Scope AI Corp.(“Scope” or the “Company”) is pleased to announce that it has closed its previously announced acquisition (the “Acquisition”) of the QSE Technology (see news release dated July 9, 2024), pursuant to a technology agreement dated July 9, 2024 (the “Technology Agreement”) with Ovryde Ltd. (“Ovryde”) whereby Ovryde transferred its ownership and rights relating to delivery and application of quantum resilient entropy (the “QSE Technology”).

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

The Acquisition enables Scope to provide retail and institutional customers with extremely robust security solutions that are easy to build upon or integrate into existing infrastructures. As quantum computing becomes more mainstream and rapidly advances, the potential for traditional encryption methods to be rendered obsolete grows, posing significant risks to data security worldwide. Our QSE Technology ensures that customers are prepared for the current, imminent, and ever-evolving threats posed by quantum computing, safeguarding their digital assets with state-of-the-art quantum-resilient solutions. This positions Scope at the forefront of the digital security industry, ready to address the current and future quantum threat and drive significant growth for the company.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

James Young, CEO of Scope AI Corp, commented, “The quantum cryptography market is currently at $11 billion for 2024, and is projected to grow to $126 billion by 2033.^[1] This tremendous growth highlights the increasing demand for advanced quantum-resilient security solutions, and with our acquisition of QSE Technology, Scope AI is well-positioned to capitalize on this market.”

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

The post SCOPE AI Closes Quantum Security Technology Acquisition appeared first on AiThority.

The post AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra appeared first on AiThority.

Quantum AI, a cutting-edge technology, combines quantum computing principles with artificial intelligence. It can potentially transform business operations and drive innovation to new heights. Unlike traditional computers that rely on bits to process data, quantum computers use qubits, which can exist in multiple states simultaneously. This inherent parallelism enables quantum AI systems to process vast amounts of data at unprecedented speeds.

The Global Quantum AI Market is valued at USD 242.4 million in 2023 and is expected to grow to USD 1.8 billion by 2030, with a compound annual growth rate (CAGR) of 34.1% over the forecast period from 2023 to 2030 as per Market Digits reports.

The transformative potential of quantum AI harnesses quantum computing to reimagine artificial intelligence, providing groundbreaking solutions across industries. This article covers how quantum AI reshapes business strategies, optimizes operations, and fosters innovation in ways previously unimaginable. Welcome to the future of intelligence—welcome to the quantum revolution.

Top Vendors In The Global Quantum AI Market:

• Alibaba Group
• Alpine Quantum Technologies (AQT)
• Atos
• Bleximo
• C12 Quantum Electronics
• Diraq
• D-Wave
• Google Quantum AI
• IBM
• Intel
• Microsoft
• Quantinuum
• Rigetti
• Zapata

Understanding Quantum AI

Quantum AI refers to the application of quantum computing to machine learning algorithms. Leveraging the computational power of quantum computing, Quantum AI can produce results unattainable by classical computers.

Classical AI vs. Quantum AI: A Comparative Analysis

Feature	Classical AI	Quantum AI
Pattern Recognition & Data Analysis	Tackles complex, multi-dimensional problems	Tackles complex, multi-dimensional problems
Strengths	Excellent for decision-making and optimization	Superfast for certain tasks with exponential scaling
	Mature technology with wide applications	Potential for groundbreaking discoveries in materials, drugs, and more
Weaknesses	Struggles with vast, complex datasets	Early stage, prone to errors and decoherence
	Relies on sequential processing, can be slow for certain problems	Not a silver bullet, best suited for specific problems
	Limited by current chip technology	Requires specialized quantum computers and algorithms
Complementary Roles	Shines at tasks like image recognition and personalized recommendations	Accelerates drug discovery, optimizes financial models, and unlocks new materials
Future Outlook	Will continue to evolve, becoming more efficient and versatile	Potential to revolutionize industries; overcoming technical hurdles is crucial

Importance of Quantum AI

Despite significant advancements in AI over the past decade, technological limitations persist. Quantum computing, with its unique capabilities, offers solutions to these challenges, potentially paving the way for Artificial General Intelligence (AGI). It enables rapid training of machine learning models and the creation of optimized algorithms. Quantum AI can perform years of analysis in a short time, driving technological advancements.

Quantum computing addresses fundamental AI challenges such as neuromorphic cognitive models, adaptive machine learning, and reasoning under uncertainty. It represents a promising solution for next-generation AI, offering optimized and stable performance.

Also Read: The Role of AI and Machine Learning in Streaming Technology

Mechanisms of Quantum AI

Quantum AI operates by integrating quantum modeling and machine learning techniques. A prime example is Google’s TensorFlow Quantum (TFQ), an open-source library designed for quantum machine learning. TFQ aims to provide essential tools to control and model natural or artificial quantum systems.

