Cloud Hosting

Read more: the future of CPUs

Computers are built around logic: performing mathematical operations using circuits. Logic is built around things such as Addersnot the snake; the basic circuit that adds together two numbers. This is as true of today's microprocessors as all those going back to the very beginning of computing history. You could go back to an abacus and find that, at some fundamental level, it does the same thing as your shiny gaming PC. It's just much, much less capable.

Nowadays, processors can do a lot of mathematical calculations using any number of complex circuits in a single clock. And a lot more than just add two numbers together, too. But to get to your shiny new gaming CPU, there has been a process of iterating on the classical computers that came before, going back centuries.

As you might imagine, building something entirely different to that is a little, uh, tricky, but that's what some are striving to do, with technologies like quantum and neuromorphic computingtwo distinct concepts that could change computing for good.

"Quantum computing is a technology that, at least by name, we have become very accustomed to hearing about and is always mentioned as 'the future of computing'," says Carlos Andrs Trasvia Moreno, software engineering coordinator at CETYS Ensenada.

Quantum computers utilise qubits, or quantum bits. Unlike a classical bit, which can only exist in one of two states, these qubits can exist in two states and a superposition of those two states. It's zero, one, or both zero and one at the same time. And if that sounds awfully confusing, that's because it is, but it also has immense potential.

Quantum computers are expected to be powerful enough to break modern-day 'unbreakable' encryption, accelerate medicine discover, re-shape how the global economy transports goods, explore the stars, and pretty much revolutionise anything involving massive number crunching.

The problem is, quantum computers are immensely difficult to make, and maybe even more difficult to run.

Social Links Navigation

"One of the main drawbacks of quantum computing is its high-power consumption, since it works with algorithms of far greater complexity than that of any current CPU," Moreno continues. "Also, it requires an environment of near absolute zero temperatures, which worsens the power requirements of the system. Lastly, they are extremely sensitive to environmental disturbances such as heat, light and vibrations.

We're scratching the surface there with quantum computing.

"Any of these can alter the current quantum states and produce unexpected outcomes."

And while you can sort of copy the function of classical logic with qubitswe're not starting entirely at zero in developing these machinesto exploit a quantum computer's power requires new and complex quantum algorithms that we're only just getting to grips with.

IBM is one company investing heavily in quantum computing, aiming to create a quantum computer with 4,158 or more qubits by 2025. Google also has its fingers in quantum.

Admittedly, we're still a long way off ubiquitous 'quantum supremacy', which is the moment when a quantum computer is better than today's top classical supercomputers. Google did claim it did just that back in 2019, though that may have turned out to be something of a niche achievement, but nonetheless an impressive one. Either way, in practical terms, we're just not there yet.

They're a real pain to figure out, to put it scientifically. But that's never stopped a good engineer yet.

"I do think that we're scratching the surface there with quantum computing. And again, just like we broke the laws of physics with silicon over and over and over again, I think we break the laws of physics here, too," Marcus Kennedy, general manager of gaming at Intel, tells me.

Marcus Kennedy

There's more immediate potential for the future of computing in artificial intelligence, your favourite 2023 buzzword. But it really is a massive and life-changing development for many, and I'm not just talking about that clever-sounding, slightly-too-argumentative chatbot in your browser. We're only scratching the surface of AI's uses today, and to unlock those deeper, more impactful uses there's a whole new type of chip in the works.

"Neuromorphic computing is, in my mind, the most viable alternative [to classical computing]," Moreno says.

"In a sense, we could say that neuromorphic computers are biological neural networks implemented on hardware. One would think it's simply translating a perceptron to voltages and gates, but it's actually a closer imitation on how brains work, on how actual neurons communicate amongst each other through synapsis."

What is neuromorphic computing? The answers in the name, neuro, meaning related to the nervous system. A neuromorphic computer aims to imitate the greatest computer, and most complex creation, ever known to man: the brain.

"I think we'll get to a place where the processing capability of those neuromorphic chips far outstrips the processing capability of a monolithic die based on an x86 architecture, a traditional kind of architecture. Because the way the brain operates, we know it has the capacity and the capability that far outstrips anything else," Kennedy says.

"The most effective kind of systems tend to look very much like things that you see in nature."

Neuromorphic chips are yet to reach their breakthrough moment, but they're coming. Intel has a couple of neuromorphic chips in development today, Loihi and Loihi 2.

And what is a neuromorphic chip, really? Well, it's a brain, with neurons and synapses. But since they're still crafted from silicon, think of them as a sort of hybrid of a classical computer chip and the biology of the brain.

