Category Archives: Quantum Computing

The future is quantum: universities look to train engineers for an emerging industry –

IBM physicist Olivia Lanes says quantum tech needs workers from various educational levels.Credit: IBM

The first year of university is always an opportunity to explore, but William Papantoniou really took the plunge. From the start of his studies in 2021 at the University of New South Wales (UNSW) in Sydney, Australia, he signed up for the universitys latest offering: an undergraduate degree in quantum engineering.

Now a third-year student, Papantoniou chose the programme because he wanted to learn more about quantum computers and the physics that makes them run. He first heard of the devices in a programming class during secondary school. It was presented as the future of computing, he says. They described how quantum computing makes complex problems simpler.

The programme prepares students to enter the emerging quantum-technology industry, which has begun to develop devices that use individual atoms, electrons, photons and other components exhibiting quantum properties. These distinctive properties allow quantum computers to execute types of algorithm that are not easily accessed by conventional computers.

Quantum technology includes magnetic sensors and atomic clocks, as well as quantum computers, the development of which some specialists project will take at least a decade to be commercially useful. Proponents tout these devices as a technological paradigm shift, in which quantum mechanics enables extremely precise measurements and a fresh way for computers to crunch numbers.

William Papantoniou explores quantum devices in a practical class as part of his degree.Credit: William Papantoniou/The UNSW Quantum Engineering Society

Many industries are betting that they will benefit from the anticipated quantum-computing revolution. Pharmaceutical companies and electric-vehicle manufacturers have begun to explore the use of quantum computers in chemistry simulations for drug discovery or battery development. Compared with state-of-the-art supercomputers, quantum computers are thought to more efficiently and accurately simulate molecules, which are inherently quantum mechanical in nature.

From software developers to biologists and chemists, users are now investigating whether quantum technology can bolster their fields. But there is still lively debate about how the technology will pan out, says physicist Olivia Lanes, a researcher at IBM in Yorktown Heights, New York. A lot of people dont want to enter the industry until they see the technology is robust, but can we make it robust without them?

The UNSWs undergraduate degree begins to fill a void in quantum education outside PhD programmes. The study of quantum mechanics has fallen largely under basic research since its discovery in the early twentieth century, falling in the purview of graduate studies. When quantum technologies began to be commercialized in the 2010s, the industry predominantly hired researchers with physics PhDs.

But in the past decade, governments including those in Australia, the United States, the United Kingdom, China and the European Union have collectively pledged billions of dollars to develop the quantum-technology industry. Thats aside from the commercial investment by technology companies such as Google, Microsoft, IBM and smaller start-ups. As the industry grows, experts have already started to bemoan a lack of qualified job candidates, and the shortfall looks likely to expand.

Andrea Morello instructs students in the quantum-engineering teaching laboratory at the University of New South Wales in Sydney, Australia.Credit: UNSW Sydney

For example, one estimate suggests that Australias quantum-technology industry could provide 19,400 jobs by 2045 (see, yet a 2016 survey tallied only about 5,000 PhD physicists in the entire country (see With a physics graduate degree often taking five years or longer, we simply cannot produce PhDs fast enough to satisfy the needs of this booming industry, says physicist Andrea Morello, who helped to start the UNSWs undergraduate programme in quantum engineering. Instead, the industry will predominantly need engineers with undergraduate training in relevant quantum topics, such as how the hardware components work and how to write relevant software. The evolution of the quantum industry parallels that of the computer-science industry over the past 50 years. Jobs in computing in the United States grew by more than tenfold between 1970 and 2014, according to the US Census Bureau. In the early 1970s, many universities established and expanded their computer-science undergraduate programmes in anticipation.

The quantum-tech industry will need workers with various educational backgrounds to benefit society. A technology cant succeed if the only people who know how to use it are PhDs, says Lanes.

In response to this demand, some universities are starting quantum-training programmes at both the bachelors and masters levels. In 2019, Saarland University in Saarbrcken, Germany, introduced an undergraduate quantum-engineering degree similar to the UNSWs and launched a masters programme a year later. Bachelors students at Virginia Tech in Blacksburg can opt for quantum and information science as a secondary specialization, which was introduced in 2022. Pretty much every week, Ill learn about a new programme somewhere, says quantum experimentalist Abraham Asfaw, who leads education and outreach efforts for Googles quantum team in Santa Barbara, California.

Google quantum experimentalist Abraham Asfaw works on a dilution refrigerator in his laboratory in Santa Barbara, California.Credit: Erik Lucero, Google Quantum AI

Undergraduate degree programmes aim to train engineers who work directly with quantum devices and require a relatively deep understanding of quantum mechanics. The industry also needs engineers to work with conventional technology, such as the cryogenics systems that keep quantum computers cold enough to operate, or the optical fibres that link multiple quantum devices. These engineers could perhaps learn the necessary quantum mechanics in an undergraduate course or two that are incorporated into a conventional engineering degree or vocational programme, says Asfaw.

Morello and his colleagues built the UNSWs quantum-engineering programme on the framework of a conventional electrical-engineering degree. Students take largely the same course as do non-quantum engineers, but with extra, quantum-specific classes. Morello says they designed the programme so that its graduates could still choose to work as conventional electrical engineers. Its really important to choose a degree that gives you a solid basis while providing you options, says Morello.