Overview of Computational Steps in TensorFlow Quantum:

1. Convert Quantum Data to Quantum Dataset: Quantum data, represented as a multi-dimensional array of numbers called quantum tensors, is processed by TensorFlow to create a dataset for further use.
2. Select Quantum Neural Network Models: Based on the structure of the quantum data, appropriate quantum neural network models are chosen. These models perform quantum processing to extract information hidden in entangled states.
3. Sample or Average: Measurement of quantum states extracts classical information as samples from the classical distribution, derived directly from the quantum state. TFQ offers methods for averaging over several runs involving the previous steps.
4. Evaluate with Classical Neural Networks: Once quantum data is converted to classical data, deep learning techniques are applied to learn the correlations within the data.

Quantum AI: Opportunities and Challenges

• Symbiotic Collaboration with Classical Computers: Quantum and classical computers can collaborate, similar to the CPU-GPU dynamic, enhancing overall computing capabilities. Quantum computing offers data center professionals a unique opportunity to engage in a learning curve, leading to more efficient and powerful computing solutions. This integration of Quantum AI will contribute to a transformative synergy in computing.
• Quantum Key Distribution (QKD) for Enhanced Security: Quantum computing introduces Quantum Key Distribution as a solution for data security. QKD safeguards data in the quantum era and advances encryption methods to meet evolving technological challenges.
• Collaboration Opportunities: The advent of Quantum AI invites collaboration and knowledge sharing across diverse industries. Initially, challenges like limited quantum resources, skill and knowledge gaps in quantum programming, and varying industry adoption rates may restrict technology usage to those already ahead of the curve. However, Quantum AI’s adaptive nature encourages collective efforts to address and overcome these hurdles, fostering collaboration that transcends industry boundaries.
• Combating Deepfake Proliferation: Quantum computing can collaborate with social media platforms to combat fake news and manipulated videos. Its high-speed processing enhances content moderation efforts, ensuring the integrity of information shared on digital platforms and contributing to a healthier digital environment.
• Transformative Power in Data Centers: Quantum computing redefines efficiency and sustainability in data centers. Its ability to process complex algorithms at unprecedented speeds aligns with growing developer demands. Additionally, Quantum AI’s potential to reduce energy consumption offers a path toward a more sustainable and eco-friendly future.
• Enhanced Deepfake Detection: Quantum AI’s remarkable processing power enhances deepfake detection algorithms across social media platforms, where millions of videos and audios are uploaded daily. While quantum technology can increase deepfake creation, it also has the potential to develop Quantum Safe deepfakes, heralding a new era of secure and reliable detection methods. This advancement assures information integrity, mitigating concerns about misinformation and manipulation.

Also Read: Surviving the AI Marketing Revolution: How to Go From an Onlooker To Riding the Wave of Change

Business Innovation Benefits of Quantum AI

Quantum AI offers numerous potential advantages, including:

Increased Computing Power: Quantum AI performs calculations that classical computers cannot, enabling it to solve complex problems faster and with greater accuracy.

Faster Machine Learning: Quantum AI accelerates machine learning algorithms, processing vast datasets in real-time more effectively than classical computers.

Improved Predictive Capabilities: Quantum AI enhances prediction accuracy in complex environments, particularly beneficial in industries like finance where precise forecasts yield substantial benefits.

Enhanced Optimization: Quantum AI optimizes intricate systems such as supply chains or transportation networks more efficiently than classical computers, leading to significant cost savings and operational improvements.

Applications of Quantum AI

As quantum AI moves from theory to practical application, its transformative impact in various sectors becomes increasingly evident. Leading companies and agile startups are spearheading the integration of quantum AI, driving innovation across diverse industries. Here’s a detailed exploration of six key applications:

1. Pharmaceuticals & Healthcare: Quantum AI enables personalized medical treatments tailored to individual genetic profiles. Companies like Rigetti Computing collaborate with biotech firms to expedite drug discovery by leveraging advanced quantum AI algorithms. This breakthrough accelerates scientific research and enhances personalized medicine capabilities.
2. Finance: Quantum AI enhances data processing capabilities in finance, optimizing trading strategies and portfolio management. Institutions like JPMorgan Chase are pioneering quantum integration to refine trading algorithms and improve risk analysis, ultimately maximizing investment returns.
3. Supply Chain and Logistics: Quantum AI revolutionizes supply chain and logistics by leveraging advanced AI and camera technologies for real-time data analysis. Partnerships like D-Wave’s collaboration with logistics firms focus on optimizing delivery routes, reducing costs, and minimizing environmental impact through efficient route planning.
4. Energy: In the energy sector, Quantum AI enhances grid management efficiency and forecasts energy demand by analyzing large datasets. IBM collaborates with energy companies to optimize energy production and distribution, addressing challenges in renewable energy adoption and promoting sustainability.
5. Aerospace and Defense: Quantum AI provides advanced data-driven intelligence solutions for aerospace and defense. NASA’s Quantum Artificial Intelligence Laboratory (QuAIL) utilizes quantum algorithms for mission planning, system diagnostics, and improving aerospace system efficiency and safety.
6. Cryptography: Quantum AI advances cybersecurity with super-secure encryption and quantum-resistant cryptographic systems. Innovations by companies like Google in post-quantum cryptography ensure data security against emerging threats posed by quantum computing capabilities.

