If you review these M8010-663 PDF Braindumps, you will get 100% marks.

If are looking to successfully finish the IBM M8010-663 exam, killexams.com has IBM Digital Marketing Optimization Sales Mastery Test v1 Latest Questions which usually will ensure a person passes M8010-663 on the first attempt. killexams.com provides you download for valid, Newest, and 2022 up-to-date M8010-663 PDF Braindumps and Questions and Answers using full money back ensure.

Exam Code: M8010-663 Practice test 2022 by Killexams.com team
IBM Digital Marketing Optimization Sales Mastery Test v1
IBM Optimization availability
Killexams : IBM Optimization availability - BingNews https://killexams.com/pass4sure/exam-detail/M8010-663 Search results Killexams : IBM Optimization availability - BingNews https://killexams.com/pass4sure/exam-detail/M8010-663 https://killexams.com/exam_list/IBM Killexams : IBM Research Rolls Out A Comprehensive AI And Platform-Based Edge Research Strategy Anchored By Enterprise Use Cases And Partnerships

I recently met with Dr. Nick Fuller, Vice President, Distributed Cloud, at IBM Research for a discussion about IBM’s long-range plans and strategy for artificial intelligence and machine learning at the edge.

Dr. Fuller is responsible for providing AI and platform–based innovation for enterprise digital transformation spanning edge computing and distributed cloud management. He is an IBM Master Inventor with over 75 patents and co-author of 75 technical publications. Dr. Fuller obtained his Bachelor of Science in Physics and Math from Morehouse College and his PhD in Applied Physics from Columbia University.

Edge In, not Cloud Out

In general, Dr. Fuller told me that IBM is focused on developing an "edge in" position versus a "cloud out" position with data, AI, and Kubernetes-based platform technologies to scale hub and spoke deployments of edge applications.

A hub plays the role of a central control plane used for orchestrating the deployment and management of edge applications in a number of connected spoke locations such as a factory floor or a retail branch, where data is generated or locally aggregated for processing.

“Cloud out” refers to the paradigm where cloud service providers are extending their cloud architecture out to edge locations. In contrast, “edge in” refers to a provider-agnostic architecture that is cloud-independent and treats the data-plane as a first-class citizen.

IBM's overall architectural principle is scalability, repeatability, and full stack solution management that allows everything to be managed using a single unified control plane.

IBM’s Red Hat platform and infrastructure strategy anchors the application stack with a unified, scalable, and managed OpenShift-based control plane equipped with a high-performance storage appliance and self-healing system capabilities (inclusive of semi-autonomous operations).

IBM’s strategy also includes several in-progress platform-level technologies for scalable data, AI/ML runtimes, accelerator libraries for Day-2 AI operations, and scalability for the enterprise.

It is an important to mention that IBM is designing its edge platforms with labor cost and technical workforce in mind. Data scientists with PhDs are in high demand, making them difficult to find and expensive to hire once you find them. IBM is designing its edge system capabilities and processes so that domain experts rather than PhDs can deploy new AI models and manage Day-2 operations.

Why edge is important

Advances in computing and storage have made it possible for AI to process mountains of accumulated data to provide solutions. By bringing AI closer to the source of data, edge computing is faster and more efficient than cloud. While Cloud data accounts for 60% of the world’s data today, vast amounts of new data is being created at the edge, including industrial applications, traffic cameras, and order management systems, all of which can be processed at the edge in a fast and timely manner.

Public cloud and edge computing differ in capacity, technology, and management. An advantage of edge is that data is processed and analyzed at / near its collection point at the edge. In the case of cloud, data must be transferred from a local device and into the cloud for analytics and then transferred back to the edge again. Moving data through the network consumes capacity and adds latency to the process. It’s easy to see why executing a transaction at the edge reduces latency and eliminates unnecessary load on the network.

Increased privacy is another benefit of processing data at the edge. Analyzing data where it originates limits the risk of a security breach. Most of the communications between the edge and the cloud is then confined to such things as reporting, data summaries, and AI models, without ever exposing the raw data.

IBM at the Edge

In our discussion, Dr. Fuller provided a few examples to illustrate how IBM plans to provide new and seamless edge solutions for existing enterprise problems.

Example #1 – McDonald’s drive-thru

Dr. Fuller’s first example centered around Quick Service Restaurant’s (QSR) problem of drive-thru order taking. Last year, IBM acquired an automated order-taking system from McDonald's. As part of the acquisition, IBM and McDonald's established a partnership to perfect voice ordering methods using AI. Drive-thru orders are a significant percentage of total QSR orders for McDonald's and other QSR chains.

McDonald's and other QSR restaurants would like every order to be processed as quickly and accurately as possible. For that reason, McDonald's conducted trials at ten Chicago restaurants using an edge-based AI ordering system with NLP (Natural Language Processing) to convert spoken orders into a digital format. It was found that AI had the potential to reduce ordering errors and processing time significantly. Since McDonald's sells almost 7 million hamburgers daily, shaving a minute or two off each order represents a significant opportunity to address labor shortages and increase customer satisfaction.

Example #2 – Boston Dynamics and Spot the agile mobile robot

According to an earlier IBM survey, many manufacturers have already implemented AI-driven robotics with autonomous decision-making capability. The study also indicated that over 80 percent of companies believe AI can help Boost future business operations. However, some companies expressed concern about the limited mobility of edge devices and sensors.

To develop a mobile edge solution, IBM teamed up with Boston Dynamics. The partnership created an agile mobile robot using IBM Research and IBM Sustainability Software AI technology. The device can analyze visual sensor readings in hazardous and challenging industrial environments such as manufacturing plants, warehouses, electrical grids, waste treatment plants and other hazardous environments. The value proposition that Boston Dynamics brought to the partnership was Spot the agile mobile robot, a walking, sensing, and actuation platform. Like all edge applications, the robot’s wireless mobility uses self-contained AI/ML that doesn’t require access to cloud data. It uses cameras to read analog devices, visually monitor fire extinguishers, and conduct a visual inspection of human workers to determine if required safety equipment is being worn.

IBM was able to show up to a 10X speedup by automating some manual tasks, such as converting the detection of a problem into an immediate work order in IBM Maximo to correct it. A fast automated response was not only more efficient, but it also improved the safety posture and risk management for these facilities. Similarly, some factories need to thermally monitor equipment to identify any unexpected hot spots that may show up over time, indicative of a potential failure.

