Mediaboss Marketing

Oak Ridge National Laboratory Manufacturing Demonstration Facility. (Source: ORNL.)

Producing products quickly, efficiently and at low cost is a focal point for the manufacturing sector. There are a number of technologies that companies are using to achieve those goals including high performance computing (HPC) and supercomputing. The allure of cheap design and production optimization is strong, but a central barrier is the upfront costs of an enterprise owning an HPC or supercomputing machine.

Thats why in 2021, The U.S. Department of Energy began providing companies with HPC access via the High Performance Computing for Energy Innovation program. In addition to providing funding opportunities, the program allows companies to partner with national laboratories that have advanced computing resources up to 100 times more powerful than typical enterprise systems available for private sector use.

HPC and supercomputing technologies could usher in a manufacturing revolution marked by faster product development, improved product quality, reduced costs and increased efficiency.

Ideas sometimes work better in theory than in practice. Being able to visualize how a part or product will behave in the real world prior to the production process can save design teams a lot of headaches.

HPC and supercomputing technologies enable manufacturers to simulate and model products and production processes at a scale that was previously impossible. This allows manufacturers to design better products and optimize manufacturing processes. For example, in the automotive industry, manufacturers use HPC to simulate vehicle crashes and predict the behavior of materials under extreme conditions, which helps in designing safer and more durable vehicles.

A recent example is Tesla building its custom Dojo supercomputer to expand neural net training capacity using video data to advance computer vision technology to make self-driving vehicles safer.

In the aerospace industry, manufacturers use HPC to simulate how certain aircraft components will perform under varying conditions. Such simulations help manufacturers design more fuel efficient and reliable aircraft. For the semiconductor industry, HPC can help optimize performance through design simulation. Even complex systems such as cross-regional transportation networks can benefit from HPC simulation.

Design simulation is a primary area where HPC and supercomputers can help the manufacturing process. However, advanced simulation technologies have other uses during production and post-production.

Once products and systems are up and running, they need to be maintained. Simulation technologies can help pinpoint what maintenance needs to be performed in order to prevent equipment failures, which can be costly.

HPC and supercomputing simulations can help optimize the production process by helping manufacturers identify bottlenecks and efficiencies. For example, in the chemical industry, manufacturers use simulation and modeling technology to optimize the production process for chemicals such as polymers and plastics. This enables them to reduce the amount of raw materials and energy required to produce a given amount of product, resulting in significant cost savings. Similarly, advanced simulation technologies can help manufacturers like automakers simulate the performance of systems such as brakes under stressful, real-world-like conditions to correct any defects or deficiencies that the models identify.

In high-risk factory conditions, advanced simulations can also help train employees on equipment and tasks prior to their doing so in a real production environment. This can help reduce the risk of accidents while also enhancing worker productivity.

Getting products to market as fast as possible is a top concern for manufacturers. HPC and supercomputers can help companies stay ahead of the competition. For example, in the pharmaceutical industry, they can accelerate drug discovery by simulating the behavior of molecules and predicting their effectiveness at targeting diseases. This helps quicken the pace that drugs can move to clinical trials and ultimately enter the market.

Several studies and case studies demonstrate the benefits of using HPC and supercomputers to accelerate product development in manufacturing. A study conducted by the Council on Competitiveness found that the use of HPC and supercomputers in product design and development can reduce product development time and reduce the number of physical prototypes needed.

The U.S. Department of Energy's (DOE) High Performance Computing for Manufacturing program has funded several projects that demonstrate the benefits of using HPC and supercomputers in manufacturing. The Partnership for Advanced Computing in Europe (PRACE) has also funded several projects in the same vein.

Hewlett Packard Enterprise has made its HPE Cray portfolio available to the enterprise. The new HPE Cray EX and HPE Cray XD supercomputers speed up time-to-insight with massive performance and AI-at-scale benefits, delivered in a smaller data center footprint and at a lower price point. This allows manufacturers and other industries to harness insights, solve problems and innovate faster by delivering energy-efficient supercomputers in a smaller form factor and at a lower cost.

The simulation and modeling power of HPC and supercomputers helps reduce manufacturing costs by enabling the avoidance of errors during prototyping, reducing the time and resources needed for design and development and optimizing the supply chain.

The Council on Competitiveness found that using HPC and supercomputers can reduce design and development costs. By optimizing designs through simulation and modeling, manufacturers can avoid costly mistakes that may arise during physical prototyping and testing.