And not necessarily a big brainLoihi 2 has 1 million neurons and 120 million synapses, which is many orders of magnitude smaller than a human brain with roughly 86 billion neurons and trillions of synapses. It's hard to count them all, as you might imagine, so we don't really know precisely, but we have big ol' brains. You can brag about that all you want to your smaller-brained animal companions.

A cockroach is estimated to have as many synapses as Loihi 2, for a better understanding of the grey matter scale we're talking about here.

"We claim you don't need to be that complex that the brain has its function, but if you're going to do computing, you just need some of the basic functions of a neuron and synapse to actually make it work," Dr. Mark Dean told me in 2021.

Dr. Mark Dean

Neuromorphic computing has a lot of room to grow, and with a rapidly growing interest in AI, this nascent technology may prove to be the key to powering those ever-more-impressive AI models you keep reading about.

The amount of processing power would surpass any of the existing products with just a fraction of the energy.

You might think that AI models are running just fine today, which is primarily thanks to Nvidia's graphics cards running the show. But the reason neuromorphic computing is so tantalising to some is "that it can heavily reduce the power consumption of a processor, whilst still managing the same computational capabilities of modern chips," Moreno says.

"In comparison, the human brain is capable of hundreds of teraflops of processing power with only 20 watts of energy consumption, whilst a modest graphics card can output 40-50 teraflops of power with an energy consumption of 450 watts."

Basically, "If a neuromorphic processor were to be developed and implemented in a GPU, the amount of processing power would surpass any of the existing products with just a fraction of the energy."

Sound appealing? Yeah, of course it does. Lower energy consumption isn't only massive for the potential computing power it could bring about, it's massive for using less energy, which has knock-on effects for cooling, too.

"Changing the architecture of computing would also require a different programming paradigm to be implemented, which in its own will also be an impressive feat," Moreno continues.

Building a neuromorphic chip is one thing, programming for it is something else. That's one reason why Intel's neuromorphic computing framework is open-source, you need a lot of hands on deck to get this sort of project off the ground.

"The thing that we haven't cracked yet is the software behind how to leverage the structure," Kennedy says. "And so you can create a chip that looks very much like a brain, the software is really what makes it function like a brain. And to date, we haven't cracked that nut."

It'll take some time before we entirely replace AI accelerators with something that resembles a brain. Or Adders and binary functions, that are as old as computing itself, with quantum computers. Yet experiential attempts have already begun to replace classical computing as we know it.

A recent breakthrough claimed by Microsoft sees the company very bullish on quantum's future, and there's also recently been IBM predicting quantum computers will outperform classical ones in important tasks within two years.

In the words of Intel's Kennedy, "I think we're getting there."

Excerpt from:
We're on the brink of the biggest changes to computing's DNA and it's not just quantum that's coming - PC Gamer

Read More..

TROY, N.Y. (NEWS10) The quantum craze landing at RPIs campus, but while theyre not shooting the next Marvel movie, they are making pretty cool moves in the realm of computer sciences.

RPI announces its Alan M. Voorhees Computing Center will soon be the home of IBMs Quantum System Onea quantum computer and the first to be in any university around the world.

Its obviously much faster in terms of computing power, but its not just we made it 10 times faster and you can use the same codes. Its we made it 1000 times faster and you have to write a whole bunch of new codes, explains John Kolb, the vice president of information services at Rensselaer Polytechnic Institute and its chief information officer.

Kolb tries to make it simple for NEWS10s Mikhaela Singleton during a tour Wednesday in order to explain just how revolutionary this technology is. He says a quantum computer can solve every problem too complex for your desktop that can only process straightforward problems answered by 1s and 0s.

He says this technology can make millions of probabilities based on a wealth of data. For instance, he predicts it will fit well with RPIs exiting AI research.

Weve been very heavily a leader in artificial intelligence and machine learning, and these systems will help some of the learning processes, he says.

Its not just the next generation of machines this quantum computer will be able to advance. Kolb says this can also help move medical research leaps and bounds.

He says RPI last year launched a precision medicine center in partnership with Mount Sinai focusing on biomedical engineering research. In order to make predictions on diseases, drug side effects, and any number of factors that can change a persons health, he says the quantum computer could go beyond simple family medical history by examining outcomes from many people.

If you have a sample of two or three, doesnt quite work. If you have a sample of eight million people in New York City, well now youre starting to get somewhere, but how do you crunch that in a way that you actually get answers that then come back to you and say well heres the medicine that you need, Kolb explains.

IBM will help build and assemble the System One with a completion date around January 2024. Kolb foresees every RPI student taking future courses on quantum computing, poising the Capital Region to rival Silicon Valley and other hubs of technology and talent.