Future Outlook

Looking ahead, the future of Quantum AI can only be transformative at best. Imagine personal cancer therapies optimized by quantum simulation, platforms for weather forecasting that can predict extreme events more accurately than ever before, and more.

In addition, the domain-specific effects of Quantum AI are poised to transform our understanding of the universe. The synergy between quantum computing and AI is already showing promising results and is expected to improve further. This applications revolution, where quantum computers offer power and AI provides intelligence, will be evident across various industries.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

The post Quantum AI in Businesses: Transforming the Future appeared first on AiThority.

Developing new quality productive forces has become a major theme in China’s policymaking since it was first put forward by Chinese President Xi Jinping in September 2023. It is also listed as a priority for this year’s economic tasks outlined in the Government Work Report delivered to the ongoing two sessions in Beijing.

“China will strive to modernize the industrial system and develop new quality productive forces at a faster pace this year,” noted the report, which placed sci-tech innovation high on the government’s agenda.

The national lawmakers and political advisers have expressed full confidence on the prospects of China’s sci-tech advance and economic development, saying that the rapid development of strategic emerging industries such as artificial intelligence (AI), quantum computing and new green energies will shore up sustainable momentum to support the high-quality development of Chinese economy.

Xi, also general secretary of the Communist Party of China (CPC) Central Committee and chairman of the Central Military Commission, on Tuesday stressed developing new quality productive forces in China in accordance with local conditions during the second session of the 14th National People’s Congress (NPC), the Xinhua News Agency reported.

President Xi called for focusing on high-quality development as the top priority, urging efforts to step up innovation, foster emerging industries, adopt forward-thinking plans for developing future-oriented industries and improve the modernized industrial system.

Recommended AI News: Hexaware Introduces Tensai GPT, an AI Innovation Powered by Azure Open AI

New growth drivers

The term – new quality productive forces – emerges from continuous breakthroughs in science and technology, which will drive the development of strategic emerging industries that may bring disruptive technological advances in the era of intelligent information.

Developing new quality productive forces is a decisive step in the economy’s high-quality development course, Guo Guoping, an NPC deputy and a vice director of the Key Laboratory of Quantum Information of the Chinese Academy of Sciences, told the Global Times.

“The concept offers guidance for our country to take advantage of the historical opportunity of a new round of technological upgrade and aims to develop strategic emerging industries and future industries,” Guo said, noting it is of great importance for China to implement innovation-driven development strategy, seize the high ground in global industrial competition and build up China’s manufacturing edge.

Currently, the development of new quality productive forces in China is picking up pace.

Official data showed that China’s output of new-energy vehicles reached 9.44 million in 2023, up 30.3 percent on a yearly basis, while the output of solar panels rose by 54 percent to reach 540 million kilowatts. Last year, the country’s production of service robots reached 7.83 million sets, up 23.3 percent year-on-year.

The development of new quality productive forces has great potential in China, as its huge market place ensures full testing, application and evolution of new technologies and new business models, Xu Jiuping, a professor of Sichuan University and a member of the National Committee of the 14th Chinese People’s Political Consultative Conference (CPPCC), told the Global Times.

Xu advocated that enterprises, with the support of national innovation policies, make full use of the market demand to help China’s manufacturing sector overcome shortcoming and boost the development of new quality productive forces in China.

Recommended AI News: Veeam and Microsoft to Collaborate on AI Solutions for Top Data Protection Platform

AI Plus initiative

In recent years, the economic growth in the world was mainly driven by new technologies, which would give birth to new industries and then form new productivity. In order to promote the development of new quality productive forces in China, analysts said the country should firmly adhere to deepening scientific research and technological innovation.

China is now beefing up support for building new manufacturing lines that are integrated with advanced tech breakthroughs such as AI, quantum computing and new and green energies. China ranked 12th place in the 2023 Global Innovation Index, and became the country with the largest number of top 100 sci-tech innovation clusters in the world for the first time, according to the latest ranking by the World Intellectual Property Organization.

It’s projected that global AI competition will become a systemic contest in 2024, which will also be a crucial year for China and the US to compete in the in-depth application of generative AI breakthroughs said Liu Qingfeng, chairman of Chinese artificial intelligence company iFlytek who is also a deputy to the NPC.

Liu suggested that China should accelerate technological advances in key sectors including neural science, brain-like intelligence innovation, and quantum computing so as to achieve an overtaking on the curve.

Yang Jie, chairman of China Mobile and also a member of the National Committee of the CPPCC, suggested that the country boosts an AI Plus initiative in the national level by strengthening top-level design and clarifying development goals and key tasks, in order to fully give play to the huge potential of AI in achieving leapfrog development of technologies, industrial upgrade and productivity.

Comprised of AI and the manufacturing sector, smart manufacturing is an important part of forming new quality productive forces. However, China’s smart manufacturing faces three major problems: Supply capability need to be strengthened, application needs to be promoted, and a standard system needs to be established, said Zhong Zheng, an NPC deputy and vice president of Midea Group.