IBM is working with National Grid, an energy company, to develop a mobile solution using Spot, the agile mobile robot, for image analysis of transformers and thermal connectors. As shown in the above graphic, Spot also monitored connectors on both flat surfaces and 3D surfaces. IBM was able to show that Spot could detect excessive heat build-up in small connectors, potentially avoiding unsafe conditions or costly outages. This AI/ML edge application can produce faster response times when an issue is detected, which is why IBM believes significant gains are possible by automating the entire process.

IBM market opportunities

Drive-thru orders and mobile robots are just a few examples of the millions of potential AI applications that exist at the edge and are driven by several billion connected devices.

Edge computing is an essential part of enterprise digital transformation. Enterprises seek ways to demonstrate the feasibility of solving business problems using AI/ML and analytics at the edge. However, once a proof of concept has been successfully demonstrated, it is a common problem for a company to struggle with scalability, data governance, and full-stack solution management.

Challenges with scaling

“Determining entry points for AI at the edge is not the difficult part,” Dr. Fuller said. “Scale is the real issue.”

Scaling edge models is complicated because there are so many edge locations with large amounts of diverse content and a high device density. Because large amounts of data are required for training, data gravity is a potential problem. Further, in many scenarios, vast amounts of data are generated quickly, leading to potential data storage and orchestration challenges. AI Models are also rarely "finished." Monitoring and retraining of models are necessary to keep up with changes the environment.

Through IBM Research, IBM is addressing the many challenges of building an all-encompassing edge architecture and horizontally scalable data and AI technologies. IBM has a wealth of edge capabilities and an architecture to create the appropriate platform for each application.

IBM AI entry points at the edge

IBM sees Edge Computing as a $200 billion market by 2025. Dr. Fuller and his organization have identified four key market entry points for developing and expanding IBM’s edge compute strategy. In order of size, IBM believes its priority edge markets to be intelligent factories (Industry 4.0), telcos, retail automation, and connected vehicles.

IBM and its Red Hat portfolio already have an established presence in each market segment, particularly in intelligent operations and telco. Red Hat is also active in the connected vehicles space.

Industry 4.0

There have been three prior industrial revolutions, beginning in the 1700s up to our current in-progress fourth revolution, Industry 4.0, that promotes a digital transformation.

Manufacturing is the fastest growing and the largest of IBM’s four entry markets. In this segment, AI at the edge can Boost quality control, production optimization, asset management, and supply chain logistics. IBM believes there are opportunities to achieve a 4x speed up in implementing edge-based AI solutions for manufacturing operations.

For its Industry 4.0 use case development, IBM, through product, development, research and consulting teams, is working with a major automotive OEM. The partnership has established the following joint objectives:

  • Increase automation and scalability across dozens of plants using 100s of AI / ML models. This client has already seen value in applying AI/ML models for manufacturing applications. IBM Research is helping with re-training models and implementing new ones in an edge environment to help scale even more efficiently. Edge offers faster inference and low latency, allowing AI to be deployed in a wider variety of manufacturing operations requiring instant solutions.
  • Dramatically reduce the time required to onboard new models. This will allow training and inference to be done faster and allow large models to be deployed much more quickly. The quicker an AI model can be deployed in production; the quicker the time-to-value and the return-on-investment (ROI).
  • Accelerate deployment of new inspections by reducing the labeling effort and iterations needed to produce a production-ready model via data summarization. Selecting small data sets for annotation means manually examining thousands of images, this is a time-consuming process that will result in - labeling of redundant data. Using ML-based automation for data summarization will accelerate the process and produce better model performance.
  • Enable Day-2 AI operations to help with data lifecycle automation and governance, model creation, reduce production errors, and provide detection of out-of-distribution data to help determine if a model’s inference is accurate. IBM believes this will allow models to be created faster without data scientists.

Maximo Application Suite

IBM’s Maximo Application Suite plays an important part in implementing large manufacturers' current and future IBM edge solutions. Maximo is an integrated public or private cloud platform that uses AI, IoT, and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. IBM is working with several large manufacturing clients currently using Maximo to develop edge use cases, and even uses it within its own Manufacturing.

IBM has research underway to develop a more efficient method of handling life cycle management of large models that require immense amounts of data. Day 2 AI operations tasks can sometimes be more complex than initial model training, deployment, and scaling. Retraining at the edge is difficult because resources are typically limited.

Once a model is trained and deployed, it is important to monitor it for drift caused by changes in data distributions or anything that might cause a model to deviate from original requirements. Inaccuracies can adversely affect model ROI.

Day-2 AI Operations (retraining and scaling)

Day-2 AI operations consist of continual updates to AI models and applications to keep up with changes in data distributions, changes in the environment, a drop in model performance, availability of new data, and/or new regulations.

IBM recognizes the advantages of performing Day-2 AI Operations, which includes scaling and retraining at the edge. It appears that IBM is the only company with an architecture equipped to effectively handle Day-2 AI operations. That is a significant competitive advantage for IBM.

A company using an architecture that requires data to be moved from the edge back into the cloud for Day-2 related work will be unable to support many factory AI/ML applications because of the sheer number of AI/ML models to support (100s to 1000s).

“There is a huge proliferation of data at the edge that exists in multiple spokes,” Dr. Fuller said. "However, all that data isn’t needed to retrain a model. It is possible to cluster data into groups and then use sampling techniques to retrain the model. There is much value in federated learning from our point of view.”

Federated learning is a promising training solution being researched by IBM and others. It preserves privacy by using a collaboration of edge devices to train models without sharing the data with other entities. It is a good framework to use when resources are limited.

Dealing with limited resources at the edge is a challenge. IBM’s edge architecture accommodates the need to ensure resource budgets for AI applications are met, especially when deploying multiple applications and multiple models across edge locations. For that reason, IBM developed a method to deploy data and AI applications to scale Day-2 AI operations utilizing hub and spokes.

The graphic above shows the current status quo methods of performing Day-2 operations using centralized applications and a centralized data plane compared to the more efficient managed hub and spoke method with distributed applications and a distributed data plane. The hub allows it all to be managed from a single pane of glass.

Data Fabric Extensions to Hub and Spokes

IBM uses hub and spoke as a model to extend its data fabric. The model should not be thought of in the context of a traditional hub and spoke. IBM’s hub provides centralized capabilities to manage clusters and create multiples hubs that can be aggregated to a higher level. This architecture has four important data management capabilities.