UNSWs quantum courses originate from masters classes that Morello and his colleagues deliver. These have required academics to rethink how they teach quantum mechanics. The conventional approach comes from a theoretical physics perspective, which centres on understanding the behaviour of idealized quantum objects, such as a single confined particle. In traditional quantum mechanics courses, you [might] spend a day talking about applications, but its not the focus of the course, says physicist Lex Kemper, who is developing an undergraduate quantum engineering course at North Carolina State University in Raleigh.

How to get started in quantum computing

For example, undergraduate physics students typically learn about quantized energy levels, in which quantum objects can lose or gain energy only in discrete amounts, or quanta, through physicist Niels Bohrs quantum model of hydrogen, the simplest atom. Bohrs model depicts hydrogen as a positively charged nucleus with orbiting negatively charged electrons, and the atom can lose or gain a quantum of energy by emitting or absorbing a photon, a particle of light. Instead, Morello uses a real-world example in his teaching a material called a quantum dot, which is used in some LEDs and in some television screens. I can now teach quantum mechanics in a way that is far more engaging than the way I was taught quantum mechanics when I was an undergrad in the 1990s, he says.

Morello also teaches the mathematics behind quantum mechanics in a more computer-friendly way. His students learn to solve problems using matrices that they can represent using code written for the Python programming language, rather than conventional differential equations on paper.

His colleagues at the UNSW are also developing laboratory courses to give students hands-on experience with the hardware in quantum technologies. For example, they designed a teaching lab to convey the fundamental concept of quantum spin, a property of electrons and some other quantum particles, using commercially available synthetic diamonds known as nitrogen vacancy centres (V. K. Sewani et al. Preprint at; 2020). Students can use magnets and a laser to observe and measure effects resulting from the diamonds quantum spin.

Quantum computers: what are they good for?

During his second trimester, Papantoniou started the Quantum Engineering Student Society. Its a difficult degree. Theres a lot of physics, a lot of maths and a lot of engineering, all of it combined together, he says. I realized straight away that there would be a need for study groups and social events to bring us together. The group invites people working at quantum-technology companies to give talks, and organizes tours of academic labs.

Asfaw thinks of these academic programmes as experiments. The quantum-technology community still needs to work out how to evaluate their success, and how various programmes can share their experiences, says Asfaw, who has helped to organize the quantum-education community. In 2020, he worked with a group of academics to identify the key concepts needed to prepare students for entering the quantum industry. These include the idea of a quantum bit, or qubit, which is the fundamental unit of information; and of a quantum state, which is a mathematical representation of a quantum object. In 2021, Asfaw worked with academics and quantum-industry specialists to publish an undergraduate curriculum in quantum engineering (A. Asfaw et al. Preprint at; 2021).

Quantum-computing companies are helping to develop quantum education directly. The industrys overall objectives are to build quantum computers and work out how to use them, says Asfaw. It will require a large and diverse workforce to achieve those objectives, so it is in the companies interests to help to train that workforce.

IBM quantum researcher Abby Mitchell.Credit: IBM

Companies are offering teaching resources for undergraduate educators. Kemper has logged into IBMs small prototype quantum computers through the cloud to teach his undergraduates the basics. Both IBMs Qiskit and Googles Cirq are open-source software packages that anyone can use and build on. For those who have left university, contributing to this software offers a path into a quantum-computing-related job, if theyre willing to put in the time. Abby Mitchell, who works for IBMs quantum team in Yorktown Heights and who studied arts and sciences as an undergraduate, learnt quantum computing on the job by writing and debugging code for Qiskit. I managed to transfer from my old job at IBM doing web development into a full-time member of the Qiskit community team, she says.

Its still unclear how quantum technology will bring commercial value. In many ways, it is a solution looking for a problem. Quantum communications, such as creating and delivering encryption keys encoded in single photons, is theoretically more secure than current cryptography techniques. But these technologies have delivered mixed results in practice, and require buy-in from institutions such as banks and governments. Existing quantum computers still make too many errors to be able to execute commercially valuable algorithms, and researchers have not worked out whether they can do anything useful with these adolescent machines. Its a chicken-and-egg problem in some ways, says Lanes.

But Papantoniou sees the uncertain future of quantum technologies as an opportunity. Even if quantum computing doesnt become commercially successful in the next few years, he says he can use the skills in short-term technologies, such as quantum sensing.

He has two more years before he graduates with two bachelors degrees, in quantum engineering and computer science. He plans to enter the quantum-technology industry after graduation, and is particularly interested in the development of algorithms for quantum computers. I have to do a lot of explaining to my parents [about] what I study, says Papantoniou. At this point, nobody really knows what a quantum engineer is. But in ten years time, they will.

The future is quantum: universities look to train engineers for an emerging industry -

D-Wave Reveals Progress in Quantum Error Mitigation with Upcoming Advantage2 System – HPCwire

PALO ALTO, Calif. and BURNABY, British Columbia, Nov. 14, 2023 D-Wave Quantum Inc. today announced important research results that demonstrate successful Quantum Error Mitigation (QEM) in its Advantage2 annealing quantum computing experimental prototype.

The techniques reduce errors in quantum simulations, producing results consistent with the quantum system maintaining its quantum state (coherence) for an order of magnitude longer time than an unmitigated system. These techniques are expected to drive performance advancements in the forthcoming Advantage2 system and future processors.