She suggested that the country support leading companies in various industries to take the lead in developing industrial solutions so as to help more companies set up world-leading smart factories.

While putting focus on emerging and future industries, Chinese analysts said the country should aggregate high-level innovation talent to foster the whole chain of innovation. In addition to independently nurturing high-level talent, the country should also deepen reforms in talent introduction channels and set up a new mechanism to attract talent from all over the globe.

Recommended AI News: DoiT Achieves the AWS Generative AI Competency

[To share your insights with us as part of editorial or sponsored content, please write to sghosh@martechseries.com]

The post Global Times: China’s Focus on AI, Robotics, and Quantum Computing appeared first on AiThority.

The research demonstrates a breakthrough in applying quantum-enhanced generative AI to drug discovery using today’s quantum devices

Zapata Computing, Inc,, the Industrial Generative AI company, announced that its scientists, in collaboration with Insilico Medicine, the University of Toronto, and St. Jude Children’s Research Hospital have demonstrated the first instance of a generative model running on quantum hardware outperforming state-of-the-art classical models in generating viable cancer drug candidates. The research points to a promising future of hybrid quantum generative AI for drug discovery using today’s quantum devices.

In the study, the researchers utilized generative AI to develop novel KRAS inhibitors, a critical focus in cancer therapy historically deemed “undruggable” due to its intrinsic biochemical properties. Generative models running on classical hardware, quantum hardware (specifically, a 16-qubit IBM device), and simulated quantum hardware generated one million drug candidates each, which were then filtered algorithmically and by humans. The resulting 15 molecules were then synthesized and tested through cell-based assays. The two molecules generated by the quantum-enhanced generative model were distinct from existing KRAS inhibitors and showed a superior binding affinity over the molecules generated by purely classical models.

Recommended AI News: MGID Revamps Ad Campaign Planning and Execution With New and Intuitive AI-Driven Platform

“This project is an exciting demonstration of how quantum and classical computing can complement each other to deliver an end-to-end solution,” said Yudong Cao, CTO and co-founder at Zapata AI. “The collaboration between Zapata, UofT, St. Jude and Insilico is also a great example of how the startup and university ecosystems can leverage each other’s advantages to drive progress. We’re looking forward to taking this research further to move the discovered molecules through the drug discovery pipeline, apply our methodology to other disease targets, and extend our quantum-enhanced generative AI to other industrial use cases with complex design challenges.”

Zapata AI is an Industrial Generative AI company, revolutionizing how enterprises solve complex problems with its powerful suite of Generative AI software. By combining numerical and text-based solutions, Zapata AI empowers industrial-scale enterprises and government entities to leverage large language models and numerical generative models better, faster, and more efficiently delivering solutions to drive growth, savings and unprecedented insight. With proprietary science and engineering techniques and the Orquestra platform, Zapata AI is accelerating Generative AI’s impact in Industry.

The research is currently published on ArXiv as it awaits peer review. The study is a follow-up to a study published by the team in 2023, in collaboration with Foxconn, that first showed the promise of quantum generative AI for drug discovery.

“This research provides further validation of the potential of Insilico’s generative AI engine, Chemistry42, to be combined with quantum-augmented generative models in order to develop novel therapeutic possibilities for difficult-to-drug targets in cancer and other indications,” says Alex Zhavoronkov, PhD, founder and co-CEO of Insilico Medicine. “This represents an important early step toward a more advanced drug discovery future and we look forward to working with Zapata AI and Alán Aspuru-Guzik at the University of Toronto to further develop these methods.”

Recommended AI News: InveniAI Announces Collaboration with Ono Pharmaceutical for Target Discovery

The news also follows a recent announcement that Zapata AI has entered a new strategic partnership with D-Wave Quantum Inc., with an initial focus on building quantum generative AI models that accelerate the discovery of new molecules for commercial applications. “For the first time ever, we’ve been able to produce real effective drug lead molecules with quantum-enhanced generative AI,” said Christopher Savoie, CEO and co-founder of Zapata AI. “The best part is this is only the beginning. This is the same tech we are developing into a commercial product in our work with D-Wave, which we expect to bring to market quickly given the advanced commercial maturity of Zapata’s generative AI technology and D-Wave’s annealing quantum computing. We’re looking forward to expanding on this research to discover new molecules for drug discovery and other industrial applications.”

“I have always been excited about the potential of AI and quantum computing for drug and materials discovery,” said Alán Aspuru-Guzik, a professor of Chemistry and Computer Science at the University of Toronto, as well as a co-founder and Scientific Advisor of Zapata AI. “We are just starting to see how to integrate quantum computing modules into the drug discovery pipeline. It is great to see that we were able to successfully discover a new compound to inhibit KRAS. Many questions are open. Although everything you can do in this paper could also be done with a classical computer, it is exciting to see that this work sets the path for future, more powerful quantum computers to demonstrate their abilities. The global community of researchers will be able to further improve upon this first-of-a-kind experiment.”

Recommended AI News: Ascendion and HFS Research GenAI Report: Unlocking the Future with GenAI Skills Playbook

The post Quantum-Enhanced Generative AI Generates Viable Cancer Drug Candidates appeared first on AiThority.

