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What is cloud computing? | Google Cloud

In cloud computing, the capital investment in building and maintaining data centers is replaced by consuming IT resources as an elastic, utility-like service from a cloud provider (including storage, computing, networking, data processing and analytics, application development, machine learning, and even fully managed services).

Whereas in the past cloud computing was considered the province of startups and aggressively visionary enterprise users, today, it is part of the enterprise computing mainstream across every industry, for organizations of any type and size.

Cloud computing has made a profound impact on innovation and the economics of business overall. It gives forward-looking organizations the opportunity to not only improve flexibility, reduce costs, and focus on core competencies, but also to fully transform how they operate for example, by re-designing internal workflows or customer interactions as digital experiences that extend from the data center all the way through to mobile devices.

Specifically, the business advantages of cloud computing include:

Although some companies have lifted and shifted their entire infrastructure to the cloud as part of a full digital transformation, most will choose to take a gradual approach that presumes a hybrid environment. For that reason, its important for your cloud provider to support integration with on-premises systems via standard connectors and interfaces, in addition to open frameworks and APIs that help make customer applications portable to other platforms (whether on-premises or cloud-based).

For those organizations taking a gradual approach to a cloud migration, there are a few use cases that present good opportunities for initial success:

With your use case(s) identified, it will be important to identify your preferred method of storage, model your costs, and determine whether you will migrate your data via self-service or with the help of a vendor.

Initially, cloud computing was premised on running IT infrastructure in a more flexible, more cost-effective way. In contrast, the next wave of cloud computing is about helping customers forget about the existence of that infrastructure entirely (aka serverless computing), thereby freeing them to unlock full digital business transformation.

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Artificial Intelligence in Healthcare: the future is amazing …

The role of artificial intelligence in healthcare has been a huge talking point in recent months and theres no sign of the adoption of this technology slowing down, well, ever really.

AI in healthcare has huge and wide reaching potential with everything from mobile coaching solutions to drug discovery falling under the umbrella of what can be achieved with machine learning.

That being said, many healthcare executives are still too shy when it comes to experimenting with AI due to privacy concerns, data integrity concerns or the unfortunate presence of various organizational silos making data sharing next to impossible. Weve covered the main barriers to adopting AI in healthcare here.

However, the future of healthcare & the future of machine learning and artificial intelligence are deeply interconnected.

Following our comprehensive guides on Artificial Intelligence in Pharma and Blockchain in Healthcare, weve decided to take a closer look at how the healthcare industry is positively impacted by the rise in popularity of artificial intelligence.

But first, a definition:

Artificial intelligence in healthcare refers to the use of complex algorithms designed to perform certain tasks in an automated fashion. When researchers, doctors and scientists inject data into computers, the newly built algorithms can review, interpret and even suggest solutions to complex medical problems.

Applications of Artificial Intelligence in healthcare are endless. That much we know.

We also know that weve only scratched the surface of what AI can do for healthcare. Which is both amazing and frightening at the same time.

At the highest level, here are some of the current technological applications of AI in healthcare you should know about (some will be explored further in the article while some use cases have gotten their own standalone articles on HealthcareWeekly already).

Medical diagnostics: the use of Artificial Intelligence to diagnose patients with specific diseases. Check out our roundup report from industry experts here. Also, a report AI platform was announced in March 2019 which is expected to help identify and anticipate cancer development.

Drug discovery: There are dozens of health and pharma companies currently leveraging Artificial Intelligence to help with drug discovery and improve the lengthy timelines and processes tied to discovering and taking drugs all the way to market. If this is something youre interested in, check our report titled Pharma Industry in the Age of Artificial Intelligence: The Future is Bright.

Clinical Trials: Clinical Trials are, unfortunately, a real mess. Most clinical trials are managed offline with no integrated solutions that can track progress, data gathering and drug tria outcomes. Read about how Artificial Intelligence is reshaping clinical trials here. Also, you may also be interested in the Healthcare Weekly podcast episode with Robert Chu, CEO @ Embleema where we talk about how Embleema is using AI and blockchain to revolutionize clinical trials. If Blockchain in healthcare is your thing, you may also be interested in our Global Blockchain in Healthcare Report: the 2019 ultimate guide for every executive.

Pain management: This is still an emergent focus area in healthcare. As it turns out, by leveraging virtual reality combined with artificial intelligence, we can create simulated realities that can distract patients from the current source of their pain and even help with the opioid crisis. You can read more about how this works here. Another great example of where AI and VR meet is the Johnson and Johnson Reality Program which weve covered at length here. In short, J&J has created a simulated environment which used rules-based algorithms to train physicians in a simulated environment to get better at their job.

Improving patient outcomes: Patients outcomes can be improved through a wide variety of strategies and outcomes driven by artificial intelligence. To begin with, check our report on 10 ways Amazons Alexa is revolutionizing healthcare and our Healthcare Weekly Podcast with Helpsys CEO Sangeeta Agarwal. Helpsy has developed the first Artificial Intelligence nurse in the form of a chatbot which assists patients at every stage of the way in their battle with cancer.

These are just a few examples and theyre only meant to quickly give you a flavor of what artificial intelligence in healthcare is all about. Lets dig into more specific examples that every healthcare executive should be aware of in 2019.

Artificial intelligence in the medical field relies on the analysis and interpretation of huge amounts of data sets in order to help doctors make better decisions, manage patient data information effectively, create personalized medicine plans from complex data sets and discover new drugs.

Lets look at each of these amazing use-cases in more details.

AI in healthcare can prove useful within clinical decision support to help doctors make better decisions faster with pattern recognition of health complications that are registered far more accurately than by the human brain.

The time saved and the conditions diagnosed are vital in an industry where the time taken and decisions made can be life-altering for patients.

AI in healthcare is a great addition to the information management for both physician and patient. With patients getting to doctors faster, or not at all when telemedicine is employed, valuable time and money are saved, taking the strain off of healthcare professionals and increasing comfort of patients.

Doctors can also further their learning and increase their abilities within the job through AI-driven educational modules, further showing the information management capabilities of AI in healthcare.

Around $5bn was invested into AI companies in 2016 and its no surprise that healthcare is up there with one of the fastest growing sectors. The healthcare industry is expected to get more than $6.6bn in investments by 2021.

There are 4 main machine learning initiatives within the top 5 pharmaceutical and biotechnology companies ranging from mobile coaching solutions and telemedicine to drug discovery and acquisitions.

Mobile coaching solutions come in the form of advising patients and improving treatment outcomes using real-time data collection. Theres a huge push in telemedicine in recent years too with companies employing AI for minor diagnosis within smartphone apps.

The ability to analyze large amounts of patient data to identify treatment options. The technology is able to identify treatment options through cloud-based systems able to process natural language.

Acquisitions continue to feed to innovation needs of both large and old biotech firms and with the development of AI, theres plenty to offer up when it comes to company control.

With startups combining the world of AI and healthcare, theres more choice for older and larger companies to acquire information, systems and even the people responsible for leaps and bounds in technology.

Drug discovery is another great place for AI to slip in with pharma companies able to include cutting-edge technology into the expensive and lengthy process of drug discovery.

The benefits of AI are instantly apparent with the focus on time-saving and pattern recognition upon testing and identification of new drugs.

In early-stage drug discovery, start-ups such as BenevolentAI or Verge Genomics are known to adopt algorithms which comb through portions of data for patterns too complex for humans to identify, saving both time and innovating in a way that we otherwise may not have been able to.

Insilico, another company with a heavy AI focus, has taken a different approach by using AI to design treatments not yet found in either nature of chemical libraries. An approach of using AI to simulate clinical trials before human trials have also been seen, leaving plenty of scope available for what AI can create.

For more information regarding how AI used in pharma, click here.

Growth opportunities may be hard to come by without significant investment from companies, but a major opportunity exists in the self-running engine for growth within the artificial intelligence sector of healthcare.

