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While Quantum Computing technology is relatively nascent, it influences a new generation of simulations that are already running on classic computers and are now accelerated with the NVIDIA quantum SDK.

Twenty-seven years before Steve Jobs introduced a pocket-sized computer, physicist Paul Benioff published an article showing that, in theory, it was possible to build a much more robust system that could be hidden in a thimble: a computer. Quantum.

The name of this concept comes from the subatomic physics that it tried to take advantage. Benioff described in 1980 still drives research today, including efforts to build the next big thing in computing: a system that could make a PC look just as picturesque as an abacus.

Richard Feynman, a Nobel Prize winner whose witty lectures brought physics to a broad audience, helped establish the field, outlining how such systems could simulate wacky quantum phenomena more efficiently than traditional computers.

So What Is Quantum Computing?

Quantum computing uses the physics that governs subatomic particles to perform sophisticated parallel calculations, replacing the more simplistic transistors in today’s computers.

Quantum computers calculate using qubits, computing units that can be on, off, or whatever value in between, rather than the bits in traditional computers that are on or off, one or zero. The qubit’s ability to live in the intermediate state, called superposition, adds a powerful ability to the computing equation, making quantum computers superior for some types of math.

What Does a Quantum Computer Do?

Quantum computers can perform calculations that would take a long time for classical computers if they could finish them at all.

For example, today’s computers use eight bits to represent any number between 0 and 255. Thanks to features like superposition, a quantum computer can simultaneously use eight qubits to represent all numbers between 0 and 255.

It’s a feature like parallelism in computation: All possibilities are calculated simultaneously rather than sequentially, providing tremendous accelerations.

So while a classical computer performs long division calculations one at a time to factor a huge number. A quantum computer can get the answer in one step. Boom!

That means quantum computers could reshape entire fields, like cryptography, that rely on factoring huge numbers today.

A Big Role for Small Simulations

That could be just the beginning. Some experts believe that quantum computers will push past the limits that now hamper simulations in chemistry, materials science. And anything else involving worlds built from the nano-sized bricks of quantum mechanics.

Quantum computers could even extend the life of semiconductors by helping engineers create more sophisticated simulations of the quantum effects they are beginning to find in today’s smaller transistors.

Experts say that quantum computers will ultimately not replace classical computers but rather complement them. And some predict that quantum computers will be used as accelerators just as much as GPUs accelerate today’s computers.

How Does Quantum Computing Work?

Don’t expect to build your quantum computer as a DIY PC with parts pulled from discount bins at your local electronics store.

The few systems in operation today typically require cooling that creates working environments just above absolute zero. They need that arctic computing to handle the fragile quantum states that power these systems.

An example graphs how difficult it can be to build a quantum computer: to create a qubit, a prototype suspends an atom between two lasers. Try it in your home workshop!

Quantum computing needs muscles to create something called entanglement. That’s when two or more qubits exist in a single quantum state. A condition that is sometimes measured by electromagnetic waves just one millimeter wide.

If you add too much energy to that wave, you will lose the entanglement, overlap, or both.

What’s the State of Quantum Computers?

A few companies, such as Alibaba, Google, Honeywell, IBM, IonQ, and Xanadu, operate the first versions of quantum computers today.

Today they provide dozens of qubits. But qubits can be noisy, which sometimes makes them unreliable. To reliably address real-world problems, systems need tens or hundreds of thousands of qubits.

Experts believe it could be a couple of decades before we reach a high-fidelity era in which quantum computers are beneficial.

Predictions of when we reach the so-called supremacy of quantum computing. The moment when quantum computers perform tasks that classical computers cannot. Is the subject of a heated debate in the industry.

Accelerating Quantum Circuit Simulations Today

The good news is that the world of AI and machine learning puts both accelerators and GPUs on the spot. Which can perform many kinds of operations that quantum computers would compute with qubits.

So classical computers are already finding ways to host quantum simulations with GPUs today. For example, NVIDIA ran a state-of-the-art quantum simulation on Selene, our in-house AI supercomputer.

At the GTC keynote, NVIDIA introduced the SDL cuQuantum to accelerate quantum circuit simulations running on GPUs. The first works suggest that quantum will be able to offer accelerations of orders of magnitude.