Quantum computation can be hampered by environmental noise and hardware imperfections, known as errors. While Quantum Error Correction is widely acknowledged by the industry as the ultimate solution for eliminating the impact of these errors, it comes with significant overhead, making it impractical with the current state of technology. QEM has emerged as a near-term solution for estimating error-free expectation values in the presence of small noise. This research marks D-Waves first experimental demonstration of Zero-Noise Extrapolation (ZNE), one of the most practical QEM techniques, within its annealing quantum computing systems. It offers valuable insights into the performance of more coherent systems and assists in determining design specifications for our next-generation processors. Further, these results could prove beneficial for helping customers tackle highly computationally complex problems in scientific-related and machine learning applications.

Errors represent the most significant obstacle in all forms of quantum computation, said Mohammad Amin, fellow, quantum algorithms and systems, who led the research at D-Wave. This work demonstrates the successful mitigation of such errors in quantum annealing, producing measurement results as if the qubits were nearly one order of magnitude more coherent. This enables computation in regimes that were previously inaccessible, such as quantum simulations of exotic magnetic materials, an important milestone on the road to demonstrating quantum supremacy on D-Wave processors.

This work is the latest in the companys advancements of coherent annealing quantum computing. In September 2023, D-Waveannounced notable progress in the development of high coherence fluxonium qubits. D-Wave has designed, manufactured, and operated fluxonium qubits that have demonstrated quantum properties that are comparable to the best seen to date in peer-reviewed scientific literature.

To learn more about the quantum error mitigation research and results, the technical paper is availablehere.

About D-Wave Quantum Inc.

D-Wave is a leader in the development and delivery of quantum computing systems, software, and services, and is the worlds first commercial supplier of quantum computersand the only company building both annealing quantum computers and gate-model quantum computers. Our mission is to unlock the power of quantum computing today to benefit business and society. We do this by delivering customer value with practical quantum applications for problems as diverse as logistics, artificial intelligence, materials sciences, drug discovery, scheduling, cybersecurity, fault detection, and financial modeling. D-Waves technology has been used by some of the worlds most advanced organizations including Volkswagen, Mastercard, Deloitte, Davidson Technologies, ArcelorMittal, Siemens Healthineers, Unisys, NEC Corporation, Pattison Food Group Ltd., DENSO, Lockheed Martin, Forschungszentrum Jlich, University of Southern California, and Los Alamos National Laboratory.

Source: D-Wave

Originally posted here:
D-Wave Reveals Progress in Quantum Error Mitigation with Upcoming Advantage2 System - HPCwire

Quantum AI represents a ‘transformative advancement’ – AI News

Quantum AI is the next frontier in the evolution of artificial intelligence, harnessing the power of quantum mechanics to propel capabilities beyond current limits.

GlobalData highlights a 14 percent compound annual growth rate (CAGR) increase in related patent filings from 2020 to 2022, underscoring the vast influence and potential of quantum AI across industries.

Adarsh Jain, Director of Financial Markets at GlobalData, emphasises the transformative nature of Quantum AI:

Quantum AI represents a transformative advancement in technology. As we integrate quantum principles into AI algorithms, the potential for speed and efficiency in processing complex data sets grows exponentially. This not only enhances current AI applications but also opens new possibilities across various industries.

The surge in patent filings is a testament to its growing importance and the pivotal role it will play in the future of AI-driven solutions.

Kiran Raj, Practice Head of Disruptive Tech at GlobalData, highlights that while AI thrives on data and computational power, the inner workings of the technology often remain unclear. Quantum computing not only promises increased power but also potentially provides greater insights into these workings, paving the way for AI to transcend its current capabilities.

GlobalDatas Disruptor Intelligence Center analysis reveals significant synergy between quantum computing and AI innovations, leading to revolutionary impacts in various industries. Notable collaborations include HSBC and IBM in finance, Menten AIs healthcare advancements, Volkswagens partnership with Xanadu for battery simulation, Intels Quantum SDK, and Zapatas collaboration with BMW.

Raj concludes with a note of caution: Quantum AI offers the potential for smarter, faster AI systems, but its adoption is complex and demands caution. The technology is still in its early stages, requiring significant investment and expertise.

Key challenges include the need for advanced cybersecurity measures and ensuring ethical AI practices as we navigate this promising yet intricate landscape.

(Photo by Anton Maksimov on Unsplash)

See also: Google expands partnership with Anthropic to enhance AI safety

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, artificial intelligence, globaldata, quantum ai, quantum computing, report, study

See the article here:
Quantum AI represents a 'transformative advancement' - AI News

IBM Extends Its Goals For AI And Quantum Computing – Seeking Alpha

David Ramos

While no one ever doubts the heritage of technological advancements that NYSE:IBM has made over the last several decades, there are certainly some whove wondered recently whether the company is able to sustain those types of efforts into the future. At a recent analyst day at their historic Thomas J. Watson Research Center, IBM made a convincing argument that they are up to the task, especially in the fields of AI - generative AI, in particular - as well as quantum computing.

What was particularly notable was the fact that the company showed a much tighter connection between the work its research group is doing on advanced technologies and the rapid productization of this work into its commercial product organizations. In both prepared remarks and in response to questions, it was clear that theres a renewed focus to ensure that the two groups are in lockstep with regard to their future outlook and development priorities.