AI applications within the healthcare industry have the potential to create $150 billion in savings annually for the United States, a recent Accenture study estimates, by 2026. With AI in healthcare funding reading historic highs of $600m in equity funding (Q218) there are huge projected equity funding deals and equity deals as the years continue.

We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10 Bill Gates

Saliently, AI represents a significant opportunity for bottom line growth with the introduction of AI into the healthcare sector, with a combined expected 2026 value of $150bn:

The growth, however, is not unexpected and with the needs of the healthcare industry of which AI fits the gap its a match made in heaven.

With the predicted 2026 value of robot-assisted surgery, virtual nursing assistants and administrative workflow assistance are expected to be valued at $40bn, $20bn and $18bn respectively, its the numbers that come with claims that are the most impressive.

Although AI in healthcare has huge potential, as with most developments in the technological space, there are a number of known current limitation.

Experiencing teething problems with the introduction of any new technology is not rare, but must be overcome for large scale adoption of AI to occur in the healthcare market.

Ultimately, the adoption of AI will attract stakeholders who will invest in AI and successful case studies need to be highlighted and presented for future encouragement. These case studies will require some early adopters of healthcare companies to kickstart the process.

Privacy within healthcare is, by nature, extremely sensitive and thus confidential.

For utmost confidence in the technology, systems should be put in place to ensure data privacy and protection from hackers. Unfortunately, data breaches continue to be a common occurrence as reported before when UW Medicine exposed 1 million patient records or with Missouri Medicaid.

But privacy concerns should not be a deterrent from adopting artificial intelligence in the healthcare space. In fact, last year we did a story on how Artificial Intelligence can actually help healthcare data security.

HIPAA and a number of other patient data laws are subject to the approval of governing organizations e.g. FDA to ensure that federal standards are maintained.

The sharing of data among a variety of databases poses challenges to the HIPAA compliance and care must be taken around these areas if future developments are to succeed. As companies developing software, therefore AI, are also required to comply with Hitrust rules, current rules are regulations are definitely known to be a barrier to AI adoption.

Deep learning, AI and machine learning do not have the ability to ask the question why?. As a result, the logic behind decisions is not justified, meaning mostly guesswork is required to how the decision was made.

How and why the decision has been made is key to the information within the treatment plan. With a lack of reasoning can come a lack of confidence within the decision, potentially rendering the technology as unreliable or untrustworthy by both patients and professionals.

When it comes to the stakeholders within the adoption of AI in healthcare, everyone, including patients, insurance companies, pharma companies, healthcare workers etc. are key.

Resistance to pursue the technology at any of the aforementioned levels would result in issues and potential failure to the incorporation of the technology in the macro. Stakeholdering is one of the top ten reasons why the healthcare industry as a whole is not innovating enough in 2019.

Diagnostic errors account for 60% of all medical errors and an estimated 40,000 to 80,000 deaths each year. As a result, artificial intelligence has been employed in a variety of different areas in a bid to reduce the toll and number of errors made by human judgement.

That said, there continues to be significant pushback when it comes to AI adoption in the clinical decision support process as scientists and medical personnel continue to approach the topic of AI with incredible caution.

With minimal operator training needed and design with common output formats that directly interface with other medical software and health record systems, the system is incredibly easy to use and simple to implement.

A clear output from the system allows 60 seconds to identify whether the exam quality was of sufficient quality, the patient is negative for referable DR or the patient has signs of referable DR. Following signs of referable DR, further action in the form of a human grader over-reading, teleconsultation and/or referral to an ophthalmologist may be suggested.

Despite some setbacks and limitations, Artificial Intelligence in healthcare are virtually announced every day. In this section, we will cover some of the most remarkable and revolutionary uses of AI in healthcare with an understanding that this list is by no means complete and definitely a work in progress.

With the launch of the Apple Watch Series 4 and the new electrodes found within the gadget, its now possible for users to take an ECG directly from the wrist.

The Apple Watch Series 4 is the very first direct-to-consumer product that enables users to get a electrocardiogram directly from their wrist. The app that permits the readings provides vital data to physicians that may otherwise be missed. Rapid and skipped heartbeats are clocked and users will now receive a notification if an irregular heart rhythm (atrial fibrillation) is detected.

The number of accessories and add ons that technology companies are releasing for the Apple Watch is also beginning to crossover into the health industry. Companies, such as AliveCor have released custom straps that allow a clinical grade wearable ECG that replaces the original Apple Watch band. Although the strap may be rendered useless with the Series 4, for any of the earlier watches, the strap may prove a useful attachment to identify AF.

In addition, earlier this year, Omron Healthcare made the news when they deployed a new smart watch, called Omron HeartGuide. The watch can take a users blood pressure on the go while interpreting blood pressure data to provide actionable insights to users on a daily basis.

Last year, Fitbit released their signature Charge 3 wristband which uses Artificial Intelligence to detect sleep apnea.

What all these examples have in common is how wearable technologies are slowly being repurposed or augmented to improve medical outcomes. And in all these examples, artificial intelligence is leveraged, under the hood, to collect, analyze and interpret massive amounts of data which can improve the quality of life of patients everywhere.

Late 2018 marked the announcement from Aidoc that it had been granted its U.S. FDA clearance of its first AI-based workflow solution, the diagnosis of bleeds on the brain.

The systems created work with radiologists to flag acute intracranial haemorrhage (ICH), or bleeds on the brain, in CT scans. With over 75 percent of all patient care involving cardiovascular diseases, the workload on radiologists is massive.

Integration into the health industry is simple and wont require significant IT time and with additional hardware not required, its a simple resource that can be set up and maintained remotely. With a solution to assist workflow optimizations and increase the number of correct and high-quality scans, the demand for this AI-enabled technology is expected to be huge.

IDxhas developed an AI diagnostic system, IDx-DR, that autonomously analyzes images of the retina for signs of diabetic retinopathy. The software has received FDA approval to be used in the US.

1. Using a fundus camera, a trained operator captures two-color, 45 FOV images per eye

2. The images are transferred to the IDx-DR client on a local computer

3. The images are then submitted to the IDx-DR analysis system

4. Inside 60 seconds, IDx-DR provides an image quality or disease output and follow-up care instructions

5. If negative for mtmDR, the patient can be rescreened at a later date. If positive for mtmDR, refers the patient to eye care.

iCAD announced the launch of iReveal back in 2015 with the goal to monitor breast density via mammography to support accurate decisions in breast cancer screening.

With an estimated 40% of women in the US having dense breast tissue that can block the mammography from viewing potential cancerous tissue, the issue is huge and a solution was imperative.

The technology uses AI to assess breast density in order to identify patients that may experience reduced sensitivity to digital mammography due to dense breast tissue.

Ken Ferry, CEO of iCAD stated that With iReveal, radiologists may be better able to identify women with dense breasts who experience decreased sensitivity to cancer detection with mammography.

Mr. also Ferry added that The increasing support for the reporting of breast density across the US, there is a significant opportunity to drive adoption of iReveal by existing users of the PowerLook AMP platform and with new customers, which represents an incremental $100 million market opportunity over the next few years. Longer-term, we plan to integrate the iReveal technology into our Tomosynthesis CAD product, which is the next large growth opportunity for our Cancer Detection business.

Ultimately, the system remains at the forefront of breast cancer identification in women in the U.S. and with so many lives expected to be saved, I think everyone can agree what a fantastic use of AI it is.

QuantX is the first MRI workstation to provide a true computer-aided diagnosis, delivering an AI-based set of tools to help radiologists in assessment and characterization of breast abnormalities.

Using MR image data, QuantX uses a deep database of known outcomes and combines this with advanced machine learning and quantitative image analysis for real-time analytics during scans. A fast comprehensive display is seen with all processing on-demand in real-time with rapid display and reformatting of MPR, full MIPs, thin MIPs and subtractions.