The Oak Ridge National Laboratory (ORNL) is helping manufacturers by developing innovative approaches to using its Spallation Neutron Source (SNS) supercomputer and the High Flux Isotope Reactor (HFIR) to allow researchers to examine microstructures to better design new materials and fabrication methods, and leverage multidisciplinary expertise for the development of new bio-based materials. These efforts are geared toward driving economic competitiveness, energy efficiency and productivity.

HPC and supercomputing systems are also being combined with robotics and automation to enhance manufacturing.

The technologies can analyze real-time data from sensors in factory environments so that robots can use the insights to adapt to changing conditions while maintaining accuracy and efficiency. The data analysis can also be used to optimize robotic systems for greater performance and efficiency. HPC and supercomputers can be used for virtual commissioning, allowing manufacturers to test and optimize robotic systems in a virtual environment before they are deployed in the real world. Supercomputers are also used to train and deploy machine learning models that can direct robots and autonomous systems to make more precise movements and decisions without human intervention.

A number of companies are using this approach, including GE, who has developed a software platform called Predix that combines HPC and supercomputers with the Internet of Things (IoT) to optimize the performance of its manufacturing equipment. This has helped to reduce downtime and improve overall efficiency. Siemens is using HPC and supercomputers to develop virtual commissioning tools such as the Tecnomatix Process Simulate Commissioning and Tecnomatix Plant Simulation Commissioning, which enable manufacturers to test and optimize robotic systems in a virtual environment.

The manufacturing sector is poised for a revolution driven by HPC, supercomputers and AI. Part of that will likely involve the advancement of quantum computing, which has applications for the manufacturing sector as well. Because quantum computers make simultaneous calculations versus the sequential calculations of classical machines, they could enable factory robots to move with greater efficiency and precision, driving better throughput for more complicated tasks. Quantum computers could also advance the creation of new materials for use as semiconductors, industrial production catalysts, electronic components, sustainable fuels, pharmaceuticals and consumer products. As these technologies continue to evolve, it is likely that we will see even more advanced and innovative applications in the manufacturing sector.

This story is one in a series underwritten by AMD and produced independently by the editors of engineering.com.Subscribe hereto receive informative infographics, handy fact sheets, technology recommendations and more in AMDs data center insights newsletter.

Continued here:
Revolutionizing Manufacturing with High-Performance Computing and Supercomputers - ENGINEERING.com

U.S. Rep. Ed Case speaks with UH students about cybersecurity internships with the Navy.

U.S. Rep. Ed Case visited with students from Kapiolani Community College and the University of Hawaii at Mnoa on May 3, to hear how cybersecurity internships with the Naval Information Warfare Center (NWIC) are helping to prepare them for jobs in areas of critical need.

U.S. officials said there are more than 30,000 jobs open nationwide in cybersecurity.

The students in the cybersecurity program at the various UH campuses are learning skills and gaining experience in areas that will prepare them for a career in this fast growing field. Some of the students presented their research, which ranged from data security for healthcare to using quantum computing to provide additional layers of protection.

Case said he was impressed by what the students were learning and had access to through such a program.

Im trying to make sure that people come out of my school here with the skills, and to find jobs and can stay home, the congressman said. Im looking at how we can help further these efforts.

Eric Inouye, a division head at NWIC, said that of the 400 employees at the center, about 175 are engineers, and 60 are computer scientists. He said about 75% to 80% have degrees from a UH campus.

One of the students who presented, Jericho Macabante, a junior from UH Mnoa, said the opportunity has provided a lot of experience in issues facing cybersecurity.

Ive had the chance to work on risk assessment, gaining technical knowledge and studying different areas that are part of cybersecurity, Macabante said. He said he looks forward to a career that will involve some aspects of his internship.

David Stevens, a faculty member from Kapiolani CCs Information Technology Program, created the annual NWIC internship in 2020, which has since expanded systemwide. On most UH campuses, the internship counts toward an IT students internship requirements for their degree/certificate.

As teachers, were always looking for ways to help students overcome the barriers they often face when transitioning from academia to professional life.The NIWC Cybersecurity Internship provides the skills and real-world experiences that help students launch a career, Stevens said.

Go here to see the original:
Cybersecurity students share research, internship experiences with ... - University of Hawaii

Shor says that the toy quantum computers we have today are not reliable enough to run his algorithm. It will take several conceptual breakthroughs and a huge engineering effort before we can scale quantum computers to the necessary 1mn qubits.