Scientists to Run State-of-the-Art Quantum Computing Simulations Using NVIDIA CUDA Quantum Platform, Turbocharged by NVIDIA Grace Hopper Superchips

NVIDIA announced that Australia’s Pawsey Supercomputing Research Centre will add the NVIDIA CUDA Quantum platform accelerated by NVIDIA Grace Hopper Superchips to its National Supercomputing and Quantum Computing Innovation Hub, furthering its work driving breakthroughs in quantum computing.

Researchers at the Perth-based center will leverage CUDA Quantum — an open-source hybrid quantum computing platform that features powerful simulation tools, and capabilities to program hybrid CPU, GPU and QPU systems — as well as, the NVIDIA cuQuantum software development kit of optimized libraries and tools for accelerating quantum computing workflows.

The NVIDIA Grace Hopper Superchip — which combines the NVIDIA Grace CPU and Hopper GPU architectures — provides extreme performance to run high-fidelity and scalable quantum simulations on accelerators and seamlessly interface with future quantum hardware infrastructure.

Recommended AI News: Salt Security API Protection Platform Now Available for Purchase in the CrowdStrike Marketplace

“High-performance simulation is essential for researchers to address the biggest challenges in quantum computing — from algorithm discovery and device design to the invention of powerful methods for error correction, calibration and control,” said Tim Costa, director of HPC and quantum computing at NVIDIA. “CUDA Quantum, together with the NVIDIA Grace Hopper Superchip, allows innovators such as Pawsey Supercomputing Research Centre to achieve these essential breakthroughs and accelerate the timeline to useful quantum-integrated supercomputing.”

“Pawsey Supercomputing Centre’s research and test-bed facility is helping to advance scientific exploration for all of Australia as well as the world,” said Mark Stickells, executive director at the Pawsey Supercomputing Research Centre. “NVIDIA’s CUDA Quantum platform will allow our scientists to push the boundaries of what’s possible in quantum computing research.”

Australia’s national science agency, CSIRO (Commonwealth Scientific and Industrial Research Organisation), estimates the domestic market opportunity from quantum computing to be worth $2.5 billion annually in revenue, with the potential to create 10,000 new jobs by 2040. Achieving this will require quantum computing to be embedded in other scientific domains, with applications in astronomy, life sciences, medicine, finance and more.

Recommended AI News: Lenovo and Anaconda Announce Agreement to Accelerate AI Development and Deployment

Pushing the Boundaries of Quantum Computing

Pawsey will deploy the system to run quantum workloads directly from traditional high performance computing systems, leveraging their processing power and developing hybrid algorithms that intelligently divide calculations into classical and quantum kernels, using the quantum device to improve computing efficiency. Quantum machine learning, chemistry simulations, image processing for radio astronomy, financial analysis, bioinformatics and specialized quantum simulators will be studied, starting with various quantum variational algorithms.

Pawsey is deploying eight NVIDIA Grace Hopper Superchip nodes based on NVIDIA MGX™ modular architecture. GH200 Superchips eliminate the need for a traditional CPU-to-GPU PCIe connection by combining an Arm-based NVIDIA Grace™ CPU with an NVIDIA H100 Tensor Core GPU in the same package, using NVIDIA NVLink™-C2C chip interconnects.

This increases the bandwidth between GPU and CPU by 7x compared with the latest PCIe technology. It delivers up to 10x higher performance for applications running terabytes of data, giving quantum-classical researchers unprecedented power to solve the world’s most complex problems.

Pawsey is committed to making the NVIDIA Grace Hopper platform available to the Australian quantum community, as well as its international partners.

Recommended AI News: Thales and Quantinuum Launch Starter Kit to Help Enterprises Prepare for Post-Quantum Cryptography

[To share your insights with us as part of editorial or sponsored content, please write to sghosh@martechseries.com]

The post NVIDIA Accelerates Quantum Computing Exploration at Australia’s Pawsey Supercomputing Centre appeared first on AiThority.

Breakthrough technology accelerates development of algorithms for practical use in quantum computers

Fujitsu announced the development of a novel technique on a quantum simulator that speeds up quantum-classical hybrid algorithms, which have been proposed as a method for the early use of quantum computers, achieving 200 times the computational speed of previous simulations. For quantum circuit computations using conventional quantum and classical hybrid algorithms, the number of times of quantum circuit computation increases depending on the scale of the problem to be solved. Larger-scale problems that require many qubits, including simulations in the materials and drug discovery fields, may even require several hundred days.

The newly developed technology enables simultaneous processing of a large number of repetitively executed quantum circuit computations distributed among multiple groups. Fujitsu has also devised a way to simplify problems on a large scale with less loss of accuracy by using one of the world’s largest-scale quantum simulators (1)it has developed. Fujitsu has made it possible to perform computations on a quantum simulator in just one day, which would take an estimated 200 days to complete with conventional methods. As a result, it is now possible to complete simulations of large-scale quantum computation within a realistic timeframe and to simulate the behavior of larger molecules computed by a hybrid quantum-classical algorithm, leading to algorithm development.