A QI Score, a clinical metric correlated to the likelihood of malignancy is calculated with the images and regions of interest during scans. This is paired with a similar case compare, a tool which allows up to 45 similar cases from a reference library to be displayed for each analyzed lesion.

This information is passed on to radiologists to make accurate clinical decisions, decreasing the number of incorrect diagnoses in high-risk environments.

Coronary calcium scoring is a biomarker of coronary artery disease and quantification of this coronary calcification is a very strong predictor for cardiovascular events, including heart attacks or strokes.

A conventional coronary calcium scoring requires dedicated cardiac, ECG gated CT performed with and without contrast.

However, in recent times, a reliable derivation of coronary calcium score has been found algorithmically with the use of AI from low dose chest CT data. Zebra Medicals scoring algorithm uses these standard, non-contrast Chest CTs and automatically calculates the Coronary Calcium Scores.

The tool is vital for the early detection of people at high risk of severe cardiovascular events that otherwise would not be aware of the risk without extensive testing.

San Francisco-based privately held company Bay Labs gained FDA approval in June 2018 for the fully automated and AI-based selection of the left ventricular ejection fraction (EF). Note that Healthcare Weekly has included Bay Labs is our list of the most promising healthcare startups to watch for in 2019.

With EF noted as the single most widely used metric of cardiac function, used as the basis for numerous clinical decisions, Bay Labs AI based EchoMD and AutoEF algorithms work to reduce the errors and minimise workflow that surrounds the industry. The algorithms eliminate the need to manually select views, choose the best clips, and manipulate them for quantification, which is often noted as a particularly time-consuming and highly variable process.

The algorithms automatically review all relevant information and digital clips from a patients echocardiography study and proceeds to rate accordingly with image quality as the focus criteria. What may be most impressive about Bay Labs artificial intelligence solution is the method that the system learned clip selection in which over 4 million images were used to maximise algorithm success.

Ultimately, EchoMD and AutoEF will strive to maximise workflow efficiency while reducing the error in clinical decision making by helping physicians make correct choices.

Neural Analytics, a medical device company tackling brain health, announced a device for paramedic stroke diagnosis back in 2017, revolutionising the way that paramedics diagnose stroke victims.

Neural Analytic Lucid M1 Transcranial Doppler Ultrasound System tackles the issues of expensive and time-consuming stroke diagnosis for patients that suffer blood flow disorders.

This ultrasound system is designed for measuring cerebral blood flow velocities. This is no joke. Is successful, this technology will change how early doctors can detect stroke and could drastically improve patient outcomes.

The use of Transcranial Doppler (TCD), a type of ultrasound, allows for AI to assess the brains blood vessels from outside the body, preventing the need for more invasive tests. The AI software helps physicians detect stroke and other brain disorders caused by blood flow issues, increasing the capability of correct clinical decisions.

Icometrix is a company with the mission to transform patient care through imaging AI. With MRI brain interpretation used to decrease error in clinical diagnosis, the company is well on the way to changing the way that abnormalities are discovered within the brain.

The system developed objectively quantifies brain white matter abnormalities in patients, decreasing the amount of time taken, increasing the accuracy and improving patient care for those with brain issues. Changes in the brain are confidently evaluated with a focus on the structure with utmost accuracy. The system allows an increased sensitivity and augmented detection, ultimately leading to improved healthcare.

With quantification of clinically relevant brain structures for individual patients and a range of identifiable neurological disorders, theres plenty that AI had to offer in the space.

The OsteoDetect software is an AI based detection/diagnostic software that utilises intelligent algorithms that analyze two-dimensional X-rays.

The software searches for damage in the bone, specifically a common wrist fracture called the distal radius fracture. The software utilises the machine learning techniques to identify these problem areas and mark the location of the fracture on the image, assisting the physician with identification of a break.

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10 Best Artificial Intelligence Course & Certification [2019 …

Our global team of experts have done extensive research to come up with this list of 14 Best +Free Artificial Intelligence Courses, Tutorial, Training and Certifications available online for 2019. These are relevant for beginners, intermediate learners as well as experts.

If learning Machine Learning is on your mind, then there is no looking further. Created by Andrew Ng, Professor at Stanford University, more than 1,680,000 students & professionals globally have enrolled in this program, who have rated it very highly. This course provides an introduction to the core concepts of this field such as supervised learning, unsupervised learning, support vector machines, kernel, and neural networks. Draw from numerous case studies and applications and get hands-on to apply the theoretical concepts to practice. By the end of the classes, you will have the confidence to apply your knowledge to real-life scenarios.You may also like to have a look at some of the best machine learning courses.

Key USPs-

Understand parametric and non-parametric algorithms, clustering, dimensionality reduction among other important topics.

Gain best practices and advice from the instructor.

Interact with your peers in a community of like-minded learners from all levels of experience.

Real world based case studies give you the opportunity to understand how problems are solved on a daily basis.

The flexible deadline allows you to learn as per your convenience.

Learn to apply learning algorithms to build smart robots, understand text, audio, database mining.

Duration : 55 hours

Rating : 4.9 out of 5

You can Sign up Here

Review :This course provides a thorough, end-to-end immersion into the world of machine learning. Not only does it cover clear explanations of theory, but it also highlights practical pointers and words of caution. Highly recommended course.

This course is created for individuals who are looking forward to learning about strategies and techniques of artificial intelligence to solve business problems. After the fundamental topics are discussed you will go over how AI is impacting different industries as well as the various tools that are involved in the operations for developing efficient solutions. By the end of the program you will have numerous strategies under your belt that can be used to improve the performance of your organization.

Key USPs

Learn to manage customer expectations and develop AI models accordingly.

Multiple case studies that allow you to get a better understanding of the challenges faced in the real world.

Get answers to your queries from a dedicated support team.

Complete the exercises and get feedback on your performance.

Work with real-life based data.

Pass the exam with at least 80% to earn the certification.

Duration: 2 months, 4 to 6 hours per week

Rating: 4.5 out of 5

You can Sign up Here

If you want to jumpstart a career in AI then this specialization will help you achieve that. Through this array of 5 courses, you will explore the foundational topics of Deep Learning, understand how to build neural networks, and lead successful ML projects. Along with this, there are opportunities to work on case studies from various real-world industries. The practical assignments will allow you to practice the concepts in Python and in Tensorflow. Additionally, there are talks from top leaders in the field that will give you motivation and help you to understand the scenarios in this line of work.In case you are interested, you may also want to check out Best Python Courses.

Key USPs-

Learn about convolutional networks, RNNs, BatchNorm, Dropout and more.

The lessons will help you to learn different techniques using which you can build models to solve real-life problems.

Real-world case studies in fields such as healthcare, autonomous driving, sign language reading, music generation, and natural language processing are covered.

Gain best practices and advice from the industry experts and leaders.

Complete all the assessments and assignments as per your schedule to earn the specialization completion certification.

Duration: 3 months, 11 hours per week

Rating : 4.7 out of 5

You can Sign up Here

Review :Course content is very good. Andrew Ngs style of teaching is phenomenal. He has a knack for uncomplicating an otherwise complex subject matter. Highly recommended for anyone who is trying to understand the fundamentals of neural networks and deep learning.

Artificial intelligence is considered to be one of the more complex topics in technology but its use in our daily lives cannot be overstated. So if you want your organization to become better at using this technology then this program is worth a look. In the classes, you will learn the meaning behind basic and crucial terminologies, what AI can do and cannot do, spot opportunities to apply AI solutions to problems in your organization and more. By the end of the lectures, you will be proficient in the business aspects of AI and apply them aptly in relevant situations. The course is created by Andrew Ng, the pioneer in the field of artificial intelligence, and the founder of Coursera.

Key USPs-

Understand what it is like to build machine learning and data science projects.

Work with an artificial intelligence team and build a strategy in your company.

Navigate ethical and societal discussions surrounding this field.

