case analysis capabilities, simulation in many senses now comes before design. This shifted workflow comes in the form of generative design tools and simulations use as a design aid. Rather than designing a part and then testing whether it will work, CAD-integrated simulation software like Nastran and Inventors shape generator tools allow for simulation before or alongside design. Generative design allows for simulation to create a design whereas analysis tools allow for testing of part design every step of the way.

The increasing utilization of simulation in modern part design is only natural, in fact, its primal to our drive as engineers. We innately seek to improve, innovate, optimize, and otherwise endeavor to design the best part/assembly/machine possible. Simulation tools and the development therein leverage themselves on our innate desire toknow.

Even with the current state of CAD integrated simulation tools, there are still hurdles to overcome and improvements to be made. The NAFEMS World Congress, theInternational Association for the Engineering Modelling, Analysis, and Simulation Community,recently recognizedmany areas needing improvement in simulation tools in their 2017 assembly. They cited the most prolific problems of current simulation tools reuse of knowledge, speed and model fidelity, and pre-design simulation. In other words, the ways that NAFEMS believes simulation tools need to improve are their abilities to capture and reapply learned knowledge from past analysis, the speed and fidelity of models (which will naturally improve with cloud implementation/increased processing power) and the ability for simulation to be usedbeforethe design process.

So, while the modern usage of simulation tools alongside CAD has improved and grown to a point that has far exceeded manys expectations, theres a long way to go before it is perfect. This means good things for us as engineers. If we want more abilities to simulate, chances are they are coming with improved technical infrastructure. Cloud implementation is so vital to the adoption of simulation because simulation by nature requires significant processing power. Cloud offsets this burden from the engineer to the cloud data center, making expansive simulation analysis possible for anyone, anywhere.

The future of simulation is now, but the innovation wont be over anytime soon.

Ultimately, the goal of advancing mechanical design is to replace the restricting confines of computer interface and let then engineer create in a pure form.

Diverging from the mechanical design interface, the industries that are most in need of mechanical design in the coming future are those like automotive and manufacturing. However, theres is a new budding industry that will require the skills of the best mechanical designers AI design. Artificially intelligent programs and machines will soon be doing a large part of the design of the future. First, they have to learn and be complemented by actual mechanical design engineers.

RELATED: CONCEPTS MECHANICAL ENGINEERS NEED TO UNDERSTAND

Dont worry about losing your job to robots just yet. The specific tasks of a design engineer will only transform with AI, not be eliminated. What AI and ultimately, generative design, will do to mechanical design is revolutionize just what is possible in our industry. The future is bright for the mechanical designer.

Read more:

What Lies In the Future of Mechanical Design Industry - Interesting Engineering

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To most of us, Elon Musk is the real-life embodiment of Tony Stark. He started from nothing more or less and now is a leader in some aspects of the tech world. He started small, with an archive of newspapers and magazines, and in no time he was in Space. Space X that is.

Now, Elon has decided to notably withdraw from operating Tesla and SpaceX, and move to the next big chapter in his life: Quantum Computing, a venture that has seen investments over 2 billion dollars in just the prior to these years.

So what is quantum computing? In a nutshell, if you network all the PCs on the planet right now, and put them to work, the resulting power would not be sufficient to run the complex calculation that a quantum computer can permutate.

Elon never announced his move on Twitter, and that made us wonder. He left SpaceX and Tesla for this? It must be a scam! And it was. One that wants data and personal info.

On top of that if some untrusted sources are to be believed, the project is LIVE right now,beating companies like Microsoft and IBM to the punchand delivering QuantumAI. Or how Elon puts it: A new way to redistributing the worlds wealth.

The scammers strongly believe that the 1% that controls 90% of the worlds financial capital can share and help normal people to grow in wealth by using quantum computing. This theory has been thrown around even in the times ofMoore, and it now seems to be a reality for everybody on Earth.

This time the greedy scammers have raised the bar. The scam group used real footage of Elon Musk talking about his companies, but they overimposed another audio, making sure to turn people to the fake QuantumAI investment platform and automated trading app.

The group responsible for masterminding this charade are part of a bigger affiliate network, and they specialize in social media advertising like Facebook and Twitter. These networks are operating in cooperation with rogue offshore brokers who are paying referral money for investing clients. You are the investing client in this case!

And the rabbit hole goes deeper. When you sign in, you are signed up for your broker, in this case, Crypto Kartal owned by Elmond Enterprise Ltd. A company that is located in St. Vincent and the Grenadines as well as an office in Estonia where it is named Fukazawa Partnership OU. The QuantumAI scam is particularly shoddy because it practices an aggregate of two highly effective baiting systems: social media and video manipulation. And Facebook and Twitter are disseminating the message right now in some regions.

According to the scam, this iteration of QuantumAI hopes to make people 2 3 times wealthier, and no one, except the super-wealthy, will take a hit.