  1. First, models running in unattended environments must be monitored. From an operational standpoint, detecting when a model’s effectiveness has significantly degraded and if corrective action is needed is critical.
  2. Secondly, in a hub and spoke model, data is being generated and collected in many locations creating a need for data life cycle management. Working with large enterprise clients, IBM is building unique capabilities to manage the data plane across the hub and spoke estate - optimized to meet data lifecycle, regulatory & compliance as well as local resource requirements. Automation determines which input data should be selected and labeled for retraining purposes and used to further Boost the model. Identification is also made for atypical data that is judged worthy of human attention.
  3. The third issue relates to AI pipeline compression and adaptation. As mentioned earlier, edge resources are limited and highly heterogeneous. While a cloud-based model might have a few hundred million parameters or more, edge models can’t afford such resource extravagance because of resource limitations. To reduce the edge compute footprint, model compression can reduce the number of parameters. As an example, it could be reduced from several hundred million to a few million.
  4. Lastly, suppose a scenario exists where data is produced at multiple spokes but cannot leave those spokes for compliance reasons. In that case, IBM Federated Learning allows learning across heterogeneous data in multiple spokes. Users can discover, curate, categorize and share data assets, data sets, analytical models, and their relationships with other organization members.

In addition to AI deployments, the hub and spoke architecture and the previously mentioned capabilities can be employed more generally to tackle challenges faced by many enterprises in consistently managing an abundance of devices within and across their enterprise locations. Management of the software delivery lifecycle or addressing security vulnerabilities across a vast estate are a case in point.

Multicloud and Edge platform

In the context of its strategy, IBM sees edge and distributed cloud as an extension of its hybrid cloud platform built around Red Hat OpenShift. One of the newer and more useful options created by the Red Hat development team is the Single Node OpenShift (SNO), a compact version of OpenShift that fits on a single server. It is suitable for addressing locations that are still servers but come in a single node, not clustered, deployment type.

For smaller footprints such as industrial PCs or computer vision boards (for example NVidia Jetson Xavier), Red Hat is working on a project which builds an even smaller version of OpenShift, called MicroShift, that provides full application deployment and Kubernetes management capabilities. It is packaged so that it can be used for edge device type deployments.

Overall, IBM and Red Hat have developed a full complement of options to address a large spectrum of deployments across different edge locations and footprints, ranging from containers to management of full-blown Kubernetes applications from MicroShift to OpenShift and IBM Edge Application Manager.

Much is still in the research stage. IBM's objective is to achieve greater consistency in terms of how locations and application lifecycle is managed.

First, Red Hat plans to introduce hierarchical layers of management with Red Hat Advanced Cluster Management (RHACM), to scale by two to three orders of magnitude the number of edge locations managed by this product. Additionally, securing edge locations is a major focus. Red Hat is continuously expanding platform security features, for example by recently including Integrity Measurement Architecture in Red Hat Enterprise Linux, or by adding Integrity Shield to protect policies in Red Hat Advanced Cluster Management (RHACM).

Red Hat is partnering with IBM Research to advance technologies that will permit it to protect platform integrity and the integrity of client workloads through the entire software supply chains. In addition, IBM Research is working with Red Hat on analytic capabilities to identify and remediate vulnerabilities and other security risks in code and configurations.

Telco network intelligence and slice management with AL/ML

Communication service providers (CSPs) such as telcos are key enablers of 5G at the edge. 5G benefits for these providers include:

  • Reduced operating costs
  • Improved efficiency
  • Increased distribution and density
  • Lower latency

The end-to-end 5G network comprises the Radio Access Network (RAN), transport, and core domains. Network slicing in 5G is an architecture that enables multiple virtual and independent end-to-end logical networks with different characteristics such as low latency or high bandwidth, to be supported on the same physical network. This is implemented using cloud-native technology enablers such as software defined networking (SDN), virtualization, and multi-access edge computing. Slicing offers necessary flexibility by allowing the creation of specific applications, unique services, and defined user groups or networks.

An important aspect of enabling AI at the edge requires IBM to provide CSPs with the capability to deploy and manage applications across various enterprise locations, possibly spanning multiple end-to-end network slices, using a single pane of glass.

5G network slicing and slice management

Network slices are an essential part of IBM's edge infrastructure that must be automated, orchestrated and optimized according to 5G standards. IBM’s strategy is to leverage AI/ML to efficiently manage, scale, and optimize the slice quality of service, measured in terms of bandwidth, latency, or other metrics.

5G and AI/ML at the edge also represent a significant opportunity for CSPs to move beyond traditional cellular services and capture new sources of revenue with new services.

Communications service providers need management and control of 5G network slicing enabled with AI-powered automation.

Dr. Fuller sees a variety of opportunities in this area. "When it comes to applying AI and ML on the network, you can detect things like intrusion detection and malicious actors," he said. "You can also determine the best way to route traffic to an end user. Automating 5G functions that run on the network using IBM network automation software also serves as an entry point.”

In IBM’s current telecom trial, IBM Research is spearheading the development of a range of capabilities targeted for the IBM Cloud Pak for Network Automation product using AI and automation to orchestrate, operate and optimize multivendor network functions and services that include:

  • End-to-end 5G network slice management with planning & design, automation & orchestration, and operations & assurance
  • Network Data and AI Function (NWDAF) that collects data for slice monitoring from 5G Core network functions, performs network analytics, and provides insights to authorized data consumers.
  • Improved operational efficiency and reduced cost

Future leverage of these capabilities by existing IBM Clients that use the Cloud Pak for Network Automation (e.g., DISH) can offer further differentiation for CSPs.

5G radio access

Open radio access networks (O-RANs) are expected to significantly impact telco 5G wireless edge applications by allowing a greater variety of units to access the system. The O-RAN concept separates the DU (Distributed Units) and CU (Centralized Unit) from a Baseband Unit in 4G and connects them with open interfaces.

O-RAN system is more flexible. It uses AI to establish connections made via open interfaces that optimize the category of a device by analyzing information about its prior use. Like other edge models, the O-RAN architecture provides an opportunity for continuous monitoring, verification, analysis, and optimization of AI models.