The lessons do not require any prerequisites, hence it can be taken by anyone with any level of experience.

The deadlines of the classes can be adjusted as per your convenience.

Duration: 4 weeks, 2 to 3 hours per week

Rating: 4.9 out of 5

You can Sign up Here

Review :Its a fantastic course by Andrew. Everyone should take it to understand how AI can impact any software system.

Enroll in this certification to gain expertise in one of the fastest growing areas of computer science through a series of lectures and assignments. The classes will help you to get a solid understanding of the guiding principles of artificial intelligence. With equal emphasis on theory and practical, these lessons will teach you to deal with real-world problems and come up with suitable AI solutions. With this credential in your bag, it is safe to say that you will have an upper hand at job interviews and other opportunities. Dont forget checking list of best Deep Learning Courses out there.

Key USPs-

The videos guide you through all the fundamental concepts beginning from the basic topics to more advanced ones.

Apply the concepts of machine learning to real-life challenges and applications.

Thorough instructions are provided for configuring and navigating through the required software.

Working on designing and harnessing the capabilities of the neural network.

The classes are divided into 4 parts along with relevant examples and demonstrations.

Apply the knowledge gained in these lectures in an array of fields such as robotics, vision and physical simulations.

Duration: 12 weeks per course, 8 to 10 hours per week, per course

Rating : 4.5 out of 5

You can Sign up Here

Offered by IBM, this introductory course will guide you to the basics of artificial intelligence. With this course, you will learn what AI is and how it used in the software or app development industry. During the course, you will be exposed to various issues and concerns that surround artificial intelligence like ethics and bias, and jobs. After completing the course, you will also demonstrate AI in action with a mini project that is designed to test your knowledge of AI. Moreover, after finishing the project, you will also receive your certificate of completion from Udacity.

Key USPs

Learn and understand AI concepts and useful terms like machine learning, deep learning, and neural networks

No prior knowledge of programming or computer science required to enroll in this course

Get advice from experts about learning artificial intelligence better and how to start a career in this growing field

Be eligible to enter into other classes and programs like AI Foundations, IBM Applied AI professional certificate after finishing this course

100% flexible course with no deadlines and freedom to study from your own pace

Duration: 4 weeks, 1-2 hours/week

Rating: 4.7 out of 5

You can Sign up Here

This program is designed with the focus to help you gain the skills needed to build deep learning predictive models for AI. While you are free to take the lessons in any order it is advised to follow along with the suggested format so that you can develop your knowledge with gradually advanced concepts. After the completion of the first eight mandatory courses, you can choose from four options for the ninth one prior to getting started with the capstone project.

Key USPs-

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WPMU DEV Hosting Review Managed WordPress Cloud Hosting

WPMU DEV Hosting$49

WPMU DEV is a popular WordPress platform where you can manage your WordPress sites, get support and get plugins. You can also host your sites on the recently introduced WPMU DEV Hosting platform. Read our review of WPMU DEVs hosting here.

Youve probably already heard about them. Youve either already heard about their maintenance hub, one of their plugins, their support forums, or one of their extensive blog posts. Theyre a popular platform and already well-known among the WordPress community. The plugin portfolio by WPMU DEV includes plugins that are some of the most used plugins in WordPress overall, which include an SEO plugin, an image optimization plugin, and more. They have a hub where you can manage any WordPress sites, hosted at any other host. Take backups, update plugins and themes, track uptime, and more. You dont even have to use their own hosting platform to use the hub.

One of their recently introduced membership features is the WPMU DEV WordPress hosting. If youre a member, you can host 3 sites at their platform for free.

Well go in detail and review their hosting platform below. We wont focus much on the hub or other features, there are plenty of other good reviews about them.

The hosting dashboard (control panel/hub) is one of the easiest and most user-friendly platforms weve come across. You can spin up a new site in seconds. Everything you need to manage can be done with a couple of clicks, including changing your PHP version, taking backups, creating SFTP/SSH accountsYou can even analyze your sites analytics right from the hub.

Their hosting platform is pretty feature-rich. Well go over some of the features:

The WPMU DEV membership starts at $49 per month and includes all their plugins, the site management hub, and 3 free Bronze sites.

After the first 3 sites, the sites will be pilled per month, per site. Theres a 30-day free trial when you first sign up.

As per usual with other hosts, we did a simple speed test on a default WordPress site without making any changes.

The homepage fully loaded in 1.2 seconds, which is a great result, better than most other hosts.

You can view the full test results here.

If you get a better plan or tweak the image compression, CDN, and caching more, youll get better results. WPMU DEV has great tutorials on their site.

Here are the pros and cons of the WPMU DEV hosting platform, in short:

Pros:

Cons:

All in all theyre a great option. Easy to use, fast cloud hosting with lots of features, especially if you decide to use their hub with all their plugins. Everything just fits and works perfectly. They arent the cheapest though, so if you expect quality support with lots of features, youve got to pay for it.

Bear in mind that this is still a fairly new hosting platform so you may notice some oddities here and there. Make sure to report them.

Theyre always improving and they always accept feedback and feature requests from the community, so you can expect more features and improvements soon.

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Securing Your Wireless Network | FTC Consumer Information

Todays home network may include a wide range of wireless devices, from computers and phones, to IP Cameras, smart TVs and connected appliances. Taking basic steps to secure your home network will help protect your devices and your information from compromise.

Going wireless generally requires connecting an internet "access point" like a cable or DSL modem to a wireless router, which sends a signal through the air, sometimes as far as several hundred feet. Any device within range can pull the signal from the air and access the internet.

Unless you take certain precautions, anyone nearby can use your network. That means your neighbors or any hacker nearby could "piggyback" on your network or access information on your device. If an unauthorized person uses your network to commit crime or send spam, the activity could be traced back to your account.

Once you go wireless, you should encrypt the information you send over your wireless network, so that nearby attackers cant eavesdrop on these communications. Encryption scrambles the information you send into a code so that its not accessible to others. Using encryption is the most effective way to secure your network from intruders.

Two main types of encryption are available for this purpose: Wi-Fi Protected Access (WPA) and Wired Equivalent Privacy (WEP). Your computer, router, and other equipment must use the same encryption. WPA2 is strongest; use it if you have a choice. It should protect you against most hackers. Some older routers use only WEP encryption, which likely wont protect you from some common hacking programs. Consider buying a new router with WPA2 capability.

Wireless routers often come with the encryption feature turned off. You must turn it on. The directions that come with your router should explain how. If they don't, check the companys website.

Allow only specific devices to access your wireless network. Every device that is able to communicate with a network is assigned a unique Media Access Control (MAC) address. Wireless routers usually have a mechanism to allow only devices with particular MAC addresses to access to the network. Some hackers have mimicked MAC addresses, so don't rely on this step alone.

Its also important to protect your network from attacks over the internet by keeping your router secure. Your router directs traffic between your local network and the internet. So, its your first line of defense for guarding against such attacks. If you don't take steps to secure your router, strangers could gain access to sensitive personal or financial information on your device. Strangers also could seize control of your router, to direct you to fraudulent websites.

Change the name of your router from the default. The name of your router (often called the service set identifier or SSID) is likely to be a standard, default ID assigned by the manufacturer. Change the name to something unique that only you know.

Change your router's pre-set password(s). The manufacturer of your wireless router probably assigned it a standard default password that allows you to set up and operate the router, as its administrator. Hackers know these default passwords, so change it to something only you know. The same goes for any default user passwords. Use long and complex passwords think at least 12 characters, with a mix of numbers, symbols, and upper and lower case letters. Visit the companys website to learn how to change the password.

Turn off any Remote Management features. Some routers offer an option to allow remote access to your routers controls, such as to enable the manufacturer to provide technical support. Never leave this feature enabled. Hackers can use them to get into your home network.

Log out as Administrator: Once youve set up your router, log out as administrator, to lessen the risk that someone can piggyback on your session to gain control of your device.