His best guess as to when this might happen? I would predict between 20 and 40 years, he says. But he does not rule out the possibility that the physics challenges will prove too hard and we will never build workable quantum computers. Shor, who has worked as a maths professor at MIT for 20 years, has also published poetry on quantum computing.quantum computing.Transcript

The best quantum computers today, produced in countries like China and at Google, can do on the order of 100 operations before failure, explains Steve Brierley, founder and chief executive of Riverlane a company building operating systems for quantum computers. To implement Shors algorithm you need something like a trillion quantum operations before failure.

But researchers are employing all kinds of ingenious techniques to overcome these challenges. Scientific breakthroughs dont always come on a predictable time. But were looking at years and not decades for this level of innovation, says Julie Love, product leader for quantum computing at Microsoft.

For several years, the US government has been planning for a quantum world and has been running competitions to find the most secure communication protocols of the future that would forestall the threat of Q-day. The US National Institute of Standards and Technology is in the process of approving new cryptography systems based on problems other than factorisation that are secure against both quantum and classical computers. Its really a race between quantum computers and the fix which is to stop using RSA, says Brierley.

But whatever new security protocols are finally approved, it will take years for governments, banks and internet companies to implement them. That is why many security experts argue every company with sensitive data should be preparing for Q-day today.

However, the obstacles to developing 1mn-qubit quantum computers remain daunting, with some private sector investors predicting a quantum winter as they lose faith in how quickly a quantum advantage can be achieved.

Even if private sector investment slows, the escalating geopolitical rivalry between the US and China will provide added impetus to develop the worlds first robust quantum computer. Neither Washington nor Beijing wants to come second in that particular race.

This story is free to read so you can share it with family and friends who dont have an FT subscription yet.

See the original post here:
Quantum computing could break the internet. This is how - Financial Times

Rarely are things just as they seem, and the world of quantum computing lends itself better than most to that description. Described as a fundamental shift in our processing capabilities, quantum computing's development has accelerated incredibly in the past few years. Yet according to a research paper published in the journal of the Association for Computing Machinery, relevant quantum computing (the one that's usually referred to as running circles around even the most powerful classical computers) still requires groundbreaking discoveries in a number of areas before it can dethrone a mere graphics card.

The most surprising element in the paper is the conclusion that a number of applications will remain better suited for classical computing (rather than quantum computing) for longer than previously thought. The researchers say this is true even for quantum systems running across more than a million physical qubits, whose performance the team simulated as part of their research.

Considering how today's top system, IBM's Osprey, still "only" packs in 433 qubits (with an IBM-promised 4,158-qubit system launch for 2025), the timescale towards a million qubits extends further ahead than expected.

The problem, say the researchers, is not with the applications or workloads themselves drug discovery, materials sciences, scheduling, and optimization problems in general are still very much in quantum computing's crosshairs. The issue is with the quantum computing systems themselves their architectures, and their current and future inability to intake the egregious amounts of data some of these applications require before a solution is even found. It's a simple I/O problem, not unlike the one we all knew from before NVMe SSDs became the norm, when HDDs bottlenecked CPUs and GPUs left and right: data can only be fed so quickly.

Yet how much data is sent, how fast it reaches its destination, and how long it takes to process are all elements of the same equation. In this case, the equation is for quantum advantage the moment where quantum computers offer performance that's beyond anything possible for classical systems. And it seems that in workloads that require the processing of large datasets, quantum computers will have to watch as GPUs such as Nvidia's A100 run by likely for a long, long while.

Quantum computing might have to make do with solving big compute problems on small data, while classical will have the unenviable task of processing the "big data" problems a hybrid approach to quantum computing that's been gaining ground for the last few years.

According to a blog post (opens in new tab) by Microsoft's Matthias Troyer, one of the researchers involved in the study, this means that workloads such as drug design and protein folding, as well as weather and climate prediction would be better suited for classical systems after all, while chemistry and material science perfectly fit the bill for the "big compute, small data" philosophy.

While this may feel like an ice bucket challenge flop for the hopes of quantum computing, Troyer was quick to emphasize that that isn't the case: "If quantum computers only benefited chemistry and material science, that would be enough. Many problems facing the world today boil down to chemistry and material science problems," he said. "Better and more efficient electric vehicles rely on finding better battery chemistries. More effective and targeted cancer drugs rely on computational biochemistry."