The IBM-telco collaboration is expected to advance O-RAN interfaces and workflows. Areas currently under development are:

  • Multi-modal (RF level + network-level) analytics (AI/ML) for wireless communication with high-speed ingest of 5G data
  • Capability to learn patterns of metric and log data across CUs and DUs in RF analytics
  • Utilization of the antenna control plane to optimize throughput
  • Primitives for forecasting, anomaly detection and root cause analysis using ML
  • Opportunity of value-added functions for O-RAN

IBM Cloud and Infrastructure

The cornerstone for the delivery of IBM's edge solutions as a service is IBM Cloud Satellite. It presents a consistent cloud-ready, cloud-native operational view with OpenShift and IBM Cloud PaaS services at the edge. In addition, IBM integrated hardware and software Edge systems will provide RHACM - based management of the platform when clients or third parties have existing managed as a service models. It is essential to note that in either case this is done within a single control plane for hubs and spokes that helps optimize execution and management from any cloud to the edge in the hub and spoke model.

IBM's focus on “edge in” means it can provide the infrastructure through things like the example shown above for software defined storage for federated namespace data lake that surrounds other hyperscaler clouds. Additionally, IBM is exploring integrated full stack edge storage appliances based on hyperconverged infrastructure (HCI), such as the Spectrum Fusion HCI, for enterprise edge deployments.

As mentioned earlier, data gravity is one of the main driving factors of edge deployments. IBM has designed its infrastructure to meet those data gravity requirements, not just for the existing hub and spoke topology but also for a future spoke-to-spoke topology where peer-to-peer data sharing becomes imperative (as illustrated with the wealth of examples provided in this article).

Wrap up

Edge is a distributed computing model. One of its main advantages is that computing, and data storage and processing is close to where data is created. Without the need to move data to the cloud for processing, real-time application of analytics and AI capabilities provides immediate solutions and drives business value.

IBM’s goal is not to move the entirety of its cloud infrastructure to the edge. That has little value and would simply function as a hub to spoke model operating on actions and configurations dictated by the hub.

IBM’s architecture will provide the edge with autonomy to determine where data should reside and from where the control plane should be exercised.

Equally important, IBM foresees this architecture evolving into a decentralized model capable of edge-to-edge interactions. IBM has no firm designs for this as yet. However, the plan is to make the edge infrastructure and platform a first-class citizen instead of relying on the cloud to drive what happens at the edge.

Developing a complete and comprehensive AI/ML edge architecture - and in fact, an entire ecosystem - is a massive undertaking. IBM faces many known and unknown challenges that must be solved before it can achieve success.

However, IBM is one of the few companies with the necessary partners and the technical and financial resources to undertake and successfully implement a project of this magnitude and complexity.

It is reassuring that IBM has a plan and that its plan is sound.

Paul Smith-Goodson is Vice President and Principal Analyst for quantum computing, artificial intelligence and space at Moor Insights and Strategy. You can follow him on Twitter for more current information on quantum, AI, and space.

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and tech industry analyst firms, provides or has provided paid services to technology companies. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking, and speaking sponsorships. The company has had or currently has paid business relationships with 8×8, Accenture, A10 Networks, Advanced Micro Devices, Amazon, Amazon Web Services, Ambient Scientific, Anuta Networks, Applied Brain Research, Applied Micro, Apstra, Arm, Aruba Networks (now HPE), Atom Computing, AT&T, Aura, Automation Anywhere, AWS, A-10 Strategies, Bitfusion, Blaize, Box, Broadcom, C3.AI, Calix, Campfire, Cisco Systems, Clear Software, Cloudera, Clumio, Cognitive Systems, CompuCom, Cradlepoint, CyberArk, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Dialogue Group, Digital Optics, Dreamium Labs, D-Wave, Echelon, Ericsson, Extreme Networks, Five9, Flex, Foundries.io, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Revolve (now Google), Google Cloud, Graphcore, Groq, Hiregenics, Hotwire Global, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Infinidat, Infosys, Inseego, IonQ, IonVR, Inseego, Infosys, Infiot, Intel, Interdigital, Jabil Circuit, Keysight, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, Lightbits Labs, LogicMonitor, Luminar, MapBox, Marvell Technology, Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco), Merck KGaA, Mesophere, Micron Technology, Microsoft, MiTEL, Mojo Networks, MongoDB, MulteFire Alliance, National Instruments, Neat, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek, Novumind, NVIDIA, Nutanix, Nuvia (now Qualcomm), onsemi, ONUG, OpenStack Foundation, Oracle, Palo Alto Networks, Panasas, Peraso, Pexip, Pixelworks, Plume Design, PlusAI, Poly (formerly Plantronics), Portworx, Pure Storage, Qualcomm, Quantinuum, Rackspace, Rambus, Rayvolt E-Bikes, Red Hat, Renesas, Residio, Samsung Electronics, Samsung Semi, SAP, SAS, Scale Computing, Schneider Electric, SiFive, Silver Peak (now Aruba-HPE), SkyWorks, SONY Optical Storage, Splunk, Springpath (now Cisco), Spirent, Splunk, Sprint (now T-Mobile), Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, Telesign,TE Connectivity, TensTorrent, Tobii Technology, Teradata,T-Mobile, Treasure Data, Twitter, Unity Technologies, UiPath, Verizon Communications, VAST Data, Ventana Micro Systems, Vidyo, VMware, Wave Computing, Wellsmith, Xilinx, Zayo, Zebra, Zededa, Zendesk, Zoho, Zoom, and Zscaler. Moor Insights & Strategy founder, CEO, and Chief Analyst Patrick Moorhead is an investor in dMY Technology Group Inc. VI, Dreamium Labs, Groq, Luminar Technologies, MemryX, and Movandi.

Mon, 08 Aug 2022 03:51:00 -0500 Paul Smith-Goodson en text/html https://www.forbes.com/sites/moorinsights/2022/08/08/ibm-research-rolls-out-a-comprehensive-ai-and-ml-edge-research-strategy-anchored-by-enterprise-partnerships-and-use-cases/
Killexams : Frances Allen Optimised Your Code Without You Even Knowing

In 2020, our digital world and the software we use to create it are a towering structure, built upon countless layers of abstraction and building blocks — just think about all the translations and interactions that occur from loading a webpage. Whilst abstraction is undoubtedly a great thing, it only works if we’re building on solid ground; if the lower levels are stable and fast. What does that mean in practice? It means low-level, compiled languages, which can be heavily optimised and leveraged to make the most of computer hardware. One of the giants in this area was Frances Allen, who recently passed away in early August. Described by IBM as “a pioneer in compiler organization and optimization algorithms,” she made numerous significant contributions to the field.