Keep your router up-to-date: To be secure and effective, the software that comes with your router needs occasional updates. Before you set up a new router and periodically thereafter, visit the manufacturers website to see if theres a new version of the software available for download. To make sure you hear about the latest version, register your router with the manufacturer and sign up to get updates.

And when you secure your router, dont forget to secure your computer too. Use the same basic computer security practices that you would for any computer connected to the internet. For example, use protections like antivirus, antispyware, and a firewall -- and keep these protections up-to-date.

Apps now allow you to access your home network from a mobile device. Before you do, be sure that some security features are in place.

Use a strong password on any app that accesses your network. Log out of the app when youre not using it. That way, no one else can access the app if your phone is lost or stolen.

Password protect your phone or other mobile device. Even if your app has a strong password, its best to protect your device with one, too.

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Quantum computer – Simple English Wikipedia, the free …

A quantum computer is a model of how to build a computer. The idea is that quantum computers can use certain phenomena from quantum mechanics, such as superposition and entanglement, to perform operations on data. The basic principle behind quantum computation is that quantum properties can be used to represent data and perform operations on it.[1] A theoretical model is the quantum Turing machine, also known as the universal quantum computer.

The idea of quantum computing is still very new. Experiments have been done. In these, a very small number of operations were done on qubits (quantum bit). Both practical and theoretical research continues with interest, and many national government and military funding agencies support quantum computing research to develop quantum computers for both civilian and military purposes, such as cryptanalysis.[2]

Today's computers, called "classical" computers, store information in binary; each bit is either on or off. Quantum computation use qubits, which, in addition to being possibly on or off, can be both on and off, which is a way of describing superposition, until a measurement is made. The state of a piece of data on a normal computer is known with certainty, but quantum computation uses probabilities. Only very simple quantum computers have been built, although larger designs have been invented. Quantum computation uses a special type of physics, quantum physics.

If large-scale quantum computers can be built, they will be able to solve some problems much more quickly than any computer that exists today (such as Shor's algorithm). Quantum computers are different from other computers such as DNA computers and traditional computers based on transistors. Some computing architectures such as optical computers[3] may use classical superposition of electromagnetic waves. Without quantum mechanical resources such as entanglement, people think that an exponential advantage over classical computers is not possible.[4] Quantum computers cannot perform functions that are not theoretically computable by classical computers, in other words they do not alter the Church-Turing thesis. They would, however, be able to do many things much more quickly and efficiently.

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Topological quantum computer – Wikipedia

Hypothetical fault-tolerant quantum computer based on topological condensed matter

A topological quantum computer is a theoretical quantum computer that employs two-dimensional quasiparticles called anyons, whose world lines pass around one another to form braids in a three-dimensional spacetime (i.e., one temporal plus two spatial dimensions). These braids form the logic gates that make up the computer. The advantage of a quantum computer based on quantum braids over using trapped quantum particles is that the former is much more stable. Small, cumulative perturbations can cause quantum states to decohere and introduce errors in the computation, but such small perturbations do not change the braids' topological properties. This is like the effort required to cut a string and reattach the ends to form a different braid, as opposed to a ball (representing an ordinary quantum particle in four-dimensional spacetime) bumping into a wall. Alexei Kitaev proposed topological quantum computation in 1997. While the elements of a topological quantum computer originate in a purely mathematical realm, experiments in fractional quantum Hall systems indicate these elements may be created in the real world using semiconductors made of gallium arsenide at a temperature of near absolute zero and subjected to strong magnetic fields.

Anyons are quasiparticles in a two-dimensional space. Anyons are neither fermions nor bosons, but like fermions, they cannot occupy the same state. Thus, the world lines of two anyons cannot intersect or merge, which allows their paths to form stable braids in space-time. Anyons can form from excitations in a cold, two-dimensional electron gas in a very strong magnetic field, and carry fractional units of magnetic flux. This phenomenon is called the fractional quantum Hall effect. In typical laboratory systems, the electron gas occupies a thin semiconducting layer sandwiched between layers of aluminium gallium arsenide.

When anyons are braided, the transformation of the quantum state of the system depends only on the topological class of the anyons' trajectories (which are classified according to the braid group). Therefore, the quantum information which is stored in the state of the system is impervious to small errors in the trajectories. In 2005, Sankar Das Sarma, Michael Freedman, and Chetan Nayak proposed a quantum Hall device that would realize a topological qubit. In a key development for topological quantum computers, in 2005 Vladimir J. Goldman, Fernando E. Camino, and Wei Zhou claimed to have created and observed the first experimental evidence for using a fractional quantum Hall effect to create actual anyons, although others have suggested their results could be the product of phenomena not involving anyons. It should also be noted that non-abelian anyons, a species required for topological quantum computers, have yet to be experimentally confirmed. Possible experimental evidence has been found,[1] but the conclusions remain contested.[2]

Topological quantum computers are equivalent in computational power to other standard models of quantum computation, in particular to the quantum circuit model and to the quantum Turing machine model[citation needed]. That is, any of these models can efficiently simulate any of the others. Nonetheless, certain algorithms may be a more natural fit to the topological quantum computer model. For example, algorithms for evaluating the Jones polynomial were first developed in the topological model, and only later converted and extended in the standard quantum circuit model.

To live up to its name, a topological quantum computer must provide the unique computation properties promised by a conventional quantum computer design, which uses trapped quantum particles. Fortunately in 2002, Michael H. Freedman, Alexei Kitaev, Michael J. Larsen, and Zhenghan Wang proved that a topological quantum computer can, in principle, perform any computation that a conventional quantum computer can do.[3]

They found that a conventional quantum computer device, given an error-free operation of its logic circuits, will give a solution with an absolute level of accuracy, whereas a topological quantum computing device with flawless operation will give the solution with only a finite level of accuracy. However, any level of precision for the answer can be obtained by adding more braid twists (logic circuits) to the topological quantum computer, in a simple linear relationship. In other words, a reasonable increase in elements (braid twists) can achieve a high degree of accuracy in the answer. Actual computation [gates] are done by the edge states of a fractional quantum Hall effect. This makes models of one-dimensional anyons important. In one space dimension, anyons are defined algebraically.

Even though quantum braids are inherently more stable than trapped quantum particles, there is still a need to control for error inducing thermal fluctuations, which produce random stray pairs of anyons which interfere with adjoining braids. Controlling these errors is simply a matter of separating the anyons to a distance where the rate of interfering strays drops to near zero. Simulating the dynamics of a topological quantum computer may be a promising method of implementing fault-tolerant quantum computation even with a standard quantum information processing scheme. Raussendorf, Harrington, and Goyal have studied one model, with promising simulation results.[4]

One of the prominent examples in topological quantum computing is with a system of fibonacci anyons.[5] These anyons can be used to create generic gates for topological quantum computing. There are three main steps for creating a model:

Fibonacci Anyons are defined by three qualities:

The last fusion rule can be extended this to a system of three anyons:

Thus, fusing three anyons will yield a final state of total charge {displaystyle tau } in 2 ways, or a charge of 1 {displaystyle 1} in exactly one way. We use three states to define our basis.[6] However, because we wish to encode these three anyon states as superpositions of 0 and 1, we need to limit the basis to a two-dimensional Hilbert Space. Thus, we consider only two states with a total charge of {displaystyle tau } . This choice is purely phenomenological. In these states, we group the two leftmost anyons into a 'control group', and leave the rightmost as a 'non-computational anyon'. We classify a | 0 {displaystyle |0rangle } state as one where the control group has total 'fused' charge of 1 {displaystyle 1} , and a state of | 1 {displaystyle |1rangle } has a control group with a total 'fused' charge of {displaystyle tau } . For a more complete description, see Nayak.[6]

Following the ideas above, adiabatically braiding these anyons around each-other with result in a unitary transformation. These braid operators are a result of two subclasses of operators:

The R matrix can be conceptually thought of as the topological phase that is imparted onto the anyons during the braid. As the anyons wind around each-other, they pick up some phase due to the Aharonov-Bohm effect.