The SDK takes an agnostic approach to provide a variety of tools that users can choose from to best suit their strategy. For example, the state vector method provides high-fidelity results, but its memory requirements grow exponentially with the number of qubits.

That creates a practical limit of about 50 qubits on today’s largest classic supercomputers. However, we have seen great results (below) using quantum to speed up quantum circuit simulations using this method.

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Mobile Tech – Healthcare is changing like never before, with mobile tech leading the charge. Health apps and smart gadgets aren’t just for fitness junkies—they’re reshaping how we think about staying healthy and accessing care. From instant consultations to tracking every heartbeat, mobile technology is giving the power back to patients.

The Rise of Mobile Health Apps

There’s an app for almost everything health-related today. Scheduling appointments, checking symptoms, or finding that perfect meditation track—it’s all a tap away. These apps bring a whole new meaning to “having a doctor in your pocket.” They offer convenience as well as a level of personalization that was once unimaginable. It’s simple: more control over health means better outcomes.

Mobile apps also support chronic disease management by allowing patients to track medication adherence, monitor symptoms, and contact healthcare professionals quickly. Engaging with these tools encourages consistent self-care and fosters a more informed approach to managing conditions like diabetes or hypertension. As more apps integrate telehealth capabilities, they simplify connecting with specialists and making follow-up appointments, closing the gap between therapy and ongoing care.

The development of these advanced apps is often backed by innovation hubs around the world. For instance, health app developers in Los Angeles are working tirelessly to create intuitive and user-friendly interfaces, continuously contributing to the rapid expansion of mobile health technologies.

Virtual Consultations: No More Waiting Rooms

Forget long waiting hours at the clinic. With virtual consultations, all you need is a smartphone and a stable internet connection. Doctors, psychiatrists, and specialists are just a call away. This isn’t the future; it’s happening now. It’s bringing healthcare to even the remotest areas and making top-notch care accessible to everyone.

These online consultations can also significantly reduce healthcare costs by cutting down on unnecessary ER visits and enabling doctors to manage routine consultations more effectively. Patients can maintain routine check-ups, mental health sessions, or specialist advice from the comfort of their homes, thus ensuring continuity of care without the challenges of travel. Consequently, virtual consultations help bridge the gap not only in location but also in healthcare equity.

Sensors and Wearable Tech: Know Your Body Better

Wearable tech is more than just flashy wristbands. They’re relentless health monitors. Tracking steps is only the beginning. Today’s wearables monitor heart rate variability, oxygen levels, sleep patterns, and beyond. They provide insights that empower individuals to take proactive steps toward better health.

Furthermore, wearables have transformed fitness regimens by offering users real-time feedback and personalized coaching. Many devices now incorporate artificial intelligence to suggest better fitness plans, remind users when to hydrate, and even alert them to sedentary behavior. This constant feedback loop creates a sustained interest in maintaining an active and healthy lifestyle.

Data Security: Confidence in Privacy

With great technology comes the question of data security. The fear of hacks or breaches is real. But efforts to ensure robust data protection are stronger than ever. Strong encryptions, multi-factor authentication, and regular audits are becoming standard. Patients have to trust that their data is safe, and tech developers are making strides to ensure this.

The Benefits for Healthcare Providers

Doctors and healthcare teams are rapidly adopting mobile technology too. Access to real-time patient data means no more hunting through stacks of paper records. Treatment plans are more accurate, and results are quickly measurable. Medical errors go down when doctors have up-to-date information in their hands.

Moreover, direct access to patients’ daily health metrics enables healthcare professionals to offer more personalized advice and immediate intervention when necessary. As electronic health records become more widely accepted, collaboration among multidisciplinary teams has improved, reducing duplication of tests and hastening the overall treatment process. The seamless integration of data is proving to be invaluable in healthcare settings where rapid decision-making is vital.

The Future of Healthcare: Personalized Medicine

The vision is clear—personalized medicine driven by mobile tech. Instead of generic treatment plans, imagine approaching healthcare where medication and therapies are tailored specifically to the individual’s genetic makeup and lifestyle. With mobile technology at the forefront, personalized medicine is not just a dream; it’s becoming a reality.