Fujitsu plans to incorporate this technology into its hybrid quantum computing platform to accelerate research into the practical application of quantum computers in various fields, including finance and drug discovery. Additionally, Fujitsu will not only apply this technology to quantum simulators, but also to accelerate quantum circuit computations on actual quantum computers.

Recommended AI News:  Fujitsu Revolutionizes the Future at MWC Barcelona 2024 with AI-powered Network Innovations

Background

Although the development of fault-tolerant quantum computers (FTQC (2) ) is currently progressing worldwide, current quantum computers face many problems, such as the inability to eliminate the effects of noise. At the same time, in order to demonstrate the usefulness of quantum computers ahead of FTQC, practical applications for small and medium-sized quantum computers (Noisy Intermediate-Scale Quantum Computer, NISQ) with noise tolerance of 100 to 1,000 qubits are being studied.

By applying VQE (3), a typical NISQ algorithm, Fujitsu, for example, has developed a quantum simulator for quantum application development (4) and has been working to speed up quantum circuit computation itself. However, in VQE, the number of iterations of quantum circuit computation increases as the size of the problem increases, so it takes a very long time to perform computation, especially for large problems requiring many qubits, and it is estimated that it takes several 100 days for a quantum simulator. Therefore, it was difficult to develop quantum algorithms for practical use.

Outline of the newly developed technology

In response to this problem, Fujitsu has developed a technology that achieves 200 times higher the performance speed of conventional technologies by simultaneously distributing multiple repetitively executed quantum circuit computations and reducing the amount of quantum circuit computations by reducing accuracy degradation.

Distributed concurrency of optimization processes requiring repeated computation of quantum circuits

Quantum-classical hybrid algorithms seek a quantum circuit that provides the lowest energy state, for example, the ground state of a molecule, by alternating between the process of performing quantum circuit computation and the process of optimizing quantum circuit parameters (5) using a classical computer. However, for parameter optimization of quantum circuits by classical computers, it is necessary to prepare a large number of quantum circuits with small changes in parameters, perform quantum circuit computation for all of them sequentially, and derive the optimal parameters from the results. This requires considerable time for computation, especially for larger-scale problems. Increasing the number of nodes simply to speed up circuit computation has conventionally been limited by communication overhead, and new technologies were required.

Focusing on the fact that quantum circuits with small parameter changes can be executed without affecting each other, Fujitsu has developed a distributed processing technology that enables each group to execute different quantum circuits by dividing the computation nodes of the quantum simulator into multiple groups and using RPC (6)technology to submit quantum circuit computation jobs through the network. Using this technology, multiple quantum circuits with different parameters can be simultaneously distributed and calculated, and the computation time can be reduced to 1/70th of the conventional technology.

Recommended AI News:  Fujitsu and Delft University of Technology Collaborate to Establish Cutting-Edge Quantum Lab

In addition, since the computation quantity in the quantum-classical hybrid algorithm is proportional to the number of terms in the equation of the problem to be solved, and the number of terms is the fourth power of the number of qubits in the general VQE, the computation quantity increases as the problem scale increases, and the result cannot be obtained in a realistic time. Through simulations of large molecules using 32 qubits of one of the world’s largest 40 qubit quantum simulators, Fujitsu has found that the ratio of terms with small coefficients to the total number of terms increases as the scale increases, and that the effect of terms with small coefficients on the final results of calculations is minimal. By taking advantage of this characteristic, Fujitsu was able to achieve both a reduction in the number of terms in the equation and prevention of deterioration in computation accuracy, thereby reducing the quantum circuit computation time by approximately 80%.

By combining these two technologies, Fujitsu was able to demonstrate for the first time in the world that when distributed processing of 1024 compute nodes into 8 groups for a 32 qubit problem, it was possible to achieve a quantum simulation run time of 32 qubits in one day, compared to the previous estimate of 200 days. This is expected to advance the development of quantum algorithms for problems with a large number of qubits and the application of quantum computers to the fields of materials and finance.

Yukihiro Okuno, Senior Research Scientist, Analysis Technology Center, Fujifilm Corporation, comments:

We are investigating the application of quantum computers to materials development. Among them, the use of VQE in NISQ devices is an essential consideration. We expect that this acceleration technology will greatly speed up the principle verification of the VQE algorithm.

Tsuyoshi Moriya, Vice President, Digital Design Center, Tokyo Electron Limited, comments:

We are studying the use of VQE to calculate the energy of molecules related to semiconductor materials, to predict the electronic structure and physical properties of specific materials, and to optimize chemical reactions in semiconductor manufacturing processes. We hope that accelerating this process will enable us to quickly verify the principle and effectiveness of the VQE algorithm and discover its usefulness. NISQ devices whose use is limited by noise and errors will be considered with an eye toward these limitations.