The F matrix is a result of the physical rotations of the anyons. As they braid between each-other, it is important to realize that the bottom two anyonsthe control groupwill still distinguish the state of the qubit. Thus, braiding the anyons will change which anyons are in the control group, and therefore change the basis. We evaluate the anyons by always fusing the control group (the bottom anyons) together first, so exchanging which anyons these are will rotate the system. Because these anyons are non-abelian, the order of the anyons (which ones are within the control group) will matter, and as such they will transform the system.

The complete braid operator can be derived as:

B = F 1 R F {displaystyle B=F^{-1}RF}

In order to mathematically construct the F and R operators, we can consider permutations of these F and R operators. We know that if we sequentially change the basis that we are operating on, this will eventually lead us back to the same basis. Similarly, we know that if we braid anyons around each-other a certain number of times, this will lead back to the same state. These axioms are called the pentagonal and hexagonal axioms respectively as performing the operation can be visualized with a pentagon/hexagon of state transformations. Although mathematically difficult,[7] these can be approached much more successfully visually.

With these braid operators, we can finally formalize the notion of braids in terms of how they act on our Hilbert space and construct arbitrary universal quantum gates.

Explicit braids that perform particular quantum computations with Fibonacci anyons have been given by [8]

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Google absorbs DeepMind healthcare unit 10 months after …

Google has finally absorbed the healthcare unit of its artificial intelligence company DeepMind, the British company it acquired for 400 million ($500 million) in 2016.

The change means that DeepMind Health, the unit which focuses on using AI to improve medical care, is now part of Google's own dedicated healthcare unit. Google Health was created in November 2018, and is run by big-name healthcare CEO David Feinberg.

DeepMind's clinical lead, Dominic King, announced the change in a blogpost on Wednesday. King will continue to lead the team out of London.

It has taken some 10 months for the integration to happen.

It also comes one month after the DeepMind cofounder overseeing that division, Mustafa Suleyman, confirmed that he was on leave from the business for unspecified reasons. He has said he plans to return to DeepMind before the end of the year.

Read more:The cofounder of Google's AI company DeepMind hit back at 'speculation' over his leave of absence

Suleyman spearheaded DeepMind's "applied" division, which focuses on the practical application of artificial intelligence in areas such as healthcare and energy. DeepMind's other cofounder and CEO, Demis Hassabis, is more focused on the academic side of the business and the firm's research efforts.

One source with knowledge of the matter said Google planned to take more control of DeepMind's "applied" division, leaving Suleyman's future role at the business unclear. The shift would essentially leave DeepMind as a research-only organization, with Google focused on commercializing its findings. "They've created a private university for AI in Britain," the person said.

DeepMind hinted as much in November, when it announced the Streams app would fall under Google's auspices.

DeepMind cofounder, Mustafa Suleyman, who is on leave from the business. DeepMind

DeepMind declined to comment.

The integration sees DeepMind's health partnerships with Britain's state-funded health system, the NHS, continued under Google Health, something that may raise eyebrows. A New Scientist investigation in 2016 revealed that DeepMind, with its Streams app, had extensive access to 1.6 million patients' data in an arrangement with London's Royal Free Hospital. A UK regulator ruled that the data-sharing agreement was unlawful. The revelations triggered public outcry over worries that a US tech giant, Google, might gain access to confidential patient data for profit.

DeepMind's current NHS partnerships include Moorfields Eye Hospital to detect eye disease, and University College Hospital on cancer radiotherapy treatment. In the US, it has partnered the US Department of Veterans Affairs on predicting patient deterioration. Dominic King, DeepMind's clinical lead, wrote in a post: "We see enormous potential in continuing, and scaling, our work with all three partners in the coming years as part of Google Health."

He added: "As has always been the case, our partners are in full control of all patient data and we will only use patient data to help improve care, under their oversight and instructions."

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DeepMind Q&A Dataset – New York University

Hermann et al. (2015) created two awesome datasets using news articles for Q&A research. Each dataset contains many documents (90k and 197k each), and each document companies on average 4 questions approximately. Each question is a sentence with one missing word/phrase which can be found from the accompanying document/context.

The original authors kindly released the scripts and accompanying documentation to generate the datasets (see here). Unfortunately due to instability of WaybackMachine, it is often cumbersome to generate the datasets from scratch using the provided scripts. Furthermore, in certain parts of the world, it turned out to be far from being straight-forward to access the WaybackMachine.

I am making the generated datasets available here. This will hopefully make the datasets used by a wider audience and lead to faster progress in Q&A research.

Hermann, K. M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015). Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems (pp. 1684-1692).

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Superconducting quantum computing – Wikipedia

Quantum computing implementation

Superconducting quantum computing is an implementation of a quantum computer in superconducting electronic circuits. Research in superconducting quantum computing is conducted by Google,[1] IBM,[2] BBN Technologies,[3] Rigetti,[4] and Intel.[5] as of May2016[update], up to nine fully controllable qubits are demonstrated in a 1D array,[6] up to sixteen in a 2D architecture.[2]

More than two thousand superconducting qubits are in a commercial product by D-Wave Systems, however these qubits implement quantum annealing instead of a universal model of quantum computation.

Classical computation models rely on physical implementations consistent with the laws of classical mechanics.[8] It is known, however, that the classical description is only accurate for specific cases, while the more general description of nature is given by quantum mechanics. Quantum computation studies the application of quantum phenomena, that are beyond the scope of classical approximation, for information processing and communication. Various models of quantum computation exist, however the most popular models incorporate the concepts of qubits and quantum gates. A qubit is a generalization of a bit - a system with two possible states, that can be in a quantum superposition of both. A quantum gate is a generalization of a logic gate: it describes the transformation that one or more qubits will experience after the gate is applied on them, given their initial state. The physical implementation of qubits and gates is difficult, for the same reasons that quantum phenomena are hard to observe in everyday life. One approach is to implement the quantum computers in superconductors, where the quantum effects become macroscopic, though at a price of extremely low operation temperatures.

In a superconductor, the basic charge carriers are pairs of electrons (known as Cooper pairs), rather than the single electrons in a normal conductor. The total spin of a Cooper pair is an integer number, thus the Cooper pairs are bosons (while the single electrons in the normal conductor are fermions). Cooled bosons, contrary to cooled fermions, are allowed to occupy a single quantum energy level, in an effect known as the Bose-Einstein condensate. In a classical interpretation it would correspond to multiple particles occupying the same position in space and having an equal momentum, effectively behaving as a single particle.

At every point of a superconducting electronic circuit (that is a network of electrical elements), the condensate wave function describing the charge flow is well-defined by a specific complex probability amplitude. In a normal conductor electrical circuit, the same quantum description is true for individual charge carriers, however the various wave functions are averaged in the macroscopic analysis, making it impossible to observe quantum effects. The condensate wave function allows designing and measuring macroscopic quantum effects. For example, only a discrete number of magnetic flux quanta penetrates a superconducting loop, similarly to the discrete atomic energy levels in the Bohr model. In both cases, the quantization is a result of the complex amplitude continuity. Differing from the microscopic quantum systems (such as atoms or photons) used for implementations of quantum computers, the parameters of the superconducting circuits may be designed by setting the (classical) values of the electrical elements that compose them, e.g. adjusting the capacitance or inductance.

In order to obtain a quantum mechanical description of an electrical circuit a few steps are required. First, all the electrical elements are described with the condensate wave function amplitude and phase, rather than with the closely related macroscopic current and voltage description used for classical circuits. For example, a square of the wave function amplitude at some point in space is the probability of finding a charge carrier there, hence the square of the amplitude corresponds to the classical charge distribution. Second, generalized Kirchhoff's circuit laws are applied at every node of the circuit network to obtain the equations of motion. Finally, the equations of motion are reformulated to Lagrangian mechanics and a quantum Hamiltonian is derived.