Final Words

Healthcare with mobile technology is no longer a vision of the future; it’s our present. The transition to digital healthcare is undeniable. The patient-doctor relationship is transforming, and the possibilities are endless. As we embrace mobile technology, health becomes more accessible, data becomes more comprehensive, and treatment becomes more personalized. Mobile tech isn’t just a tool—it’s a game-changer for healthcare.

Mobile applications have changed how businesses connect with people. Testing these apps is important to ensure they work well, are easy to use, and meet users’ wants. With apps getting more complicated, testing them is a must-have, not just a choice. Mobile app testing covers a lot of factors, including how effective they are and the ease with which one can use the apps. Another point to take home is that it does not matter whether you are developing apps for iPhones, Andriod, or both – testing guarantees the user a smooth run and prevents problems from reaching the user. Tools like Appium are handy for developers and testing teams to automate their work efficiently.

This blog guide to testing mobile apps explains a few tools, methods, and trends. It will cover the basics of mobile app testing, show how to use Appium to automate your testing, and share top practices used in the industry. It will also discuss what’s coming next in making mobile apps so you can be ready.

The Basics of Mobile Testing: Why It Matters

Ensuring a mobile application works well on different devices, OS versions, and network environments is crucial. It involves checking if the app functions properly, is easy to use, and looks consistent, considering challenges like various screen sizes, OS versions, and user habits.

Testing mobile apps is vital due to the wide range of devices available. Unlike websites that work similarly across browsers, mobile apps must adapt to different hardware and software setups. Inadequate testing can lead to crashes, bugs, or slow performance, resulting in negative user feedback and loss of customers.

Mobile testing is also important for meeting industry standards and regulations. Additionally, testing mobile apps can boost user retention. A well-tested app that provides a smooth user experience builds trust, encouraging users to interact more with the app. Businesses that invest in robust testing tools like Appium often see better returns on their investment as their apps perform well in competitive markets.

This initial testing phase in app development ensures quality is maintained, reducing the need for costly fixes after the app is launched. With the increasing use of mobile apps, understanding the significance of mobile testing is key to keeping your projects successful and on the right path.

Mobile Testing Tools: The Essential Arsenal

Mobile testing tools are very important in ensuring that applications always perform consistently and are of high quality across different devices, operating systems, and network conditions. With so many tools, selecting appropriate tools is critical to ensuring efficient and comprehensive mobile application testing. Each tool has different capabilities, strengths, and specializations, so it is essential to know its features and best use cases.

Appium: The Cross-Platform Leader

Appium is an open-source mobile testing tool that is highly effective, flexible, and can work cross-platform. The tool can work with both combined applications for Android and iOS, as well as applications on one or the other. It’s worth mentioning that the most critical advantage of Appium is that a tester writes only a single test script that can be run across different platforms.

Its complete open-source interface integrates easily through well-known programming languages such as Java, Python, and Ruby, ensuring that such integration reaches the largest possible number of developers. Still, the open-source nature with robust community support has well-ensconced Appium into a robust preferred automated mobile testing.

Selenium for Mobile Browsers

Selenium is designed for web applications but can be used for mobile browser testing. It is very useful for ensuring seamless experiences on mobile web platforms. The Selenium Grid allows for parallel testing, ensuring efficiency when dealing with multiple browser configurations.

Testing native XCTest and Espresso

XCTest (for iOS) and Espresso (for Android) are go-to tools for platform-specific requirements. XCTest is Apple’s official testing framework, offering deep integration with Xcode to deliver robust performance testing for iOS applications. Espresso, by Google, is known for its speed and simplicity in creating automated UI tests for Android applications.

Calabash: Behavior-driven testing

Calabash supports behavior-driven development by integrating into Cucumber. This makes it particularly useful for teams that prefer their test cases written in simple language, thus bridging the gap between technical and non-technical members.

Cloud-Based Platforms

Cloud testing tools like BrowserStack and Sauce Labs give a wide range of devices and operating systems access without physical hardware. They both support automated and manual testing, essential for teams needing scalability and convenience.

Automation in Mobile Testing

The rapid pace of mobile application development needs testing solutions that are both efficient and reliable. Automation in mobile testing has emerged as a transformational approach addressing the inadequacies of manual testing by allowing faster, more accurate and scalable processes. By leveraging automation tools and frameworks, developers and QA teams ensure consistent application performance across various devices, operating systems, and configurations.