The question becomes how do we create the next generation of applications and train the workforce and help educate that workforce for what those applications will do. We are not only getting the advantage of a system that IBM made, we are also developing the leaders that will develop the next generation of systems for IBM and others, he says.

This is a huge deal for our economy. This will this is something thats gonna carry us for decades, Kolb goes on to say.

The rest is here:
RPI to become only university worldwide with IBM quantum computer - NEWS10 ABC

Read More..

The future of energy distribution lies in the hands of advanced technologies, and quantum computing is emerging as a key player in the quest for grid optimization. As the world continues to grapple with the challenges of climate change and the need for sustainable energy solutions, the race is on to develop a smarter, more efficient power grid. Quantum computing, with its ability to process vast amounts of data and solve complex problems at unprecedented speeds, has the potential to revolutionize the way we manage and distribute energy.

One of the primary challenges facing the energy sector today is the integration of renewable energy sources into the existing power grid. Solar and wind power, while essential for reducing greenhouse gas emissions, are inherently variable and unpredictable. This makes it difficult for grid operators to balance supply and demand, resulting in wasted energy and increased costs for consumers. Traditional computing methods have struggled to keep up with the complexity of these problems, but quantum computing offers a promising solution.

Quantum computers operate on the principles of quantum mechanics, allowing them to process information in a fundamentally different way than classical computers. While classical computers use bits to represent information as either a 0 or a 1, quantum computers use quantum bits, or qubits, which can represent both 0 and 1 simultaneously. This enables quantum computers to perform multiple calculations at once, drastically increasing their processing power and making them uniquely suited to tackle complex optimization problems.

In the context of grid optimization, quantum computing can be used to analyze vast amounts of data from various energy sources and predict fluctuations in supply and demand. This information can then be used to make real-time adjustments to the grid, ensuring that energy is distributed efficiently and sustainably. For example, if a quantum computer predicts a sudden increase in wind power, it can direct excess energy to storage facilities or redirect it to areas with higher demand. This level of precision and adaptability is simply not possible with traditional computing methods.

Several companies and research institutions are already exploring the potential of quantum computing in the energy sector. IBM, for instance, has partnered with Daimler to develop quantum algorithms for battery research, aiming to improve the performance and lifespan of electric vehicle batteries. Meanwhile, researchers at the University of Southern California are using quantum computing to optimize the placement of electric vehicle charging stations, ensuring that they are accessible to as many drivers as possible while minimizing the impact on the power grid.

The benefits of quantum computing for grid optimization extend beyond the integration of renewable energy sources. As our power grid becomes increasingly interconnected and complex, the risk of cyberattacks and other security threats grows. Quantum computing can be used to identify vulnerabilities in the grid and develop more robust security measures, helping to protect our critical infrastructure from potential disruptions.

Despite its immense potential, quantum computing is still in its infancy, and widespread adoption remains years away. However, as research and development continue to advance, it is becoming increasingly clear that quantum computing will play a crucial role in shaping the grid of tomorrow. By harnessing the power of quantum mechanics, we can create a smarter, more efficient, and more sustainable energy system, paving the way for a greener future.

Follow this link:
Powering the Grid of Tomorrow: Quantum Computing and Grid ... - EnergyPortal.eu

Read More..

Scheme for Generator-Enhanced Optimization (GEO) strategy. (Source: Zapata)

The partnership yielded groundbreaking research in using quantum-inspired generative AI techniques to optimize vehicle production. This blend of AI and quantum computing could mark the dawn of a new era in automotive manufacturing, highlighting the potential of quantum technologies to revamp complex industrial processes.

The collaboration addressed one of the most crucial challenges in the automotive industryoptimizing vehicle production schedules across multiple manufacturing plants. At its core, this challenge is a convoluted puzzle with countless configurations and constraints, ranging from fluctuating production rates, discrete shift schedules to the prevention of buffer overflows and shortages during manufacturing processes.

Zapata Computing's Generator-Enhanced Optimization (GEO) technique was put to the test, conducting approximately one million optimization runs in a series of simulations. These trials were run on Zapatas Orquestra software platform, comparing the efficiency and performance of GEO against various algorithms and problem configurations. The studys results demonstrated that GEO outperformed even state-of-the-art solvers in minimizing assembly line idle time while meeting monthly vehicle production targets.

We ran roughly a million optimization runs cycling through dozens of various algorithms, problem configurations and optimizer solutions to benchmark their performance against each other, said Yudong Cao, CTO and co-founder at Zapata Computing. GEO uses quantum or quantum-inspired generative machine learning models to learn from and improve upon the results generated by classical solvers.