Early Days

Via Wikimedia

Trained as a maths teacher, Allen worked at a high school in New York for two years after graduating. She went back to complete a Masters in mathematics and was recruited by IBM Research on campus. Though planning to only stay long enough to pay off her debt and quickly return to teaching, she found herself staying with IBM for the rest of her career, even becoming the first female IBM fellow in 1989.

Allen’s first role at IBM was teaching internal engineers and scientists how to use FORTRAN — apparently a difficult sell to people who at the time were used to programming in assembly, which did just fine thank you very much. In an interview, Allen talks about the resistance from scientists who thought it wasn’t possible for a compiled language to produce code that was good enough.

The Stretch supercomputer (via IBM)

After teaching, Allen began working on the compiler for a 100 kW “supercomputer” called Stretch. With 2048 kB of memory, the goal of Stretch was to be 100 times faster than any other system available at the time. Though this ultimately failed (to the dismay of a few clients, one finding Stretch took 18 hours to produce their 24 hour weather forecast), it caught the attention of the NSA.

Because of this, IBM designed a coprocessor addon, Harvest, specifically for codebreaking at the NSA. Harvest ended up being larger than Stretch itself, and Allen spent a year leading a team inside the NSA, working on highly classified projects. The team didn’t find out many things about what they were working on until they were leaked to the press (it was spying on the Soviet Union — no prizes for guessing).

Engineers with Tractor tapes for Harvest

An engineering feat, Harvest used a unique streaming architecture for code-breaking: information loaded onto IBM Tractor tape was continuously fed into memory, processed and output in real time, with perfectly synchronised IO and operations. Harvest could process 3 million characters a second and was estimated by the NSA to be 50-200 times faster than anything else commercially available. The project was extremely successful and was used for 14 years after installation, an impressive feat given the pace of technological advancement at the time.

Speed is of the Essence

The success of the project was in large part due to Allen’s work on the optimisations performed by its compiler. Compiler optimisations are magic. Some of us think of compilers as simple “source code in, machine code out” boxes, but much of their complexity lies in the entirely automatic suite of optimisations and intermediate steps they use to ensure your code runs as swiftly as possible. Of course, this was important for the limited hardware at the time, but the techniques that Allen helped develop are present in modern compilers everywhere. The New York Times quotes Graydon Hoare (the creator of Rust and one of today’s most famed compiler experts) as saying that Allen’s work is in “every app, every website, every video game or communication system, every government or bank computer, every onboard computer in a car or aircraft”.

So what do compiler optimisations actually look like? Allen wrote many influential papers on the subject, but “A catalogue of optimizing transformations” which she co-authored with John Cocke in 1972 was particularly seminal. It aims to “systematize the potpourri of optimizing transformations that a compiler can make to a program”. It has been said that compilers that implement just the eight main techniques from this paper can achieve 80% of best-case performance. Here are some of the most basic ideas:

  • Procedure integration: replacing calls to subprocedures with inline code where possible, avoiding saving/restoring registers
  • Loop unrolling: flattening loops by writing out statements explicitly, avoiding unnecessary comparison conditions
  • CSE (common subexpression elimination): eliminating redundant computations which calculate values already available
  • Code Motion: moving subexpressions out of loops where it is safe to do so
  • Peephole optimisation: replacing known instruction combinations with more efficient variants

Some of these might seem obvious to us now, but formalising and standardising these ideas at the time had great impact.

Parallelism

Allen’s last major project for IBM was PTRAN, the Parallel Translator. This was a system for automatic parallelism, a special type of compiler optimisation. The aim was to take programs that weren’t written with parallelism in mind and translate them for execution on parallel architectures. This concept of taking sequentially written code and automatically extracting features from it to be run in parallel led to the extensive use of dependency graphs, now a standard representation in compilers. One of the recurring themes throughout Allen’s career was her ability to take highly technical problems and abstract them into maths — often graphs and sets — and solve them precisely. On this project Allen led a team of young engineers, churning out industry leading papers and compilers for parallelism for 15 years.

IBM Academy and Beyond

In 1995 Allen became president of the IBM Academy, an internal steering group of IBM’s very top technical minds. She was able to use the position to advocate in two areas: mentoring and women in technology. In interviews, she frequently talked about how she didn’t have a mentor, and how important it is for people starting out in tech. Her visibility as an expert in the field inspired others — at its peak in the 70s/80s, half of the IBM experimental compiler group were women. Her advocacy for women in tech never ceased, even as she described a drop in participation after the early days of computing:

Later, as computing emerged as a specialized field, employers began to require engineering credentials, which traditionally attracted few women. But the pendulum is swinging back as women enter the field from other areas such as medical informatics, user interfaces and computers in education.

In 2006 Allen received the Turing Award (considered the Nobel Prize for computing) — the first woman to do so.

So the next time you fire up gcc, write anything in a language above assembly, or even use any software at all, remember that Frances Allen’s ideas are invisibly at play.

Thu, 04 Aug 2022 12:00:00 -0500 Ben James en-US text/html https://hackaday.com/2020/08/25/frances-allen-optimised-your-code-without-you-even-knowing/
Killexams : 7 Quantum Computing Stocks to Buy for the Next 10 Years No result found, try new keyword!It has since been updated to include the most relevant information available.] Quantum computing ... in this emerging field — such as IBM’s (IBM) progressive 100-qubit quantum chip – are ... Fri, 08 Jul 2022 00:45:00 -0500 text/html https://www.nasdaq.com/articles/7-quantum-computing-stocks-to-buy-for-the-next-10-years Killexams : IBM Unveils $1 Billion Platform-as-a-Service Investment No result found, try new keyword!IBM says its data and analytics optimization system Power Systems ... quality of service, and high availability -- all the characteristics that enterprises need. BlueMix and the fact that we ... Fri, 22 Jul 2022 12:00:00 -0500 en-us text/html https://www.thestreet.com/technology/ibm-unveils-1-billion-platform-as-a-service-investment-12438325 Killexams : Quantum Computing Software Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2026

"IBM Corporation (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US), D-Wave Systems Inc (Canada), Rigetti Computing (US), Google LLC (US), Honeywell International Inc. (US), QC Ware (US), 1QBit (US), Huawei Technologies Co., Ltd. (China), Accenture plc (Ireland), Cambridge Quantum Computing (England), Fujitsu Limited (Japan), Riverlane (UK)."