Why Automation Matters in Mobile Testing

Manual testing is both time-consuming and prone to human error. Automation allows teams to run repetitive test cases without delay and human error as quickly and consistently as possible, thereby minimizing the occurrence of oversight.

Especially in mobile app development, the diversity of devices and platforms can make comprehensive testing difficult. Automated testing proves to be a reliable mechanism for ensuring broad coverage without dramatically increasing the time and effort involved.

Key Benefits of Mobile Testing Automation

Automation in software testing brings forth numerous advantages:

• Efficiency: By conducting multiple tests concurrently, automation expedites the testing process, proving especially beneficial for evaluating expansive applications across diverse devices and configurations.
• Accuracy: Automated tests significantly diminish the chances of human error, guaranteeing consistent and trustworthy outcomes in every testing iteration.
• Scalability: Automated frameworks can manage extensive testing demands, encompassing testing on multiple devices, thus assuring the app’s optimal performance on various platforms.
• Integration with CI/CD: The seamless integration of automation with Continuous Integration/Continuous Delivery (CI/CD) pipelines ensures that tests are seamlessly triggered with each code alteration, offering immediate feedback to developers for swift adjustments and enhancements.

The Role of Tools Like Appium

Appium is among the most frequently used tools for mobile testing automation that supports cross-platform capabilities, i.e., iOS, Android, and hybrid applications, allowing one to write and execute a single test script on any platform. It is an effective solution for teams of any size due to its capability of integration with various programming languages and frameworks.

Best Practices for Effective Automation

• Define Clear Objectives: Describe what test cases will be tested and which will provide the most payback for automation.
• Create Reusable Scripts: Devise a modular, reusable script that can easily be adapted to other future testing needs.
• Combine Manual and Automated Testing: Allow repeat and regression testing automation, but use only manual exploratory and usability tests.

Mobile testing automation is a game changer, enabling faster delivery cycles, improved accuracy, and enhanced app quality. By adopting the right tools and practices, teams can harness their full potential to create exceptional mobile applications.

Appium Tutorial: Automating Mobile App Testing

Appium is the latest and most used tool in mobile app testing. It is an open-source framework used to automate the testing of native, hybrid, and mobile web applications. This software supports both Android and iOS platforms. Appium adheres to the “Write Once, Run Anywhere” philosophy, providing cross-platform reusability. Thus, the same script is reused for all mobile applications. It utilizes the WebDriver protocol to communicate with a mobile device. That’s why it can quickly adopt most programming languages such as Java, Python, Ruby, etc.

Installation of Appium

• Install Node.js since Appium is based on it.
• Install JDK and set up the JAVA_HOME environment variable.
• Download Android Studio for Android testing or Xcode for iOS testing.

Setting Up the Environment:

• To set up Android testing, use the ANDROID_HOME environment variable to point to the directory of the Android SDK.
• You have to set up Xcode for testing on iOS and ensure that the iOS simulator is available.

Creating Your First Test Script

• Pick a language compatible with Appium (like Java and Python). Download the appropriate Appium client library for your language.
• Create your Test Script:
• Import in WebDriver and Appium libraries.
• Set the capabilities you want, such as device name, platform version, and app location.
• Example (in Python):

Running and Debugging Tests:

• Start the Appium server via the GUI or command line.
• Run the test script from your IDE or command line.
• Tune in the application logs in the Appium console to identify issues.

More Advanced Usage and Integration:

• Parallel Testing: Use Appium Grid to run multiple parallel tests across various devices.
• CI/CD Integration: Integrate Appium with Jenkins or GitHub Actions to automate testing through your CI/CD pipeline.
• Custom Scripts: Write your framework for testing with custom libraries like TestNG or Pytest.

Emerging Trends in Mobile Testing

The mobile testing is always changing. Keeping up with trends will ensure that your testing strategies are relevant and effective:

• AI-Powered Testing: AI in software testing is added to testing frameworks to predict defects, generate test cases, and optimize test execution.
• Cloud-Based Testing: LambdaTest enables testers to access virtual devices without physically purchasing hardware.
• Shift-Left Testing: Integrate testing at earlier stages in the development cycle, which would help find and fix issues quickly.
• Impact of 5G: New parameters must be tested, including ultra-low latency and high-speed data transfer.
• Testing for IoT: IoT testing has expanded from the smartphone to the overall IoT ecosystem.