The devices are typically designed in the radio-frequency spectrum, cooled down in dilution refrigerators below 100mK and addressed with conventional electronic instruments, e.g. frequency synthesizers and spectrum analyzers. Typical dimensions on the scale of micrometers, with sub-micrometer resolution, allow a convenient design of a quantum Hamiltonian with the well-established integrated circuit technology.

A distinguishing feature of superconducting quantum circuits is the usage of a Josephson junction - an electrical element non existent in normal conductors. A junction is a weak connection between two leads of a superconducting wire, usually implemented as a thin layer of insulator with a shadow evaporation technique. The condensate wave functions on the two sides of the junction are weakly correlated - they are allowed to have different superconducting phases, contrary to the case of a continuous superconducting wire, where the superconducting wave function must be continuous. The current through the junction occurs by quantum tunneling. This is used to create a non-linear inductance which is essential for qubit design, as it allows a design of anharmonic oscillators. A quantum harmonic oscillator cannot be used as a qubit, as there is no way to address only two of its states.

The three superconducting qubit archetypes are the phase, charge and flux qubits, though many hybridizations exist (Fluxonium,[9] Transmon,[10] Xmon,[11] Quantronium[12]). For any qubit implementation, the logical quantum states { | 0 , | 1 } {displaystyle {|0rangle ,|1rangle }} are to be mapped to the different states of the physical system, typically to the discrete (quantized) energy levels or to their quantum superpositions. In the charge qubit, different energy levels correspond to an integer number of Cooper pairs on a superconducting island. In the flux qubit, the energy levels correspond to different integer numbers of magnetic flux quanta trapped in a superconducting ring. In the phase qubit, the energy levels correspond to different quantum charge oscillation amplitudes across a Josephson junction, where the charge and the phase are analogous to momentum and position correspondingly of a quantum harmonic oscillator. Note that the phase here is the complex argument of the superconducting wavefunction, also known as the superconducting order parameter, not the phase between the different states of the qubit.

In the table below, the three archetypes are reviewed. In the first row, the qubit electrical circuit diagram is presented. In the second, the quantum Hamiltonian derived from the circuit is shown. Generally, the Hamiltonian can be divided to a "kinetic" and "potential" parts, in analogy to a particle in a potential well. The particle mass corresponds to some inverse function of the circuit capacitance, while the shape of the potential is governed by the regular inductors and Josephson junctions. One of the first challenges in qubit design is to shape the potential well and to choose the particle mass in a way that the energy separation between specific two of the energy levels will differ from all other inter-level energy separations in the system. These two levels will be used as the logical states of the qubit. The schematic wave solutions in the third row of the table depict the complex amplitude of the phase variable. In other words, if a phase of the qubit is measured while the qubit is in a specific state, there is a non-zero probability to measure a specific value only where the depicted wave function oscillates. All three rows are essentially three different presentations of the same physical system.

Type

Aspect

A superconducting island (encircled with a dashed line) defined between the leads of a capacitor with capacitance C {displaystyle C} and a Josephson junction with energy E J {displaystyle E_{J}} is biased by voltage U {displaystyle U}

A superconducting loop with inductance L {displaystyle L} is interrupted by a junction with Josephson energy E J {displaystyle E_{J}} . Bias flux {displaystyle Phi } is induced by a flux line with a current I 0 {displaystyle I_{0}}

Josephson junction with energy parameter E J {displaystyle E_{J}} is biased by a current I 0 {displaystyle I_{0}}

H = E C ( N N g ) 2 E J cos {displaystyle H=E_{C}(N-N_{g})^{2}-E_{J}cos phi } ,where N {displaystyle N} is the number of Cooper pairs to tunnel the junction, N g = C V 0 / 2 e {displaystyle N_{g}=CV_{0}/2e} is the charge on the capacitor in units of Cooper pairs number, E C = ( 2 e ) 2 / 2 ( C J + C ) {displaystyle E_{C}=(2e)^{2}/2(C_{J}+C)} is the charging energy associated with both the capacitance C {displaystyle C} and the Josephson junction capacitance C J {displaystyle C_{J}} , and {displaystyle phi } is the superconducting wave function phase difference across the junction.

H = q 2 2 C J + ( 0 2 ) 2 2 2 L E J cos [ 2 0 ] {displaystyle H={frac {q^{2}}{2C_{J}}}+left({frac {Phi _{0}}{2pi }}right)^{2}{frac {phi ^{2}}{2L}}-E_{J}cos left[phi -Phi {frac {2pi }{Phi _{0}}}right]} ,where q {displaystyle q} is the charge on the junction capacitance C J {displaystyle C_{J}} and {displaystyle phi } is the superconducting wave function phase difference across the Josephson junction. {displaystyle phi } is allowed to take values greater than 2 {displaystyle 2pi } , and thus is alternatively defined as the time integral of voltage along the inductance L {displaystyle L} .

H = ( 2 e ) 2 2 C J q 2 I 0 0 2 E J cos {displaystyle H={frac {(2e)^{2}}{2C_{J}}}q^{2}-I_{0}{frac {Phi _{0}}{2pi }}phi -E_{J}cos phi } , where C J {displaystyle C_{J}} is the capacitance associated with the Josephson junction, 0 {displaystyle Phi _{0}} is the magnetic flux quantum, q {displaystyle q} is the charge on the junction capacitance C J {displaystyle C_{J}} and {displaystyle phi } is the phase across the junction.

The potential part of the Hamiltonian, E J cos {displaystyle -E_{J}cos phi } , is depicted with the thick red line. Schematic wave function solutions are depicted with thin lines, lifted to their appropriate energy level for clarity. Only the solid wave functions are used for computation. The bias voltage is set so that N g = 1 2 {displaystyle N_{g}={frac {1}{2}}} , minimizing the energy gap between | 0 {displaystyle |0rangle } and | 1 {displaystyle |1rangle } , thus making the gap different from other energy gaps (e.g. the gap between | 1 {displaystyle |1rangle } and | 2 {displaystyle |2rangle } ). The difference in gaps allows addressing transitions from | 0 {displaystyle |0rangle } to | 1 {displaystyle |1rangle } and vice versa only, without populating other states, thus effectively treating the circuit as a two-level system (qubit).

The potential part of the Hamiltonian, ( 0 2 ) 2 2 2 L E J cos [ 2 0 ] {displaystyle left({frac {Phi _{0}}{2pi }}right)^{2}{frac {phi ^{2}}{2L}}-E_{J}cos left[phi -Phi {frac {2pi }{Phi _{0}}}right]} , plotted for the bias flux = 0 / 2 {displaystyle Phi =Phi _{0}/2} , is depicted with the thick red line. Schematic wave function solutions are depicted with thin lines, lifted to their appropriate energy level for clarity. Only the solid wave functions are used for computation. Different wells correspond to a different number of flux quanta trapped in the superconducting loops. The two lower states correspond to a symmetrical and an antisymmetrical superposition of zero or single trapped flux quanta, sometimes denoted as clockwise and counterclockwise loop current states: | 0 = [ | + | ] / 2 {displaystyle |0rangle =left[|circlearrowleft rangle +|circlearrowright rangle right]/{sqrt {2}}} and | 1 = [ | | ] / 2 {displaystyle |1rangle =left[|circlearrowleft rangle -|circlearrowright rangle right]/{sqrt {2}}} .

The so-called "washboard" potential part of the Hamiltonian, I 0 0 2 E J cos {displaystyle -I_{0}{frac {Phi _{0}}{2pi }}phi -E_{J}cos phi } , is depicted with the thick red line. Schematic wave function solutions are depicted with thin lines, lifted to their appropriate energy level for clarity. Only the solid wave functions are used for computation. The bias current is adjusted to make the wells shallow enough to contain exactly two localized wave functions. A slight increase in the bias current causes a selective "spill" of the higher energy state ( | 1 {displaystyle |1rangle } ), expressed with a measurable voltage spike - a mechanism commonly used for phase qubit measurement.