Quantum Computing Software Market by Component (Software, Services), Deployment Mode (Cloud, On-Premises), Organization Size, Technology, Application (Optimization, Simulation), Vertical (BFSI, Government), and Region - Global Forecast to 2026

The Quantum Computing Software Market size is projected to grow from USD 0.11 billion in 2021 to 0.43 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 30.5% during the forecast period. The major factors driving the growth of the Quantum Computing Software market include the growing adoption of quantum computing software in the BFSI vertical, government support for the development and deployment of the technology, and the increasing number of strategic alliances for research and development.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=179309719

Based on Component, the service segment to grow at a higher CAGR during the forecast period

Among the component segment, the services segment is leading the quantum computing software market in 2021. The growth of the services segment can be attributed to the increasing investments by start-ups in research and development related to quantum computing technology. Quantum computing software and services are used in optimization, simulation, and machine learning applications, thereby leading to optimum utilization costs and highly efficient operations in various industries.

Based on application, the optimization segment is expected to hold the highest market size during the forecast period

The optimization segment is expected to lead the global quantum computing software market in terms of market share. Optimization problems exist across all industries and business functions. Some of these problems take too long to be solved optimally with traditional computers, where the usage of quantum computing technology is expected to be an optimum solution. Several optimization problems require a global minimal point solution. By using quantum annealing, the optimization problems can be solved earlier as compared to supercomputers.

Request trial Pages: https://www.marketsandmarkets.com/requestsampleNew.asp?id=179309719

Major Quantum Computing Software vendors include IBM Corporation (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US), D-Wave Systems Inc (Canada), Rigetti Computing (US), Google LLC (US), Honeywell International Inc. (US), QC Ware (US), 1QBit (US), Huawei Technologies Co., Ltd. (China), Accenture plc (Ireland), Cambridge Quantum Computing (England), Fujitsu Limited (Japan), Riverlane (UK), Zapata Computing (US), Quantum Circuits, Inc. (US), Quantica Computacao (India), XANADU Quantum Technologies (Canada), VeriQloud (France), Quantastica (Finland), AVANETIX (Germany), Kuano (England), Rahko (UK), Ketita Labs (Estonia), and Aliro Quantum (US). These market players have adopted various growth strategies, such as partnerships, collaborations, and new product launches, to expand have been the most adopted strategies by major players from 2019 to 2021, which helped companies innovate their offerings and broaden their customer base.

IBM was founded in 1911 and is headquartered in New York, US. It is a multinational technology and consulting corporation that offers infrastructure, hosting, and consulting services. The company operates through five major business segments: Cloud and Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing. IBM Cloud has emerged as a platform of choice for all business applications, as it is AI compatible. It is a unifying platform that integrates IBM’s capabilities with a single architecture and spans over public and private cloud platforms. With this powerful cloud platform, the company can cater to the requirements of different businesses across the globe. IBM caters to various verticals, including aerospace & defense, education, healthcare, oil & gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemical, government, manufacturing, travel & transportation, construction, and metals & mining. The company has a strong presence in the Americas, Europe, MEA, and APAC and clients in more than 175 countries. IBM is one of the major players in the quantum computing ecosystem. The company in 2016 made a quantum computer available to the public by connecting it to the cloud. In September 2019, it opened a Quantum Computation Center. The Quantum Computation Center offers about 100 IBM clients, academic institutions, and more than 200,000 registered users access to this cutting-edge technology through a collaborative effort called the IBM Q Network and Qiskit, IBM’s open-source development platform for quantum computing. Through these efforts, IBM is exploring the ways quantum computing can address the most complicated problems faced while training the workforce to use this technology.

Rigetti Computing was founded in 2013 and is headquartered in California, US. Rigetti Computing designs and manufactures superconducting quantum-integrated circuits. It develops quantum computers, as well as superconducting quantum processors that power them. The machines of the company can be integrated with any public, private, or hybrid cloud through the quantum cloud services (QCS) platform. It is a full-stack quantum computing company that provides an integrated computing environment. Rigetti Computing develops algorithms for quantum computing that focus on application areas such as machine learning, logistics, healthcare and pharmaceuticals, and chemicals. The company also delivers a set of tools, such as Quil, pyQuil, and Quilc, which help solve optimization problems.

Media Contact
Company Name: MarketsandMarkets™ Research Private Ltd.
Contact Person: Mr. Aashish Mehra
Email: Send Email
Phone: 18886006441
Address:630 Dundee Road Suite 430
City: Northbrook
State: IL 60062
Country: United States
Website: https://www.marketsandmarkets.com/Market-Reports/quantum-computing-software-market-179309719.html

 

Press Release Distributed by ABNewswire.com
To view the original version on ABNewswire visit: Quantum Computing Software Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2026

© 2022 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

Ad Disclosure: The rate information is obtained by Bankrate from the listed institutions. Bankrate cannot guaranty the accuracy or availability of any rates shown above. Institutions may have different rates on their own websites than those posted on Bankrate.com. The listings that appear on this page are from companies from which this website receives compensation, which may impact how, where, and in what order products appear. This table does not include all companies or all available products.

All rates are subject to change without notice and may vary depending on location. These quotes are from banks, thrifts, and credit unions, some of whom have paid for a link to their own Web site where you can find additional information. Those with a paid link are our Advertisers. Those without a paid link are listings we obtain to Boost the consumer shopping experience and are not Advertisers. To receive the Bankrate.com rate from an Advertiser, please identify yourself as a Bankrate customer. Bank and thrift deposits are insured by the Federal Deposit Insurance Corp. Credit union deposits are insured by the National Credit Union Administration.

Consumer Satisfaction: Bankrate attempts to verify the accuracy and availability of its Advertisers' terms through its quality assurance process and requires Advertisers to agree to our Terms and Conditions and to adhere to our Quality Control Program. If you believe that you have received an inaccurate quote or are otherwise not satisfied with the services provided to you by the institution you choose, please click here.

Rate collection and criteria: Click here for more information on rate collection and criteria.

Mon, 25 Jul 2022 07:00:00 -0500 text/html https://www.benzinga.com/pressreleases/22/07/ab28192725/quantum-computing-software-market-trends-size-share-growth-industry-analysis-advance-technology-a
Killexams : Blockchain can change healthcare for the better. Here's how

usatoday.com cannot provide a good user experience to your browser. To use this site and continue to benefit from our journalism and site features, please upgrade to the latest version of Chrome, Edge, Firefox or Safari.