How do they do that? Well, the process is simpler than you can imagine. The wealthy keep their investments in bonds and stocks that they trade for a profit on the open market.

Here is the part where QuantumAI makes a power move that can affect the super-rich. The scam promises to beat Wall Street traders to the market, making winning trades before the brokers can react or intercept transactions. And with a quantum computer, you can do that! Well, as long as you have a working quantum computer that is!

Sounds super interesting right? But its a scam! All you need to do is do a Google back search of the pictures on the website and maybe, try to find out if the brokers invested in this enterprise had any scam or alerts in the past. Doubt anything, back search anything before you input any of your data, and on top of it all NEVER use your main email and password. Its safer to use a program or create a new one, just to be safe.

Be careful in these times. The Quantum AI Scam software, app, and fraudulent crypto trading platform by Elon Musk is completely blacklisted. But Facebook runs the adds with no remorse, the scammers switching between fake Guardian or CNN articles. So be aware!

See the article here:

1000 Words or So About The New QuantumAI Scam - TechTheLead

Yet that difference between the common things a company can sell and the uncommon things they quietly develop is profoundly important. In Devs, the friendly exterior of Amaya with its enormous statue of a childa literal monument to Forests lost daughteris a public face to the actual profound work his Devs team is doing in a separate, highly secretive facility. Seemingly based in part on mysterious research and development wings of tech giantsthink Googles moonshot organizations at X Development and DeepMindDevs is using quantum computing to change the world, all while keeping Forests Zen ambition as its shield.

I think it helps, actually, Garland says about Forest not being a genius. Because I think what happens is that these [CEO] guys present as a kind of front between what the company is doing and the rest of the world, including the kind of inspection that the rest of the world might want on the company if they knew what the company was doing. So our belief and enthusiasm in the leader stops us from looking too hard at what the people behind-the-scenes are doing. And from my point of view thats quite common.

A lifelong man of words, Garland describes himself as a writer with a laymans interest in science. Yet its fair to say he studies almost obsessively whatever field of science hes writing about, which now pertains to quantum computing. A still largely unexplored frontier in the tech world, quantum computing is the use of technology to apply quantum-mechanical phenomena to data a traditional computer could never process. Its still so unknown that Google AI and NASA published a paper only six months ago in which they claimed to have achieved quantum supremacy (the creation of a quantum device that can actually solve problems a classical computer cannot).

Whereas binary computers work with gates that are either a one or a zero, a quantum qubit [a basic unit of measurement] can deal with a one and a zero concurrently, and all points in between, says Garland. So you get a staggering amount of exponential power as you start to run those qubits in tandem with each other. What the filmmaker is especially fascinated by is using a quantum system to model another quantum system. That is to say using a quantum computer with true supremacy to solve other theoretical problems in quantum physics. If we use a binary way of doing that, youre essentially using a filing system to model something that is emphatically not binary.

So in Devs, quantum computing is a gateway into a hell of a trippy concept: a quantum computer so powerful that it can analyze the theoretical data of everything that has or will occur. In essence, Forest and his team are creating a time machine that can project through a probabilistic system how events happened in the past, will happen in the future, and are happening right now. It thus acts as an omnipotent surveillance system far beyond any neocons dreams.

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Devs: Alex Garland on Tech Company Cults, Quantum Computing, and Determinism - Den of Geek UK

In the Budget 2020 speech, Finance Minister Nirmala Sitharaman made a welcome announcement for Indian science over the next five years she proposed spending 8,000 crore (~ $1.2 billion) on a National Mission on Quantum Technologies and Applications. This promises to catapult India into the midst of the second quantum revolution, a major scientific effort that is being pursued by the United States, Europe, China and others. In this article we describe the scientific seeds of this mission, the promise of quantum technology and some critical constraints on its success that can be lifted with some imagination on the part of Indian scientific institutions and, crucially, some strategic support from Indian industry and philanthropy.

Quantum mechanics was developed in the early 20th century to describe nature in the small at the scale of atoms and elementary particles. For over a century it has provided the foundations of our understanding of the physical world, including the interaction of light and matter, and led to ubiquitous inventions such as lasers and semiconductor transistors. Despite a century of research, the quantum world still remains mysterious and far removed from our experiences based on everyday life. A second revolution is currently under way with the goal of putting our growing understanding of these mysteries to use by actually controlling nature and harnessing the benefits of the weird and wondrous properties of quantum mechanics. One of the most striking of these is the tremendous computing power of quantum computers, whose actual experimental realisation is one of the great challenges of our times. The announcement by Google, in October 2019, where they claimed to have demonstrated the so-called quantum supremacy, is one of the first steps towards this goal.

Besides computing, exploring the quantum world promises other dramatic applications including the creation of novel materials, enhanced metrology, secure communication, to name just a few. Some of these are already around the corner. For example, China recently demonstrated secure quantum communication links between terrestrial stations and satellites. And computer scientists are working towards deploying schemes for post-quantum cryptography clever schemes by which existing computers can keep communication secure even against quantum computers of the future. Beyond these applications, some of the deepest foundational questions in physics and computer science are being driven by quantum information science. This includes subjects such as quantum gravity and black holes.