As with many other organizations, that hasnt always been the case with IBM. The result has been that some potentially interesting research efforts havent always made it to the market. Thanks to a very clear directive from CEO Arvind Krishna (who used to run IBM Research) about the companys need to focus on a few specific areas - hybrid cloud, AI and quantum - current research director Dario Gil said that the coordination between research and commercial products groups has never been stronger. The net result should be - and is starting to show - important new capabilities that are making it into commercial products at a much faster pace.

One real-world impact of this new strategic initiative is the companys very rapid development of its suite of AI tools they call watsonx. First unveiled at the companys Think conference earlier this year (see "IBM Unleashes Generative AI Strategy With watsonx" for more), watsonx continues to evolve, driven in large part by new capabilities first developed by the IBM research group. What was particularly impressive at the recent analyst event was the number of real-world applications and customer examples using watsonx that IBM was able to talk about. While admitting that many organizations are still in the exploratory and proof-of-concept phase when it comes to GenAI, there were still a solid set of company logos from real-world implementations that they shared. In addition, IBM had an impressively thorough taxonomy of applications for which companies are starting to use watsonx and genAI.

On the application front, IBM noted that the top applications that its starting to see companies leverage GenAI for fall into three main categories: Digital Labor or HR-related activities, Customer Care or customer support, and App Modernization or code creation. Within those categories, the company discussed content creation, summarization, classification, and coding applications. Given the long history of older mainframe-related software that run on IBM mainframes, IBM noted particular interest in companies who want to move from old COBOL code to modern programming languages with the help of GenAI-powered tools.

In addition to applications, IBM talked about a number of technologies its working on within its research group to improve its watsonx offerings. Specifically, IBM discussed its efforts in Performance and Scale, Model Customization, Governance and Application Enablement. For Performance, IBM said that its working on a variety of new ways to improve the efficiency of how large foundation models. Its doing that through various combinations of technologies that do things like shrink the model size via quantization, improve the ability to share limited compute resources with GPU fractioning, and more.

Given its open-source focus, IBM also provided more details on all the work its doing with AI application framework tool Pytorch, which Meta (META) made open-source back in 2017. By leveraging the open-source community as well as its own efforts, the company talked about how its making significant improvements in both optimizing model performance and opening up the possibility of running Pytorch-built models across a wide range of different computing chip architectures from multiple vendors. Adding a hardware abstraction layer like Pytorch opens up the potential for a much wider range of programmers to build or customize GenAI models. The reason is that models can be created with these tools using languages such as JavaScript that are much more widely known than the chip-specific tools and their lower-level language requirements. At the same time, these hardware abstraction layers often end up adding fairly significant performance penalties because of their high-level nature (an issue that Nvidias (NVDA) Cuda software tools dont suffer from). With the new Pytorch 2.0, however, IBM said they and others are making concerted efforts to reduce that impact by better organizing where various types of optimization layers need to be and, as a result, are getting closer to on the metal performance.

On the Model Customization front, its clear IBM is spending a great deal of effort because theyve recognized that very few companies are actually building their own models - most are simply customizing or fine-tuning existing ones. (To read more about that development and some of its potential industry implications, check out my recent column "The Rapidly Evolving State Of Generative AI".) To that end, they discussed foundation model tuning techniques such as LoRA (Low Rank Adaptation), parameter-efficient tuning, multi-task prompt tuning, and more, all of which are expected to be commercialized within watsonx in the not-too-distant future. They also described the need to provide educational guidance in the model-building process to help developers pick the right size model and data sets for a given task. While this may sound simplistic, its an absolutely essential requirement, as even basic knowledge about how GenAI models are built and function is much more limited than people realize (or are willing to admit!).

IBMs efforts on Governance - that is, the tracking and reporting of details around how a model is built and evolved, they data used to create it, etc. - look to be an extremely important and key differentiating capability for the company. This is particularly true in regulated industries and environments where the company has a large customer base. While more details on IBMs specific governance capabilities are expected shortly, they did share some of the work theyre doing on providing guardrails to prevent the inclusion of biases, social stigmas, obscene content, and personally identifiable information (PII) into datasets intended for model ingestion. In addition, they talked about some of the work on risk assessment and prevention that theyve done. IBM recently announced that they will be offering indemnification for customers who use their foundation models so that they can avoid any IP protection-related lawsuits. Together with this governance work, these two efforts clearly demonstrate that IBM is in a market-leading position when it comes to critical concerns that some companies have about the trust and reliability of GenAI technology in general.

In the area of Application Enablement, IBM talked a great deal about the work its doing around Retrieval Augmented Generation (RAG). RAG is a relatively new technique that supercharges the inferencing process, makes it significantly easier and more cost-efficient for companies to leverage their own data, and eases the process of fine-tune existing foundation models so that organizations dont have to worry about creating models of their own. IBM says it has already seen a number of its customers start to experiment with and/or adopt RAG techniques so its working on refining its capabilities there to make the creation of more useful GenAI applications much easier for its customers.

In the world of quantum computing, IBM is already seen as a leader, in large part because of the amount of time theyve already spent working on discussing the innovations theyve made there. What was particularly impressive at the analyst event, however, was that the company showed off a detailed technology roadmap that extends all the way out to 2030. While some tech companies are willing to share their plans a few years out, its virtually unheard of for a company to provide this much information so far in advance. In part, IBM recognizes that they need to do it because quantum computing is such a dramatic and forward-looking technology that many potential customers feel the need to know how they can plan for it. To put it simply, they want to understand whats coming in order to bet on the roadmap.