[1] One of the world’s largest-scale quantum simulators :It is one of the world’s largest permanent dedicated units of State Vector (As of February 2024, according to Fujitsu), a universal quantum circuit simulation method.
[2] FTQC :Fault-Tolerant Quantum Computer. Quantum computer capable of executing quantum computation without error while correcting quantum error.
[3] VQE :Abbreviation for Variational Quantum Eigensolver, a technique for determining the energy of matter by repeating computation with a quantum computer and optimization with a classical computer.
[4] A quantum simulator for quantum application development :Fujitsu achieves major technical milestone with world’s fastest 36 qubit quantum simulator (March 30, 2022)
[5] Quantum circuit parameters :Rotation angle of the gate of the quantum circuit for adjusting the reference quantum state to a physically meaningful quantum state.
[6] RPC :Short for Remote Procedure Call. The art of performing a process from one computer to another over a network or the like.

Recommended AI News: Bybit’s Web3 Swap Levels Up with SWFT Blockchain Partnership for Liquidity and Accessibility

[To share your insights with us as part of editorial or sponsored content, please write to sghosh@martechseries.com]

The post Fujitsu Develops Technology to Speed up Quantum Circuit Computation in Quantum Simulator by 200 Times appeared first on AiThority.

SEALSQ Corp, a company that focuses on developing and selling Semiconductors, PKI and Post-Quantum technology hardware and software products, announced its entry into the race to develop cutting-edge AI chips, underpinned by its visionary AIoT (Artificial Intelligence of Things) strategy, a bold move that positions the Company at the forefront of technological innovation. This strategic pivot leverages the fusion of AI and IoT technologies to offer a fully integrated platform aiming to revolutionize digital transformation and innovation for its customers.

SEALSQ’s AIoT strategy is built on a robust foundation of semiconductors, smart sensors, IoT systems, AI technologies, and an expansive data cloud. This integration is designed to provide an end-to-end solution that not only drives innovation but also ensures that digital transformation initiatives are met with unprecedented success and efficiency.

Recommended AI News: DH2i Launches DxOperator for Simplified SQL Server Container Deployment on Kubernetes

A key component of this strategy is the utilization of the advanced cybersecurity technology and IoT network of WISeKey (SIX: WIHN, NASDAQ: WKEY), SEALSQ’s parent company, ensuring that data is collected and processed securely in real-time. This capability allows SEALSQ to offer immediate and highly secure responses to dynamic situations, setting a new standard in the industry for reliability and safety.
The AIoT system functions as the central brain of the expansive SEALSQ ecosystem, which currently includes over 1.6 billion semiconductor-powered devices. This network acts as a nervous system for the IoT landscape, facilitating swift and secure interactions across a myriad of devices and platforms.
A significant aspect of SEALSQ’s strategy is the integration of Generative AI technology, which enhances the learning capabilities of the ecosystem:

• Generative AI technology allows for the generation of novel and original content or data, dramatically improving the functionality of autonomous IoT devices. For instance, self-driving cars equipped with Generative AI technology can navigate with unprecedented precision, adapting to obstacles and changing road conditions more effectively than ever before.
• Generative AI plays a crucial role in personalizing user experiences with IoT devices. Smart home devices, for example, can adapt to user preferences over time, leading to smarter energy use, cost savings, and minimized maintenance downtime.
• In the realm of cybersecurity, Generative AI technology significantly enhances the security of autonomous devices, empowering devices to recognize and react to potential threats and vulnerabilities, bolstering resilience against hacking and other security breaches. By generating adversarial examples and synthetic data, Generative AI not only improves the accuracy of machine learning models but also strengthens defenses against sophisticated cyber-attacks.

Recommended AI News: Auterion Government Solutions Transforms Combat Drone Operations with Information Revolution

As a leading innovator in the technology sector, SEALSQ is committed to driving digital transformation and innovation through cutting-edge solutions. With its groundbreaking AIoT strategy, SEALSQ is setting new standards in the integration of AI and IoT technologies, offering secure, efficient, and transformative solutions for a rapidly evolving digital landscape.

SEALSQ focuses on selling integrated solutions based on Semiconductors, PKI and Provisioning services, while developing Post-Quantum technology hardware and software products. Our solutions can be used in a variety of applications, from Multi-Factor Authentication tokens, Smart Energy, Smart Home Appliances, and IT Network Infrastructure, to Automotive, Industrial Automation and Control Systems.

Post-Quantum Cryptography (PQC) refers to cryptographic methods that are secure against an attack by a quantum computer. As quantum computers become more powerful, they may be able to break many of the cryptographic methods that are currently used to protect sensitive information, such as RSA and Elliptic Curve Cryptography (ECC). PQC aims to develop new cryptographic methods that are secure against quantum attacks.

Recommended AI News: Planet Satellite Data Now Available on Google Cloud Marketplace

[To share your insights with us as part of editorial or sponsored content, please write to sghosh@martechseries.com]

The post SEALSQ Enters Into AI Chip Market With Advanced AIoT Strategy appeared first on AiThority.

The DC and low-frequency voltage source and breakout box are designed for quantum computing at scale, supporting high channel density with minimal rack space

Quantum Machines, the provider of breakthrough quantum control solutions that accelerate the development of practical quantum computers, announced the addition of two new high-density solutions to its industry-leading quantum electronics product family.