Knowing and keeping track of these trends will keep your mobile app testing approach complete and up-to-date.

Mobile Testing with LambdaTest

LambdaTest is a cloud-based solution for easy testing of mobile apps as it grants access to over 3000+ real devices and OS versions. Therefore, a team can conduct cross-platform testing for their mobile apps on various configurations without requiring hardware equipment.

Testers can test native, hybrid, and mobile web applications in manual and automated tests with LambdaTest. It also supports integration with popular tools like Appium and Selenium, making it a versatile choice for testing. Other features include live testing, responsive testing, and detailed debugging logs to facilitate smooth testing.

It also allows application testing on various real-time network conditions, ensuring apps perform flawlessly under different conditions. LambdaTest scalability and usability make it very valuable for developers and QA teams working towards delivering bug-free mobile applications with high quality in the most efficient way possible.

Conclusion

Mobile testing is an integral part of the application development lifecycle. Developers and QA teams will ensure quality applications if they utilize tools like Appium, adopt automation, and stay attuned to the latest trends. Effective mobile app testing minimizes risk and provides higher user satisfaction, the key to long-term success.

What is Deep Learning?

Deep learning is a special method for information processing and a sub-area of machine learning and artificial intelligence. Deep learning uses neural networks to analyze large data sets. This functionality used by deep learning acts in a similar way to the human brain. In doing so, data is first extracted, then analyzed in order to subsequently draw up a conclusion or prognosis. In practice, deep learning is mainly used to recognize images, understand texts or for better decision-making.

The basis of deep learning, neural networks, has been around since the early 1940s and is not a new topic. However, big data and the growing computing power (through graphics cards) are rapidly gaining attention. Deep learning solves problems that cannot be solve without these approaches.

DL is highly computationally intensive. The training can last for months to be able to make good predictions and decisions. The reason for this is complex architectures and millions of model parameters.

What is the association between Deep Learning and artificial neural networks?

Artificial neural networks are algorithms that are model on the biological model of the human brain. It is use to recognize patterns, interpret texts, or help us form clusters and classify objects on images.

Of course, a DL algorithm becomes like anyone machine learning algorithm trained on data. Artificial neural networks are often very complex, which interprets individual decisions as challenging to understand.

Why is D-L meaningful?

There are problems, such as unstructured image and text recognition or the development of recommendation systems in marketing, which can be map particularly well with neural networks. It is where artificial neural networks show their strengths.

The more significant the amount of data, the better profound learning works.

A good example is speech, text, and image recognition.

But structured data can also be processed well with neural networks, especially when a lot of data is available.

Big data technology and the steadily growing amount of data play a decisive role in the success of these approaches. Without big data, neural networks often work worse than classic machine learning, as the following diagram by the renowned DL expert Andrew Ng clearly shows.

What is the difference amid Deep Learning and Machine Learning?

Deep learning is a subsection of machine learning, so deep learning is always machine learning. However, it is worth taking a earlier look at the capabilities of these two types of machine learning, as both have different properties.

To better delimit the terms, we subdivide them into DL and “classic” machine learning. Classic machine learning refers to the methods that do not use artificial neural networks. Deep understanding, on the other hand, only uses artificial neural networks.

Thus, the main difference between DL and classic machine learning lies in processing unstructured data through artificial neural networks (ANN).

Artificial neural networks can convert unstructured information such as text, images, sounds, and videos into numerical values.

Is Deep Learning Overrated?

The question of whether DL is overrated is easy to answer: no.

Today, problems can be solve by neural networks that were previously considered unsolvable, in the desired quality for classic machine learning methods.

Also, reinforcement learning, used for autonomous vehicles and robotics, is only possible through DL methods. Such applications will significantly change our daily life in the next few years.

What are the use bags for Deep Learning?

DL is a powerful tool for problem-solving in data processing in the area of ​​big data. The use cases in the business environment are correspondingly diverse. We looked at seven compelling use cases for you and outlined them briefly. These illustrate the paramount role DL plays for companies today.

In the following, I will go into seven use cases of DL in different company areas and industries.