The endeavor not only provided BMW with potential production solutions but also served as a salient example of quantum computings application in tackling real-world industrial problems. Dr. William D. Oliver, Director of The Center for Quantum Engineering, hailed the project as a prime example of the center's mission to link academic rigor with industry partners to solve practical challenges.

The GEO technique leverages quantum-inspired generative machine learning models, which draw from principles of quantum mechanics like entanglement and superposition but dont require use of a quantum computer. The technique is used to learn from and enhance the outcomes produced by classical solvers. GEO shows how quantum technologies can be harnessed to enhance computational outcomes despite quantum computers still being in the early stages of R&D.

For BMW, the application of quantum-inspired generative AI techniques is a promising stride towards revolutionizing their manufacturing operations. The successful benchmarking of GEO against other production planning techniques aligns with BMW's drive for innovation and operational efficiency.

The significance of this research extends beyond the automotive industry. As we inch closer to a quantum future, collaborations like these lay the groundwork for broader applications of quantum technologies in fields like medicine, materials science, financial forecasting, energy, and climate science.

The study could also help enhance the efficiency of production lines using quantum-inspired techniques. Cao explained that the optimization challenge lies in finding the best approach under conflicting constraints in complex production settings.

When running a production line, you want to decide what the production rate should be, how fast each station should go, said Cao, But in reality, workers cannot go forever, they have their shifts, they have their breaks. You need to arrange these steps in a way that minimizes inventory and space. Thats part of the optimization problem.

Traditional methods for solving such problemssuch as linear arrangementshave limitations, especially when there are multiple variables at play. This optimization problem is like a difficult math problem where all possible solutions must be exhaustively explored, which is often not feasible in a large-scale business context. One caveat to some current aspects of the optimization problem is the rising deployment of robotics in manufacturing, which could yield more predictability compared to human workers.

Different techniques have been explored to solve these complex optimization problems. "In this project with BMW, we actually used quite a few techniquessimulated annealing, genetic algorithms, Monte Carlo algorithms, etc, said Cao. But what we showed in this project, is that you can boost the performance of these optimizers even further with AI. Thats the gist of what GEO is aboutusing AI combined with what people have come up with so far for solving optimization problems.

One of the studys goals was to minimize inventory. "We benchmarked GEO against classical algorithms for many variants of the problem, said Cao. We found that in 71 percent of cases, GEO either ties or outperforms state-of-the-art solvers.

Cao also indicated that a quantum circuit could easily replace a standard one, explaining, "The way the GEO algorithm is set up, we can pretty much just swap out the tensor network construction and swap in the quantum circuit."

Cao noted that further benchmarking is needed to scale up the problem-solving capacity, by considering infrastructure aspects such as data ingestion and the frequency of algorithm execution. Additionally, he mentioned the potential of switching to a quantum solution, "just by swapping out the tensor networks with quantum circuits," which would involve changing just one line of code.

The role of Orchestra has been critical in facilitating benchmarking, having run millions of instances of the algorithm. The next step in the study is to run more benchmarks on larger problems, with a goal to use this technology for production use cases.

Cao elaborated on the ongoing work to use quantum computing to solve complex optimization problems. Zapata researchers introduced a "synergistic framework" where "an algorithm acts like a relay race between a classical computer and a quantum computer." The classical computer is utilized to solve as much of the problem as possible before handing over the more complex elements to the quantum computer.

Their theory outlines how tensor networks, a quantum-inspired data structure, can be mapped to a quantum device for use on a specific application.

The quantum-inspired approach is a lot easier to work with because you can just run it on CPUs and GPUs, its just linear algebra under the hood, said Cao. We don't have to submit a job, pick a hardware backend, look at the parameters. When using a quantum device there are extra steps that need to be taken in order to know that what youre trying to do on the device is what the device is actually doing.

However, Cao anticipated that using quantum computers could potentially lead to performance gains and efficiency, noting that quantum computers might be able to discover solutions that would take longer using a classical computer. At the same time, more research will be needed to define what specific problems are amenable to faster solving with quantum computers.

In terms of the potential industry impact, the GEO technique isnt limited to automotive. Cao underscored that this hybrid scheme of classical and quantum-inspired techniques could tackle "the same kinds of operations research problems that people in Fortune 100 companies have been grappling with for decades." The approach, as demonstrated in the BMW case study, allows for quantum technologies to enter the arena of complex operational challenges, acting as a competitor against other commercial solvers.

Link:
BMW, Zapata and MIT Test Quantum-Inspired Generative AI in Production - ENGINEERING.com

Read More..