Full details of the specific IBM quantum computing developments will be unveiled at an event that the company will be hosting in early December. Suffice it to say, however, that the company continues to be at the cutting edge of this technology and is growing increasingly confident about its ability to eventually make it into mainstream enterprise computing.

Given the long, sad history of early technology companies that no longer exist, its certainly understandable why some harbor doubts about the 112-year-old IBMs ability to continue innovating. As it recently demonstrated, however, not only is that spirit of invention still alive, it looks to be gaining some serious steam.

Disclaimer: Some of the author's clients are vendors in the tech industry.

Disclosure: None.

Source: Author

Editor's Note: The summary bullets for this article were chosen by Seeking Alpha editors.

Read more:
IBM Extends Its Goals For AI And Quantum Computing - Seeking Alpha

How Quantum Computing Can Alter The Automotive Landscape – Mobility Outlook

Simulation plays a key role in vehicle development at various stages of design and manufacturing.

Yet, it has its own set of limitations/challenges including accuracy and realism, complexity and computational resources, model validation and verification, data availability and quality, and predictive capability.

However, says Aditya Singh, Founding Member and Head of Business, BosonQ Psi (BQP), quantum computing can significantly boost simulation, particularly in solving complex problems which can challenge classical computers,

It is the next-in-line technology for computing that leverages quantum mechanics to process and manipulate information. Where classical computers use bits (which can be either 0 or 1) as the basic unit of data, quantum computers use quantum bits or qubits.

Qubits can exist in multiple states simultaneously (everything between 0 & 1 including the two numbers) due to superposition and entanglement. This is the state of one qubit depending on another even when separated by great distances. This unique behaviour enables quantum computers to perform certain calculations faster than their classical siblings.

Quantum computing can transform the mobility sector by optimising material and vehicle design, advancing vehicle innovations like alternative fuels, managing traffic, improving supply chains, enhancing vehicle design, and advancing autonomous vehicles. In a nutshell, it offers more efficient and sustainable mobility solutions.

According to Singh, quantum computers can perform system-level simulations a lot quicker than classical computers. This helps in system-level simulations as against the more complex component-level. Quantum computers can model these systems more accurately and rapidly leading to innovations and advancements in vehicle design, lightweight materials and electric vehicles.

Optimisation Problems

Many simulations involve optimisation tasks such as finding the best configuration among many possibilities. Quantum algorithms can efficiently solve such problems where a large number of variables and complex data sets are involved.

By simulating metaphysics, quantum computing can simulate behaviour and interactions in high-energy, multi-physics environments. This can then design experiments to understand the fundamentals of crash behaviour much better.

BQP is developing a next-gen engineering simulation software, BQPhy, which leverages the power of quantum computing algorithms for speedy complex engineering simulations. Simulations are critical but can take infeasible time, so companies try to oversimplify them. This leads to design inefficiencies or product failures.

Nearly 70% of potential analyses are not done. Our solution accelerates highly accurate simulations for customers products and product developers/avoids costly mistakes, says Singh.

BQPhy, the CAE software powered by quantum computing to accelerate multiphysics and system-level simulations, is integrated with quantum algorithms that run on current high-performance computers (HPCs). There is no change in simulation experience, tech stack or systems while knowledge of quantum computing is not needed. The BQPhy V1 - quantum-inspired design optimisation module integrates with traditional solvers.


The benefits include accurate identification of global minima with more optimal design and fewer design iterations, reducing overall simulation time. Besides, it requires fewer computing resources, eventually reducing the cost of HPC.

Expectations from the engineering team to solve complex design problems, given the conflicting constraints, are immense. The margins are incredibly small with the smallest variable having the potential to massively change the outcome of the result. Even something as seemingly insignificant as tyre pressure or the weight of a bolt can impact speed and handling of the car.

This is where faster, accurate simulations are required when companies go all out on innovating something like a lightweight, faster EV that can withstand a crash impact and mitigate thermal runway problems, explains Singh.

Unlike traditional design processes, simulations combined with quantum computing algorithms can be applied in many ways. Teams involved can perform simulations faster with higher accuracy and faster turnaround than what is possible with classical computers. Therefore, designs can be tested, validated and delivered much faster, reducing the overall cost of production and reducing the engineering lead time.

Design for manufacturing, design for assembly and design for service are the three basic elements for any OEM or Tier-1 supplier. However, to grab the sweet spot in achieving all objectives is a huge challenge. Singh says achieving this elusive radial centre requires a delicate equilibrium of design considerations and quantum algorithms running on current HPCs.

BQPs simulations can help in complex optimisation where quantum algorithms can solve related problems. They analyse design variables simultaneously while seeking the optimal configuration that balances manufacturing efficiency, ease of assembly and serviceability. This helps fine-tune designs to hit the sweet spot faster in fewer iterations as simulation efficiency increases.

The parallel processing, where the simulations based on quantum leverage the power of superposition and entanglement, allows for parallel processing of multiple design scenarios. This property allows the processing of terabytes of diverse data much faster. It reduces the time needed to explore a wide design space and helps in quicker decision-making.