But there's another element to the researchers thesis, one that's harder to ignore: it seems that current quantum computing algorithms would be insufficient, by themselves, to guarantee the desired "quantum advantage" result. Rather than the systems engineering complexity of a quantum computer, here it's a simple performance problem: quantum algorithms in general just don't provide enough of an acceleration. Grover's algorithm, for instance, offers a quadratic speedup over classical algorithms; but according to the researchers, that's not nearly enough.

"These considerations help with separating hype from practicality in the search for quantum applications and can guide algorithmic developments," the paper reads. "Our analysis shows it is necessary for the community to focus on super-quadratic speeds, ideally exponential speedups, and one needs to carefully consider I/O bottlenecks."

So, yes, it's still a long road toward quantum computing. Yet the IBMs and Microsofts of the world will steadily carry on their research to enable it. Many of the issues facing quantum computing today are the same we faced in developing classical hardware the CPUs, GPUs, and architectures of today just had a much earlier and more impactful start. But they still had to undergo the same design and performance iterations as quantum computing eventually will, within its own brave new timeframe. The fact that the paper was penned by scientists with Microsoft, Amazon Web Services (AWS) and the Scalable Parallel Computing Laboratory in Zurich all parties with vested interests into the development and success of quantum computing just makes that goal all the more likely.

View post:
Single-GPU Systems Will Beat Quantum Computers for a While: Research - Tom's Hardware

How does quantum computing help the finance industry?

QCs are only in the developmental stage; experiments are already showing their great potential in the finance industry.

Based on the World Economic Forums estimate from 2022, national governments have invested more than $25 billion in quantum computing research, and over $1 billion in venture capital deals were closed in the previous year. Quantum computers (QCs) are in the early stages of development, and there are many technical challenges that need to be overcome before they can become practical tools for everyday use.

Nevertheless, they have already demonstrated great potential for applications in a wide range of fields. QCs have the ability to solve complex mathematical problems exponentially faster than classical computers, making them ideal for several complex tasks. The finance industry is one of the first runners in testing the technology. However, from the military to pharmaceuticals, logistics and manufacturing companies, several industries are experimenting with QC.

The mentioned features of QCs can have an enormous impact on the future of financial services. There are several tasks where financial forecasting and financial modeling can be supported by QCs for faster and more accurate processes. Notably, portfolio optimization, risk management and asset pricing are some of the most mentioned examples. However, their potential advantages and threats to cryptography make it important for financial service providers to monitor the technology.

Collaboration is crucial in the area of QCs due to the fact that technology and software development enable the revolution. Accelerating programs are initiated by the largest tech companies for experimentation with their hardware, software or cloud solutions, such as IBM, Microsoft, Google or Amazon.

Goldman Sachs has partnered with Microsoft Azure Quantum to explore the use of QCs for pricing. JPMorgan is experimenting with quantum solutions for optimization and risk management. HSBC announced its collaboration with IBM in 2022 to explore the use of QCs for pricing, portfolio optimization and risk mitigation.

QCs are new machines that can perform calculations much faster than classical computers, based on the principles of quantum mechanics.

The expression of QCs refers to a new type of machine based on the principles of quantum mechanics. Quantum mechanics is a division of physics that deals with the behavior of matter and light on the atomic and subatomic scales. The most valued property of QCs is that they perform certain types of calculations much faster than classical computers.

Classical computers store and process information in the unit of bits while QCs use quantum bits (or qubits). Bits represent information in a binary format and can have only two possible values: zero or one. Every piece of information going through a classical computer is essentially a long string of zeros and ones.

Qubits can exist in multiple states at once, a property known as superposition. This means that a single qubit can represent numerous possible combinations of zeros and ones; therefore, it can process a much larger amount of information than a classical bit.

Another exciting feature of qubits is the potential of entanglement, where qubit pairs are created. Modifying the state of one in the pair will change the state of the other qubit in a predictable way. This property gives extra power to QCs. Increasing the number of bits in a classical computer has a linear effect on the processing power, while adding an extra qubit to a quantum machine causes an exponential increase in the processing power.

Despite the great potential of QC, the technology and its applications need to overcome several challenging barriers.

Working with qubits is an enormously challenging scientific task because they need to be isolated in a controlled quantum state, which is extremely fragile. The smallest change in the physical environment (vibration or temperature) can cause an imbalance, which is the collapse of the superposition. Complex preventive actions are required, such as supercooled refrigerators, insulation or vacuum chambers to protect the system from losing its equilibrium.