Sun, 07 Aug 2022 22:00:00 -0500 en-US text/html https://www.usatoday.com/story/sponsor-story/imperium-group/2022/08/08/blockchain-can-change-healthcare-better-heres-how/10236069002/
Killexams : Quantum Computer Systems II No result found, try new keyword!In this course, students will learn to work with the IBM Qiskit software tools to write ... compiler, circuit optimization, python, qiskit, quantum algorithms, quantum technology, superposition ... Wed, 15 Dec 2021 00:48:00 -0600 text/html https://www.usnews.com/education/skillbuilder/quantum-computer-systems-ii-1_course_v1:UChicagoX+QCS12000+1T2022_verified Killexams : Servers Market Research Report 2022, Market Overview, Market Share, Product Dynamics, and Consumer Demographics Trends from 2022 to 2028

The MarketWatch News Department was not involved in the creation of this content.

Aug 08, 2022 (Reportmines via Comtex) -- Pre and Post Covid is covered and Report Customization is available.

The "Serversmarket research report" provides a detailed analysis of global market size, regional and country-level market size, segmentation market growth, Servers market share, competitive Landscape, sales analysis, the impact of domestic and global Servers market players, value chain optimization, trade regulations, latest developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace expanding, and technological innovations.

The global Servers market size is projected to reach multi million by 2028, in comparision to 2021, at unexpected CAGR during 2022-2028 (Ask for trial Report).

The Servers market is split by type into X86 Servers,Non-X86 Servers and by Application including Internet,Government,Telecommunications,Financial,Manufacturing,Traffic,Others for the period 2022 - 2028, the growth among segments provides accurate calculations and forecasts for sales by Type and by Application in terms of volume and value. This analysis can help you expand your business by targeting qualified markets.

Get trial PDF of Servers Market Analysis https://www.reportmines.com/enquiry/request-sample/1168973

The top competitors in the Servers Market, as highlighted in the report, are:

  • Dell
  • HPE
  • Inspur
  • Lenovo
  • IBM
  • Cisco
  • Huawei
  • H3C
  • SuperMicro
  • Fujitsu
  • Sugon

Purchase this report https://www.reportmines.com/purchase/1168973 (Price 3660 USD for a Single-User License)

Market Segmentation

The worldwide Servers Market is categorized on Component, Deployment, Application, and Region.

The Servers Market Analysis by types is segmented into:

  • X86 Servers
  • Non-X86 Servers

The Servers Market Industry Research by Application is segmented into:

  • Internet
  • Government
  • Telecommunications
  • Financial
  • Manufacturing
  • Traffic
  • Others

In terms of Region, the Servers Market Players available by Region are:

  • North America:
  • Europe:
    • Germany
    • France
    • U.K.
    • Italy
    • Russia
  • Asia-Pacific:
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • China Taiwan
    • Indonesia
    • Thailand
    • Malaysia
  • Latin America:
    • Mexico
    • Brazil
    • Argentina Korea
    • Colombia
  • Middle East & Africa:
    • Turkey
    • Saudi
    • Arabia
    • UAE
    • Korea

Inquire or Share Your Questions If Any Before the Purchasing This Report https://www.reportmines.com/enquiry/pre-order-enquiry/1168973

What's in the Servers Market Industry Research Report:

  • The Servers market research report includes Global & Regional market status and outlook.
  • Further, the report provides breakdown details about each region & country covered in the report. Identifying its sales, sales volume & revenue forecast with detailed analysis by types and applications.

Purchase this report https://www.reportmines.com/purchase/1168973 (Price 3660 USD for a Single-User License)

Impact Analysis of COVID 19:

COVID-19 is an incomparable global public health emergency that has affected almost every industry, and the long-term effects are projected to impact industry growth during the forecast period. The key manufacturers of this Servers market include Dell,HPE,Inspur,Lenovo,IBM,Cisco,Huawei,H3C,SuperMicro,Fujitsu,Sugon. The report delivers insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current Servers market forces, and the significant interventions of governments. Regions covered in this report are North America: United States, Canada, Europe: GermanyFrance, U.K., Italy, Russia,Asia-Pacific: China, Japan, South, India, Australia, China, Indonesia, Thailand, Malaysia, Latin America:Mexico, Brazil, Argentina, Colombia, Middle East & Africa:Turkey, Saudi, Arabia, UAE, Korea.

Get Covid-19 Impact Analysis for Servers Market research report https://www.reportmines.com/enquiry/request-covid19/1168973

Key benefits for Industry Players & Stakeholders:

The Servers market is studied in depth utilizing various approaches and analyses in this research report. It's broken down into sections to cover various areas of the market for a better understanding. To compile the information for the report, the researchers used both primary and secondary approaches. The goal of this research is to help consumers have a more informed, confident, and clear understanding of the market. The Servers market growth analysis covers development trends, competitive landscape analysis, investment plan, business strategy, opportunity, and the development status of major regions.

The Servers market research report contains the following TOC:

  • Report Overview
  • Global Growth Trends
  • Competition Landscape by Key Players
  • Data by Type
  • Data by Application
  • North America Market Analysis
  • Europe Market Analysis
  • Asia-Pacific Market Analysis
  • Latin America Market Analysis
  • Middle East & Africa Market Analysis
  • Key Players Profiles Market Analysis
  • Analysts Viewpoints/Conclusions
  • Appendix

Get a trial of TOC https://www.reportmines.com/toc/1168973#tableofcontents

Market Size and Industry Challenges Servers:

The Servers market research report covers the key players of the industry including Company Profile, Product Specifications, Production Capacity/Sales, Revenue, Price, and Gross Margin Sales with a thorough analysis of the Servers market's competitive landscape and detailed information on vendors, and comprehensive details of factors that will challenge the growth of major market vendors. The report consists of 156 pages.

Get trial PDF of Servers Market Analysis https://www.reportmines.com/enquiry/request-sample/1168973

Major reasons to purchase Servers Market Report:

  • To gain insightful analyses of the Servers market and have a comprehensive understanding of the global market and its commercial landscape.
  • Assess the production processes, major issues, and solutions to mitigate the development risk.
  • To understand the most affecting driving and restraining forces in the Servers market and their impact on the global market.
  • Learn about the market strategies that are being adopted by leading respective organizations.