According to Singh simulations based on the quantum approach support iterative refinement much faster than traditional approaches. This allows automakers to explore a range of design iterations much faster. This approach also facilitates a more informed and data-driven decision-making process.

In addition, quantum machine learning provides a higher level of precision in predicting how design changes impact manufacturing, assembly and serviceability. This ensures that decisions are based on a robust understanding of the consequences.

By helping automotive companies streamline their design processes, quantum simulations contribute to resource efficiency. This can result in cost savings and reduced environmental impact.

Averting Potential Risks

Quantum simulations help automakers identify potential risks and challenges in the design phase. It allows them to address issues before they become costly problems, thereby enhancing product quality and reliability. As quantum tech matures, auto companies will experience its power and Singh says BQP can help them become future-ready.

Quantum computing can provide significant advancements in processing power and optimisation. Quantum-powered simulations and algorithms can help in training autonomous vehicles, designing software defined vehicle (SDV) architectures and enhancing manufacturing processes.

Taking a cue from IBM, a leading player in quantum computing, Singh says the company is using quantum technology to help the auto industry solve EV and traffic-related problems. BMW has partnered with quantum computing company, Pasqal, to leverage quantum processors for better manufacturing processes. This eliminates the need for physical prototypes and allows simulation of new materials.

Such collaborations demonstrate the increasing focus on quantum computing within the automotive industry. Its ability to handle complex optimisation problems and perform rapid analysis can be used to design advanced SDV architectures. The simulations can find optimal configurations, thereby reducing development time and enhancing overall performance SDVs. Moreover, continues Singh, quantum algorithms can contribute to training artificial intelligence and autonomous systems.

While still in its early stages, the potential impact of quantum computing technology, especially in SDVs, is being explored and researched by industry leaders and experts. It could become an essential tool for carmakers in their journey towards SDVs.

Also Read:

Simulation Is At The Core Of Developing Sustainable Products

RVW Conference Highlights The Importance Of Simulation Testing

Read more:
How Quantum Computing Can Alter The Automotive Landscape - Mobility Outlook

NVIDIA Unveils Collaborations with BASF, IQM, Classiq, and Terra Quantum – Quantum Computing Report

In several announcements that came out in conjunction with The International Conference for High Performance Computing, Networking, Storage, and Analysis conference being held this week, NVIDIA has announced several collaborations with quantum end users and quantum providers that use its CUDA Quantum software platform.

The first collaboration is with the chemical company BASF to simulate a compound called Nitrilotriacetic acid (NTA), a compound that can remove toxic metals from a citys wastewater. BASF was able to model this compound using 60 qubit simulations on NVIDIAs Eos H100 Supercomputer. This is the largest simulation that BASF has ever run. A second collaboration announced is with Classiq, a quantum software company, and theTel Aviv Sourasky Medical Center. This collaboration will focus on researching the potential of quantum technology for applications in life sciences and healthcare. It will also provide training to non-quantum experts so they can develop quantum applications for use in their life science research. Another collaboration was announced with IQM with a purpose to advance future hybrid quantum applications. And finally, Terra Quantum also announced it will work with NVIDIA to accelerate the integration of hybrid quantum and classical computers through GPUs.

To learn more about all these and other collaborations, you can view a blog post on the NVIDIA website here. You can also view separate press releases from IQM, Classiq, and Terra Quantum that describe their respective activity with NVIDIA.

November 14, 2023

Read the original post:
NVIDIA Unveils Collaborations with BASF, IQM, Classiq, and Terra Quantum - Quantum Computing Report

Advances in Quantum Computing Pave the Way to Next Generation … – PR Newswire

WASHINGTON, Nov. 14, 2023 /PRNewswire/ -- Research from a team of scientists from Universities Space Research Association (USRA), Rigetti Computing, and NASA Ames Research Center has led to the development of a significant step toward the challenging goal of combinatorial optimization for harnessing the power of quantum computing. This research is part of the DARPA Optimization with Noisy Intermediate Scale Quantum (ONISQ) program -- awarded toUSRA in 2019 to direct a tight scientific collaboration between USRA, NASA and Rigetti Computing. The work is focused on developing fundamental advances of quantum optimization methods that will be impacting the U.S. military capabilities in the future.

Noise in quantum hardware has been a persistent problem and to mitigate this issue the researchers introduced an innovative quantum algorithm, inspired by and building on recent advances in the field of quantum hybrid optimization. This algorithm, in the presence of strong hardware noise, outperforms its classical "greedy" counterpart.

Using a cutting-edge programmable superconducting quantum computer (the Rigetti Aspen-M-3 system) featuring up to 72 qubits, this research is a significant milestone in our understanding of the requirements of quantum advantage. The results were recently published in the paper "Quantum-Enhanced Greedy Combinatorial Optimization Solver" in Science Advances.

According to Dr. Davide Venturelli, Associate Director of USRA's Research Institute for Advanced Computer Science and Principal Investigator of ONISQ "Scheduling Applications with Advanced Mixers" (SAAM) project, a large fraction of the applied quantum computing community is still focusing on toy problems that can be simulated and fully understood. "The challenge is not to be afraid of developing sophisticated algorithms that use the full resources of current quantum hardware, no matter how daunting it might seem to beat the noise that affects quantum systems".