Pursuing these challenges will require an unprecedented collaboration between physicists (both experimentalists and theorists), computer scientists, material scientists and engineers. On the experimental front, the challenge lies in harnessing the weird and wonderful properties of quantum superposition and entanglement in a highly controlled manner by building a system composed of carefully designed building blocks called quantum bits or qubits. These qubits tend to be very fragile and lose their quantumness if not controlled properly, and a careful choice of materials, design and engineering is required to get them to work. On the theoretical front lies the challenge of creating the algorithms and applications for quantum computers. These projects will also place new demands on classical control hardware as well as software platforms.

Globally, research in this area is about two decades old, but in India, serious experimental work has been under way for only about five years, and in a handful of locations. What are the constraints on Indian progress in this field? So far we have been plagued by a lack of sufficient resources, high quality manpower, timeliness and flexibility. The new announcement in the Budget would greatly help fix the resource problem but high quality manpower is in global demand. In a fast moving field like this, timeliness is everything delayed funding by even one year is an enormous hit.

A previous programme called Quantum Enabled Science and Technology has just been fully rolled out, more than two years after the call for proposals. Nevertheless, one has to laud the governments announcement of this new mission on a massive scale and on a par with similar programmes announced recently by the United States and Europe. This is indeed unprecedented, and for the most part it is now up to the government, its partner institutions and the scientific community to work out details of the mission and roll it out quickly.

But there are some limits that come from how the government must do business with public funds. Here, private funding, both via industry and philanthropy, can play an outsized role even with much smaller amounts. For example, unrestricted funds that can be used to attract and retain high quality manpower and to build international networks all at short notice can and will make an enormous difference to the success of this enterprise. This is the most effective way (as China and Singapore discovered) to catch up scientifically with the international community, while quickly creating a vibrant intellectual environment to help attract top researchers.

Further, connections with Indian industry from the start would also help quantum technologies become commercialised successfully, allowing Indian industry to benefit from the quantum revolution. We must encourage industrial houses and strategic philanthropists to take an interest and reach out to Indian institutions with an existing presence in this emerging field. As two of us can personally attest, the Tata Institute of Fundamental Research (TIFR), home to Indias first superconducting quantum computing lab, would be delighted to engage.

R. Vijayaraghavan is Associate Professor of Physics at the Tata Institute of Fundamental Research and leads its experimental quantum computing effort; Shivaji Sondhi is Professor of Physics at Princeton University and has briefed the PM-STIAC on the challenges of quantum science and technology development; Sandip Trivedi, a Theoretical Physicist, is Distinguished Professor and Director of the Tata Institute of Fundamental Research; Umesh Vazirani is Professor of Computer Science and Director, Berkeley Quantum Information and Computation Center and has briefed the PM-STIAC on the challenges of quantum science and technology development

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Picking up the quantum technology baton - The Hindu

The interdisciplinary team of researchers from UChicago, University of California, Berkeley, Princeton University and Argonne National Laboratory won the $2,500 first-place award for Best Paper. Their research examined how the VQE quantum algorithm could improve the ability of current and near-term quantum computers to solve highly complex problems, such as finding the ground state energy of a molecule, an important and computationally difficult chemical calculation the authors refer to as a killer app for quantum computing.

Quantum computers are expected to perform complex calculations in chemistry, cryptography and other fields that are prohibitively slow or even impossible for classical computers. A significant gap remains, however, between the capabilities of todays quantum computers and the algorithms proposed by computational theorists.

VQE can perform some pretty complicated chemical simulations in just 1,000 or even 10,000 operations, which is good, Gokhale says. The downside is that VQE requires millions, even tens of millions, of measurements, which is what our research seeks to correct by exploring the possibility of doing multiple measurements simultaneously.

Gokhale explains the research in this video.

With their approach, the authors reduced the computational cost of running the VQE algorithm by 7-12 times. When they validated the approach on one of IBMs cloud-service 20-qubit quantum computers, they also found lower error as compared to traditional methods of solving the problem. The authors have shared their Python and Qiskit code for generating circuits for simultaneous measurement, and have already received numerous citations in the months since the paper was published.

For more on the research and the IBM Q Best Paper Award, see the IBM Research Blog. Additional authors on the paper include Professor Fred Chong and PhD student Yongshan Ding of UChicago CS, Kaiwen Gui and Martin Suchara of the Pritzker School of Molecular Engineering at UChicago, Olivia Angiuli of University of California, Berkeley, and Teague Tomesh and Margaret Martonosi of Princeton University.

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Research by University of Chicago PhD Student and EPiQC Wins IBM Q Best Paper - Quantaneo, the Quantum Computing Source