The GHz energy gap between the energy levels of a superconducting qubit is intentionally designed to be compatible with available electronic equipment, due to the terahertz gap - lack of equipment in the higher frequency band. In addition, the superconductor energy gap implies a top limit of operation below ~1THz (beyond it, the Cooper pairs break). On the other hand, the energy level separation cannot be too small due to cooling considerations: a temperature of 1K implies energy fluctuations of 20GHz. Temperatures of tens of mili-Kelvin achieved in dilution refrigerators allow qubit operation at a ~5GHz energy level separation. The qubit energy level separation may often be adjusted by means of controlling a dedicated bias current line, providing a "knob" to fine tune the qubit parameters.

An arbitrary single qubit gate is achieved by rotation in the Bloch sphere. The rotations between the different energy levels of a single qubit are induced by microwave pulses sent to an antenna or transmission line coupled to the qubit, with a frequency resonant with the energy separation between the levels. Individual qubits may be addressed by a dedicated transmission line, or by a shared one if the other qubits are off resonance. The axis of rotation is set by quadrature amplitude modulation of the microwave pulse, while the pulse length determines the angle of rotation.[14]

More formally, following the notation of,[14] for a driving signal

E ( t ) = E x ( t ) cos ( d t ) + E y ( t ) sin ( d t ) {displaystyle {mathcal {E}}(t)={mathcal {E}}^{x}(t)cos(omega _{d}t)+{mathcal {E}}^{y}(t)sin(omega _{d}t)}

of frequency d {displaystyle omega _{d}} , a driven qubit Hamiltonian in a rotating wave approximation is

H R / = ( d ) | 1 1 | + E x ( t ) 2 x + E y ( t ) 2 y {displaystyle H^{R}/hbar =(omega -omega _{d})|1rangle langle 1|+{frac {{mathcal {E}}^{x}(t)}{2}}sigma _{x}+{frac {{mathcal {E}}^{y}(t)}{2}}sigma _{y}} ,

where {displaystyle omega } is the qubit resonance and x , y {displaystyle sigma _{x},sigma _{y}} are Pauli matrices.

In order to implement a rotation about the X {displaystyle X} axis, one can set E y ( t ) = 0 {displaystyle {mathcal {E}}^{y}(t)=0} and apply the microwave pulse at frequency d = {displaystyle omega _{d}=omega } for time t g {displaystyle t_{g}} . The resulting transformation is

U x = exp { i 0 t g H R d t } = exp { i 0 t g E x ( t ) d t x / 2 } {displaystyle U_{x}=exp left{-{frac {i}{hbar }}int _{0}^{t_{g}}H^{R}dtright}=exp left{-iint _{0}^{t_{g}}{mathcal {E}}^{x}(t)dtcdot sigma _{x}/2right}} ,

that is exactly the rotation operator R X ( ) {displaystyle R_{X}(theta )} by angle = 0 t g E x ( t ) d t {displaystyle theta =int _{0}^{t_{g}}{mathcal {E}}^{x}(t)dt} about the X {displaystyle X} axis in the Bloch sphere. An arbitrary rotation about the Y {displaystyle Y} axis can be implemented in a similar way. Showing the two rotation operators is sufficient for universality, as every single qubit unitary operator U {displaystyle U} may be presented as U = R X ( 1 ) R Y ( 2 ) R X ( 3 ) {displaystyle U=R_{X}(theta _{1})R_{Y}(theta _{2})R_{X}(theta _{3})} (up to a global phase, that is physically unimportant) by a procedure known as the X Y {displaystyle X-Y} decomposition.[15]

For example, setting 0 t g E x ( t ) d t = {displaystyle int _{0}^{t_{g}}{mathcal {E}}^{x}(t)dt=pi } results with a transformation

U x = exp { i 0 t g E x ( t ) d t x / 2 } = e i x / 2 = i x {displaystyle U_{x}=exp left{-iint _{0}^{t_{g}}{mathcal {E}}^{x}(t)dtcdot sigma _{x}/2right}=e^{-ipi sigma _{x}/2}=-isigma _{x}} ,

that is known as the NOT gate (up to the global phase i {displaystyle -i} ).

Coupling qubits is essential for implementing 2-qubit gates. Coupling two qubits may be achieved by connecting them to an intermediate electrical coupling circuit. The circuit might be a fixed element, such as a capacitor, or controllable, such as a DC-SQUID. In the first case, decoupling the qubits (during the time the gate is off) is achieved by tuning the qubits out of resonance one from another, i.e. making the energy gaps between their computational states different.[16] This approach is inherently limited to allow nearest-neighbor coupling only, as a physical electrical circuit is to be lay out in between the connected qubits. Notably, D-Wave Systems' nearest-neighbor coupling achieves a highly connected unit cell of 8 qubits in the Chimera graph configuration. Generally, quantum algorithms require coupling between arbitrary qubits, therefore the connectivity limitation is likely to require multiple swap operations, limiting the length of the possible quantum computation before the processor decoherence.

Another method of coupling two or more qubits is by coupling them to an intermediate quantum bus. The quantum bus is often implemented as a microwave cavity, modeled by a quantum harmonic oscillator. Coupled qubits may be brought in and out of resonance with the bus and one with the other, hence eliminating the nearest-neighbor limitation. The formalism used to describe this coupling is cavity quantum electrodynamics, where qubits are analogous to atoms interacting with optical photon cavity, with the difference of GHz rather than THz regime of the electromagnetic radiation.

One popular gating mechanism includes two qubits and a bus, all tuned to different energy level separations. Applying microwave excitation to the first qubit, with a frequency resonant with the second qubit, causes a x {displaystyle sigma _{x}} rotation of the second qubit. The rotation direction depends on the state of the first qubit, allowing a controlled phase gate construction.[17]

More formally, following the notation of,[17] the drive Hamiltonian describing the system excited through the first qubit driving line is

H D / = A ( t ) cos ( ~ 2 t ) ( x I J 12 z x + m 12 I x ) {displaystyle H_{D}/hbar =A(t)cos({tilde {omega }}_{2}t)left(sigma _{x}otimes I-{frac {J}{Delta _{12}}}sigma _{z}otimes sigma _{x}+m_{12}Iotimes sigma _{x}right)} ,

where A ( t ) {displaystyle A(t)} is the shape of the microwave pulse in time, ~ 2 {displaystyle {tilde {omega }}_{2}} is the resonance frequency of the second qubit, { I , x , y , z } {displaystyle {I,sigma _{x},sigma _{y},sigma _{z}}} are the Pauli matrices, J {displaystyle J} is the coupling coefficient between the two qubits via the resonator, 12 1 2 {displaystyle Delta _{12}equiv omega _{1}-omega _{2}} is the qubit detuning, m 12 {displaystyle m_{12}} is the stray (unwanted) coupling between qubits and {displaystyle hbar } is Planck constant divided by 2 {displaystyle 2pi } . The time integral over A ( t ) {displaystyle A(t)} determines the angle of rotation. Unwanted rotations due to the first and third terms of the Hamiltonian can be compensated with single qubit operations. The remaining part is exactly the controlled-X gate.

Architecture-specific readout (measurement) mechanisms exist. The readout of a phase qubit is explained in the qubit archetypes table above. A state of the flux qubit is often read by an adjust DC-SQUID magnetometer. A more general readout scheme includes a coupling to a microwave resonator, where the resonance frequency of the resonator is shifted by the qubit state.[18]

The list of DiVincenzo's criteria for a physical system to implement a logical qubit is satisfied by the superconducting implementation. The challenges currently faced by the superconducting approach are mostly in the field of microwave engineering.[18]

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Superconducting quantum computing - Wikipedia

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