Dr. Maxime Dupont, lead author of the paper said, "Our work demonstrates that noisy superconducting quantum computers can solve combinatorial optimization at scale with good performance closing the gap toward a quantum advantage as more qubits and better fidelities become available."

The work and its demonstration on 72 qubits provide a new perspective for the development of quantum algorithms and is further improvable with error-mitigation techniques, which will be investigated in future projects.

Additional Resources:



About USRA

Foundedin 1969, under the auspices of the National Academy of Sciences at the request of the U.S. Government, the Universities Space Research Association (USRA) is a nonprofit corporation chartered to advance space-related science, technology and engineering. USRA operates scientific institutes and facilities, and conducts other major research and educational programs. It engages the university community of 117 universities, employs in-house scientific leadership, and offers innovative research and development, and project management expertise.More information about USRA is available

About Rigetti Computing

Rigetti is a pioneer in full-stack quantum computing. The Company has operated quantum computers over the cloud since 2017 and serves global enterprise, government, and research clients through its Rigetti Quantum Cloud Services platform. The Company's proprietary quantum-classical infrastructure provides high performance integration with public and private clouds for practical quantum computing. Rigetti has developed the industry's first multi-chip quantum processor for scalable quantum computing systems. The Company designs and manufactures its chips in-house at Fab-1, the industry's first dedicated and integrated quantum device manufacturing facility. Learn more

PR Contact:Suraiya Farukhi[emailprotected]443-812-6945

SOURCE Universities Space Research Association

Go here to see the original:
Advances in Quantum Computing Pave the Way to Next Generation ... - PR Newswire

Crypto asset discovery and the post-quantum migration – Help Net Security

Quantum computing is reshaping our world and will revolutionize many industries, including materials science, life sciences, transportation, and energy. Google recently demonstrated the power of quantum computers by solving a problem in seconds that todays supercomputers require nearly 50 years to solve.

There is, however, a dark side to quantum computers. Many experts predict that, within the next 7 to 10 years, quantum computers will break RSA and ECC encryption. RSA and ECC are public key encryption algorithms that underpin the security for virtually all cybersecurity systems, applications, and protocols. They provide security for credit card transactions, online banking, medical devices, connected cars, and many other systems.

There is plenty of time to address this problem; after all we have around a decade before quantum computers can break these algorithms. Companies must start preparing to ensure they are protected once a sufficiently advanced quantum computer has been developed.

While quantum computers are high-speed at specific problems, they are relatively weak at solving other problems. But, they can quickly factor large prime numbers and solve elliptic curve discrete logarithm problems, allowing them to break RSA and ECC encryption.

NIST, the US National Institute of Standards and Technology, is leading a process to create and standardize new encryption algorithms to replace RSA and ECC. The new algorithms rely on mathematical approaches that are not easily broken by quantum or classical computers. NIST has standardized two algorithms for code signing and has released draft standards for 3 new PQC algorithms for Digital Signature and KEMs/Encryption use cases.

There are several reasons companies can begin planning their migration to PQC now. First, replacing todays public key encryption algorithms is a monumental undertaking. This list of items to be updated includes:

Migrating to PQC requires significant planning, development, testing, and coordination with suppliers and partners.

There is no question that adversaries are currently capturing data to decrypt it once a quantum computer is available that can break RSA and ECC. These attacks are called Store Now, Decrypt Later or Harvest Now, Decrypt Later attacks. This is a very real and current threat for any organization with data that must be kept secure beyond the 2030-2033 timeframe.

Hackers can record TLS sessions now and later break the RSA or ECC-based key exchange to extract the AES key used to encrypt session traffic. They can then decode the data transmitted during the session using the AES key. While much of the information transmitted today will not be valuable in 10 years, some information has a much longer data protection period. Corporate trade secrets, national security information, and other sensitive information must be protected for decades to come. This information is already at risk from quantum computing attacks.

Now that PQC standards are maturing, governments are beginning to mandate the migration to PQC algorithms. In December of 2022, US President Joe Biden signed into law the Quantum Computing Cybersecurity Preparedness Act which mandates timelines for moving government systems to PQC algorithms. In September of 2022, the NSA announced the Commercial National Security Algorithm Suite 2.0 (CNSA 2.0). CNSA 2.0 mandates timeliness for migration to PQC algorithms for all National Security Systems, creating urgency to companies selling solutions to the US government. These mandates require adoption of PQC algorithms as early as 2025 for many systems and use cases.

Enterprises need to act now by preparing to migrate their systems to PQC. Migration will be a multi-step process and will require several years to complete. A high-level PQC migration roadmap will address:

One of the first steps for any company developing a PQC migration roadmap is to create an inventory of where and how cryptography is used in their organization. For an organization of any scale, this requires a crypto discovery tool that can monitor network traffic and detect what encryption algorithms are used, who is using them, and how they are configured. Once the crypto inventory is complete, companies can begin planning how they will migrate these systems to PQC algorithms.

In some cases, companies must work with partners and suppliers to coordinate the migration to PQC algorithms. If the cryptographic processing is integrated into a system from a vendor, that vendor will have to provide an update or new system that supports PQC. In cases where the company maintains the software implementing RSA or ECC, they will be able to update new encryption algorithms themselves. That, of course, requires sourcing updated encryption libraries that support the new algorithms.