Purchase this report https://www.reportmines.com/purchase/1168973 (Price 3660 USD for a Single-User License)

Contact Us:

Name: Aniket Tiwari

Email: sales@reportmines.com

Phone: USA:+1 917 267 7384 / IN:+91 777 709 3097

Website: https://www.reportmines.com/

Report Published by: ReportMines

More Reports Published By Us:

Global 2K Non-Isocyanate Resin Market Growth 2022-2028

Global Silicon Photodiodes Market Growth 2022-2028

Global Shotcrete Machines Market Growth 2022-2028

Global Vegan Chocolate Market Growth 2022-2028

Source: LPI

Press Release Distributed by Lemon PR Wire

To view the original version on Lemon PR Wire visit Servers Market Research Report 2022, Market Overview, Market Share, Product Dynamics, and Consumer Demographics Trends from 2022 to 2028

COMTEX_411845167/2788/2022-08-08T10:01:58

The MarketWatch News Department was not involved in the creation of this content.

Mon, 08 Aug 2022 02:01:00 -0500 en-US text/html https://www.marketwatch.com/press-release/servers-market-research-report-2022-market-overview-market-share-product-dynamics-and-consumer-demographics-trends-from-2022-to-2028-2022-08-08
Killexams : Quantum Computing Software Market Trends, Size, Share, Growth, Industry Analysis, Advance Technology and Forecast 2026

The MarketWatch News Department was not involved in the creation of this content.

Jul 25, 2022 (AB Digital via COMTEX) -- The Quantum Computing Software Market size is projected to grow from USD 0.11 billion in 2021 to 0.43 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 30.5% during the forecast period. The major factors driving the growth of the Quantum Computing Software market include the growing adoption of quantum computing software in the BFSI vertical, government support for the development and deployment of the technology, and the increasing number of strategic alliances for research and development.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=179309719

Based on Component, the service segment to grow at a higher CAGR during the forecast period

Among the component segment, the services segment is leading the quantum computing software market in 2021. The growth of the services segment can be attributed to the increasing investments by start-ups in research and development related to quantum computing technology. Quantum computing software and services are used in optimization, simulation, and machine learning applications, thereby leading to optimum utilization costs and highly efficient operations in various industries.

Based on application, the optimization segment is expected to hold the highest market size during the forecast period

The optimization segment is expected to lead the global quantum computing software market in terms of market share. Optimization problems exist across all industries and business functions. Some of these problems take too long to be solved optimally with traditional computers, where the usage of quantum computing technology is expected to be an optimum solution. Several optimization problems require a global minimal point solution. By using quantum annealing, the optimization problems can be solved earlier as compared to supercomputers.

Request trial Pages: https://www.marketsandmarkets.com/requestsampleNew.asp?id=179309719

Major Quantum Computing Software vendors include IBM Corporation (US), Microsoft Corporation (US), Amazon Web Services, Inc. (US), D-Wave Systems Inc (Canada), Rigetti Computing (US), Google LLC (US), Honeywell International Inc. (US), QC Ware (US), 1QBit (US), Huawei Technologies Co., Ltd. (China), Accenture plc (Ireland), Cambridge Quantum Computing (England), Fujitsu Limited (Japan), Riverlane (UK), Zapata Computing (US), Quantum Circuits, Inc. (US), Quantica Computacao (India), XANADU Quantum Technologies (Canada), VeriQloud (France), Quantastica (Finland), AVANETIX (Germany), Kuano (England), Rahko (UK), Ketita Labs (Estonia), and Aliro Quantum (US). These market players have adopted various growth strategies, such as partnerships, collaborations, and new product launches, to expand have been the most adopted strategies by major players from 2019 to 2021, which helped companies innovate their offerings and broaden their customer base.

IBM was founded in 1911 and is headquartered in New York, US. It is a multinational technology and consulting corporation that offers infrastructure, hosting, and consulting services. The company operates through five major business segments: Cloud and Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing. IBM Cloud has emerged as a platform of choice for all business applications, as it is AI compatible. It is a unifying platform that integrates IBM’s capabilities with a single architecture and spans over public and private cloud platforms. With this powerful cloud platform, the company can cater to the requirements of different businesses across the globe. IBM caters to various verticals, including aerospace & defense, education, healthcare, oil & gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemical, government, manufacturing, travel & transportation, construction, and metals & mining. The company has a strong presence in the Americas, Europe, MEA, and APAC and clients in more than 175 countries. IBM is one of the major players in the quantum computing ecosystem. The company in 2016 made a quantum computer available to the public by connecting it to the cloud. In September 2019, it opened a Quantum Computation Center. The Quantum Computation Center offers about 100 IBM clients, academic institutions, and more than 200,000 registered users access to this cutting-edge technology through a collaborative effort called the IBM Q Network and Qiskit, IBM’s open-source development platform for quantum computing. Through these efforts, IBM is exploring the ways quantum computing can address the most complicated problems faced while training the workforce to use this technology.

Rigetti Computing was founded in 2013 and is headquartered in California, US. Rigetti Computing designs and manufactures superconducting quantum-integrated circuits. It develops quantum computers, as well as superconducting quantum processors that power them. The machines of the company can be integrated with any public, private, or hybrid cloud through the quantum cloud services (QCS) platform. It is a full-stack quantum computing company that provides an integrated computing environment. Rigetti Computing develops algorithms for quantum computing that focus on application areas such as machine learning, logistics, healthcare and pharmaceuticals, and chemicals. The company also delivers a set of tools, such as Quil, pyQuil, and Quilc, which help solve optimization problems.

Media Contact
Company Name: MarketsandMarkets(TM) Research Private Ltd.
Contact Person: Mr. Aashish Mehra
Email: Send Email
Phone: 18886006441
Address:630 Dundee Road Suite 430
City: Northbrook
State: IL 60062
Country: United States
Website: https://www.marketsandmarkets.com/Market-Reports/quantum-computing-software-market-179309719.html

COMTEX_410933549/2555/2022-07-25T14:28:37

Is there a problem with this press release? Contact the source provider Comtex at editorial@comtex.com. You can also contact MarketWatch Customer Service via our Customer Center.

The MarketWatch News Department was not involved in the creation of this content.

Mon, 25 Jul 2022 06:28:00 -0500 en-US text/html https://www.marketwatch.com/press-release/quantum-computing-software-market-trends-size-share-growth-industry-analysis-advance-technology-and-forecast-2026-2022-07-25
M8010-663 exam dump and training guide direct download
Training Exams List