QDAC-II Compact is a versatile, ultra-stable, ultra-low-noise 24-channel voltage source for tuning superconducting and spin qubits for optimal performance. It retains all the features of the best-selling QDAC-II, but with more compact dimensions, fitting into one fourth the space (1U).

Recommended AI News: Medidata and Sanofi Vaccines Extend Collaboration to Improve Patient Centricity

QSwitch is an easy-to-use, software-controllable breakout box with 240 relays. Users can pre-program experiments and quickly switch between setups and instruments, saving valuable research and development time.

QDAC-II Compact and QSwitch can each be used stand-alone or connected in series via a single 24-pin cable.

Key Benefits:

• Optimal Signal Integrity: Delivers ultra-low noise, minimal crosstalk, and minimal transients for reliable qubit operation.
• Versatility: Switchable between fast tuning and ultra-stable operation points, ideal for the operation of quantum processors.
• Timesaving: Incorporates numerous pre-programmed functions, such as gate-leakage detection and virtual gate scans to speed experiment setup time and reduce costs.
• Software Control: Allows remote experiment control by SCPI commands, Python, or QCoDeS.
• Easy to Use: Includes flexible, low-noise cable assembly and setup instructions to implement your experiment scripts within minutes.

Recommended AI News: LogicMonitor’s Business Results Highlight Global Demand for Hybrid Observability

“QSwitch and QDAC-II Compact embody our dedication to providing streamlined, space-saving solutions without compromising on performance,” said Itamar Sivan, co-founder and CEO of Quantum Machines. “The ability of both new solutions to be stacked and operated cohesively with multiple OPX1000 units exemplifies our focus on scalable quantum computing, allowing for the seamless expansion and sophistication of control systems.”

Recommended AI News: Perigon Secures $5 Million in Seed Funding to Structure the Open Web for AI

[To share your insights with us as part of editorial or sponsored content, please write to sghosh@martechseries.com]

The post Quantum Machines Introduces QDAC-II Compact and QSwitch – Ultra-Low-Noise Quantum Electronics Solutions appeared first on AiThority.

Qubrid, a leading GPU Cloud and Quantum Computing company announced immediate access to its leading hybrid GPU-QPU cloud computing platform focused at solving complex real-world problems. The Qubrid Cloud Platform (QCP) shortens access to GPUs and QPUs making them accessible and programmable using an easy-to-use web interface.

“Our vision of accelerating AI and making GPU and Quantum Computing resources available to everyone is now real and available for customers”

What can I do with Qubrid Cloud today?

The Qubrid platform is open now for developers, researchers and scientists to login and start working on AI/ML or Quantum Computing projects. You would be able to:

• Run AI/ML Programs on GPU
• Simulate Quantum Computing programs on GPU
• Run Quantum Computing programs on QPU
• Reserve ‘hard to get’ GPU instances
• Instantly access Jupyter Notebook programming environment
• Program in Python or Qiskit
• Use pre-loaded AI packages including Pytorch, TensorFlow, Keras etc.

Recommended AI News: SK Networks Partners with Vivek for AI-Centric Business Renaissance

Why Qubrid GPU Cloud Platform?

There are multiple reasons why QCP can be right for you:

• Budget – if you don’t have the budget for fully assembled on-premise GPU systems, you can spend a fraction of the price of systems and run your programs
• Scale – You can scale from single GPU per node to thousands of GPUs in a cluster without worrying about purchasing expensive hardware
• Zero maintenance – with QCP, you don’t have to worry about setting up hardware or installing OS or AI packages. All that is done by us so you can just focus on developing your AI/ML/LLM applications.
• Use while waiting for your on-premise hardware – if you have ordered long lead-time GPU systems, you can get a head-start using GPUs in the cloud to fill in the gap until your systems arrive at your door.

Recommended AI News: Oraki – Pioneering a New Paradigm in EMEA’s Ad Tech

How do I access QCP and what’s the pricing?

The Qubrid platform offers high volume GPUs on an on-demand basis. Additionally, for high-end GPUs, customers can reserve instances on monthly, six-monthly or annual subscription basis.

Pricing for on-demand GPUs and QPUs is available after logging in.

“Our vision of accelerating AI and making GPU and Quantum Computing resources available to everyone is now real and available for customers,” said Pranay Prakash, chief executive officer at Qubrid. “We invite developers from startups, universities, commercial and government entities to use our platform – whether you’re working on a Deep Learning application, LLM, Generative AI or solving a logistics optimization problem using Quantum Computing, you will be able to use the Qubrid platform with ease and with necessary tools required for your applications.”

Recommended AI News: Cathy Hackl To Launch Spatial Dynamics, A New Spatial Computing & AI Solutions Company

[To share your insights with us as part of editorial or sponsored content, please write to sghosh@martechseries.com]

The post Qubrid GPU Cloud Platform Early Access Available for Generative AI, LLM and Quantum Computing Simulations appeared first on AiThority.