This process also requires careful coordination. Whenever encrypted data is shared between systems, processes, or applications, both ends of the system must be updated simultaneously.

Read the original:
Crypto asset discovery and the post-quantum migration - Help Net Security

Navigating the quantum revolution in logistics – Supply Chain Dive

Logistics management has entered a new era, marked by the demand for rapid shipping, optimized value chains and real-time adaptability to supply chain disruptions. This flexibility isnt a nice-to-have. Your organization must be flexible enough to adapt in near-real-time to the frequent supply chain disruptions that your logistics operations will inevitably face.

To embrace this new wave of logistics operations, your systems and technology must be ready to solve complex shipping challenges in a fraction of the time once required. Logistics organizations can harness quantum computing and other next-gen technologies to thrive in this environment.

Here's how.

Previously confined to the pages of science fiction, quantum computing has become a powerful tool, poised to reshape logistics operations. Unlike classical computers, quantum computers use quantum bits or qubits, representing multiple states simultaneously. This enables them to process vast amounts of data and perform complex calculations at lightning speed. Quantum annealing, a subfield of quantum computing, is particularly relevant to logistics as it helps solve complex optimization problems efficiently.

Managing your logistics operations more effectively could require analyzing the massive volumes of data you acquire every day. Traditional computing methods struggle to process this data quickly, leading to delays and suboptimal decisions. Factors like weather events, traffic and unforeseen delays can disrupt routing and scheduling, making near-real-time data processing crucial.

All these factors combine to make logistics ripe for quantum innovation. Logistics organizations face four key challenges where quantum computing shines:

Far from being an aspirational vision, quantum-powered logistics has matured into a practical solution with transformative implications for your operations. Here's an example.

Consider the process of building a unit load device (ULD) or pallets for air or truck shipments and the potential routes that could be taken to reach their final destinations. Your team has an influx of data from various sources, such as crucial information about shipments' dimensions, weight and contents, handwritten notes on damaged packages, and offline decisions made on shipment exceptions. With quantum computing, logistics organizations can process vast amounts of data in near-real-time, enabling you to make data-informed decisions better and faster via logistics optimization and analytics.

Because some of this data is generated offline, your system misses out on important feedback that should be integrated for continuous improvement. But there's a fix. Logistics management systems can collect the data via intelligent process workflows and then use artificial intelligence and advanced analytics to learn and improve the ULD and pallet-building process. And because logistics operations deal with so much data at any given time, adding quantum computing allows this data to be utilized in the timeframe the operations team needs to respond.

Are you ready to experience the new frontier of logistics innovation? Unisys is at the forefront of developing next-gen computing capabilities to help organizations extract maximum value from their logistics operations.

Want to learn about groundbreaking advancements in quantum computing, artificial intelligence and advanced analytics in logistics management? Unisys can guide you through the frontiers of next-gen capabilities in logistics. Contact us today to learn more.

Continued here:
Navigating the quantum revolution in logistics - Supply Chain Dive

IQM Partners with NVIDIA for Hybrid Quantum Applications – Analytics India Magazine

IQM Quantum Computers (IQM) has partnered with NVIDIA, a collaboration set to revolutionise quantum processing units programming. The joint effort will leverage NVIDIA CUDA Quantum, an open-source platform designed for the seamless integration and programming of quantum processing units within a unified system.

As a result of this collaboration, enterprises and research institutions utilising IQMs quantum processing units will gain the ability to program and cultivate the next wave of hybrid quantum-classical applications using NVIDIA CUDA Quantum.

Read: NVIDIA wants to replicate CUDA success with Quantum Computing

Subscribe to our Newsletter

Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy

Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions.

This partnership, announced today at the SC Conference 2023 in Denver, seeks to expedite the progress and application of quantum computing across various domains. The collaborative endeavour aims to foster innovation, facilitate collaboration, and potentially unlock groundbreaking advancements in both scientific and industrial realms.

Prominent institutions, including CSC IT Centre for Science and the VTT Technical Research Centre of Finland, are poised to leverage CUDA Quantum on VTTs 5-qubit quantum computer. This quantum computer stems from a co-innovation partnership between IQM and VTT, highlighting the practical implications of the collaboration.

IQMs overarching vision is to provide immediate accessibility for scientists and experts to integrate quantum and classical systems seamlessly. Envisioning a future characterised by quantum-accelerated supercomputing, IQM aspires to witness quantum computers and supercomputers working in tandem to tackle paramount issues, such as machine learning, cybersecurity, and drug and chemical research.

Dr. Peter Eder, Head of Strategic Partnerships at IQM Quantum Computers, expressed during the announcement at the SC Conference 2023, The collaboration with NVIDIA is a strategic step that will help accelerate the progress of potential use cases. It offers our new and existing users the option to use NVIDIAs high-quality software framework to explore quantum solutions in their applications with our quantum hardware. We will continue to provide the best available tools to our users to increase quantum adoption.

Tim Costa, Director of High Performance Computing and Quantum at NVIDIA, emphasised the transformative potential of quantum integrated supercomputing, stating, NVIDIAs collaboration with IQM will enable researchers to advance the state of the art in the coupling of quantum with GPU supercomputing, opening the door for countless breakthroughs.

Read the original post:
IQM Partners with NVIDIA for Hybrid Quantum Applications - Analytics India Magazine