Another aspect of the challenge is that as a different paradigm, QCs require not only completely new hardware and software but also algorithmic solutions. Numerous articles discuss the potential of QCs in machine learning, artificial intelligence or cryptography. Less often emphasized that it does not only mean using QCs to run algorithms designed for classical computers (quantum-enhanced) but building completely new algorithms, which are leveraging the features of QCs.

QCs in banking can be a game changer due to the potential of multiplying the speed and volume of calculations and transactions. However, different financial institutions only started to experiment with their own quantum algorithms and the limits of those potentials are not clear yet. Quantum algorithms are algorithms that take advantage of the unique properties of quantum systems, such as superposition and entanglement.

One example of quantum algorithms is Grovers algorithm, which can be used to search large, unstructured databases of financial data more quickly than classical algorithms. For example, it could be used to search for specific financial transactions or to identify patterns in financial data. Another example is Shors algorithm, which enables one to factor in large numbers more quickly than classical algorithms.

The finance industry is optimistic about quantum computing. Tasks such as portfolio optimization, risk management and asset pricing have a great chance to be beneficiaries.

Grovers and Shors algorithms can be applied to portfolio optimization. Portfolio optimization involves finding the optimal combination of investments to maximize returns while minimizing risk. Besides providing faster and more accurate calculations the technology can enable more flexible optimization strategies that take into account a wider range of factors, such as environmental, social and governance factors.

Another example could be asset pricing. Asset pricing is the process of estimating the value of financial assets such as stocks, bonds and derivatives. Traditional methods for pricing financial assets rely on complex mathematical models, such as Monte Carlo simulations, which involve simulating a large number of possible outcomes for a given financial asset and then using these simulations to estimate its value. Quantum Monte Carlo (QMC) can handle, for example, complex financial instruments, such as options, that have non-linear payoffs.

Heres the billion-dollar question: Can quantum computers predict the stock market? While QCs may have some advantages over classical computers in certain financial modeling tasks, it is unlikely that they will be able to predict the stock market with complete accuracy. Additionally, as with any new technology, quantum computing also poses its own unique challenges and limitations that need to be addressed before its full potential in financial applications can be realized.

Many financial services companies have high expectations of QCs effect on risk management. It involves identifying, assessing, prioritizing risks and taking actions to mitigate or manage those risks. Every step involves mathematical modeling and simulations for predicting risk outcomes, and time and accuracy play a crucial role in the process. Cybersecurity is an important part of risk management that can be enhanced by enabling more advanced encryption methods.

Encryption became a crucial measure in the banking industry that protects sensitive information from unauthorized access. It is used to secure communication channels between banking systems, websites and mobile apps and protect data on servers, databases and backups. Additionally, encryption is used to generate digital signatures that help ensure the authenticity of documents and prevent unauthorized modification or tampering of sensitive documents.

Cryptography and blockchain technology will surely not stay untouched by quantum computing; however, the direction remains a question.

Quantum computing presents both a threat and an opportunity for cryptography. While it has the potential to break many of the current encryption methods, it also has the potential to create new and more secure methods that are immune to attacks by classical computers.

QCs are exponentially faster than classical computers, which means they can quickly solve mathematical problems that classical computers would take years, decades or even centuries to solve. This includes the mathematical problems that underlie many of the encryption schemes used to secure digital communication and transactions.

For example, Shors algorithm can be used to efficiently factor large numbers, which is the basis for many public-key encryption algorithms such as RSA (the abbreviation refers to the name of the creators, RivestShamirAdleman).

However, quantum cryptography can also be used to create new cryptographic methods that are securer than classical methods. For example, quantum key distribution is a method to generate and distribute a secret key between two parties, the confidentiality and integrity of the information being exchanged can be ensured, even if a malicious entity intercepts the communication.

The mentioned features create some uncertainty in the future of QCs in blockchain technologies. It has the potential to break current encryption methods used in blockchain, which could compromise the security of digital assets and transactions. At the same time, researchers are working on developing quantum-resistant encryption methods for blockchains to counter this threat, such as CRYSTALS-Kyber public-key encryption by IBM. Additionally, QCs can enhance blockchains by increasing their processing speed and scalability, which can lead to more efficient and secure transactions.

Link:
How does quantum computing impact the finance industry? - Cointelegraph