#qiskit programming

[quantimist]

Quantum Computing with Qiskit Free Course | 1 Year | Q World

Exciting News: QWorld’s QClass23/24 Your Gateway to Quantum Computing! Are you ready to dive into the fascinating world of quantum computing, algorithms, and programming? Look no further! QWorld is thrilled to announce the launch of our two-semester-long QClass23/24, beginning in September 2023 and running until May 2024. Join us for an immersive virtual experience that will equip you with the…

[poly-yuri-train]

spent 4 hours today trying to get ROOT to work on my desktop only to find out it works better on Linux

[story-plaza]

Power of Quantum Computing 02

Utilizing the Potential of Quantum Computing.

A revolutionary technology, quantum computing holds the promise of unmatched computational power. Development of quantum software is in greater demand as the field develops. The link between the complicated underlying hardware and the useful applications of quantum computing is provided by quantum software. The complexities of creating quantum software, its potential uses, and the difficulties developers face will all be covered in this article.

BY KARTAVYA AGARWAL

First, a primer on quantum computing.

Contrary to traditional computing, quantum computing is based on different principles. Working with qubits, which can exist in a superposition of states, is a requirement. These qubits are controlled by quantum gates, including the CNOT gate and the Hadamard gate. For the creation of quantum software, comprehension of these fundamentals is essential. Qubits and quantum gates can be used to create quantum algorithms, which are capable of solving complex problems more quickly than conventional algorithms. Second, there are quantum algorithms. The special characteristics of quantum systems are specifically tapped into by quantum algorithms. For instance, Shor's algorithm solves the factorization issue and might be a threat to traditional cryptography. The search process is accelerated by Grover's algorithm, however. A thorough understanding of these algorithms and how to modify them for various use cases is required of quantum software developers. They investigate and develop new quantum algorithms to address issues in a variety of fields, including optimization, machine learning, and chemistry simulations. Quantum simulation and optimization are the third point. Complex physical systems that are difficult to simulate on traditional computers can be done so using quantum software. Scientists can better comprehend molecular structures, chemical processes, and material properties by simulating quantum systems. Potential solutions for logistics planning, financial portfolio management, and supply chain optimization are provided by quantum optimization algorithms. To accurately model these complex systems, quantum software developers work on developing simulation frameworks and algorithm optimization techniques. The 4th Point is Tools and Languages for Quantum Programming. Programming languages and tools that are specific to quantum software development are required. A comprehensive set of tools and libraries for quantum computing are available through the open-source framework Qiskit, created by IBM. Another well-known framework that simplifies the design and simulation of quantum circuits is Cirq, created by Google. Incorporating quantum computing with traditional languages like C, the Microsoft Quantum Development Kit offers a quantum programming language and simulator. These programming languages and tools are utilized by developers to create quantum hardware, run simulations, and write quantum circuits. The 5th point is quantum error correction. Störungs in the environment and flaws in the hardware can lead to errors in quantum systems. Quantum computations are now more reliable thanks to quantum error correction techniques that reduce these errors. To guard against errors and improve the fault tolerance of quantum algorithms, developers of quantum software employ error correction codes like the stabilizer or surface codes. They must comprehend the fundamentals of error correction and incorporate these methods into their software designs. Quantum cryptography and secure communication are the sixth point. Secure communication and cryptography are impacted by quantum computing. Using the concepts of quantum mechanics, quantum key distribution (QKD) offers secure key exchange and makes any interception detectable. Post-quantum cryptography responds to the danger that quantum computers pose to already-in-use cryptographic algorithms. To create secure communication protocols and investigate quantum-resistant cryptographic schemes, cryptographers and quantum software developers work together. Point 7: Quantum machine learning A new field called "quantum machine learning" combines machine learning with quantum computing. The speedup of tasks like clustering, classification, and regression is being studied by quantum software developers. They investigate how quantum machine learning might be advantageous in fields like drug discovery, financial modeling, and optimization. Point 8: Validation and testing of quantum software. For accurate results and trustworthy computations, one needs trustworthy quantum software. Different testing methodologies are used by quantum software developers to verify the functionality and efficiency of their products. To locate bugs, address them, and improve their algorithms, they carry out extensive testing on simulators and quantum hardware. Quantum software is subjected to stringent testing and validation to guarantee that it produces accurate results on various platforms. Point 9: Quantum computing in the study of materials. By simulating and enhancing material properties, quantum software is crucial to the study of materials. To model chemical processes, examine electronic architectures, and forecast material behavior, researchers use quantum algorithms. Variational quantum eigensolvers are one example of a quantum-inspired algorithm that makes efficient use of the vast parameter space to find new materials with desired properties. To create software tools that improve the processes of materials research and discovery, quantum software developers work with materials scientists. Quantum computing in financial modeling is the tenth point. Quantum software is used by the financial sector for a variety of applications, which helps the industry reap the benefits of quantum computing. For portfolio optimization, risk assessment, option pricing, and market forecasting, quantum algorithms are being investigated. Financial institutions can enhance decision-making processes and acquire a competitive advantage by utilizing the computational power of quantum systems. Building quantum models, backtesting algorithms, and converting existing financial models to quantum frameworks are all tasks carried out by quantum software developers.

FAQs:. What benefits can software development using quantum technology offer? Complex problems can now be solved exponentially more quickly than before thanks to quantum software development. It opens up new opportunities in materials science, machine learning, optimization, and cryptography. Is everyone able to access quantum software development? Despite the fact that creating quantum software necessitates specialized knowledge, there are tools, tutorials, and development frameworks available to support developers as they begin their quantum programming journey. What are the principal difficulties faced in creating quantum software? Algorithm optimization for particular hardware, minimization of quantum errors through error correction methods, and overcoming the dearth of established quantum development tools are among the difficulties. Are there any practical uses for quantum software? Yes, there are many potential uses for quantum software, including drug discovery, financial modeling, traffic optimization, and materials science. What can be done to advance the creation of quantum software? Researchers, programmers, contributors to open-source quantum software projects, and people working with manufacturers of quantum hardware to improve software-hardware interactions are all ways that people can make a difference. Conclusion: The enormous potential of quantum computing is unlocked in large part by the development of quantum software. The potential for solving difficult problems and revolutionizing numerous industries is exciting as this field continues to develop. We can use quantum computing to influence the direction of technology by grasping its fundamentals, creating cutting-edge algorithms, and utilizing potent quantum programming languages and tools. link section for the article on Quantum Software Development: - Qiskit - Website - Qiskit is an open-source quantum computing framework developed by IBM. It provides a comprehensive suite of tools, libraries, and resources for quantum software development. - Cirq - Website - Cirq is a quantum programming framework developed by Google. It offers a platform for creating, editing, and simulating quantum circuits. - Microsoft Quantum Development Kit - Website - The Microsoft Quantum Development Kit is a comprehensive toolkit that enables quantum programming using the Q# language. It includes simulators, libraries, and resources for quantum software development. - Quantum Computing for the Determined - Book - "Quantum Computing for the Determined" by Alistair Riddoch and Aleksander Kubica is a practical guide that introduces the fundamentals of quantum computing and provides hands-on examples for quantum software development. - Quantum Algorithm Zoo - Website - The Quantum Algorithm Zoo is a repository of quantum algorithms categorized by application domains. It provides code examples and explanations of various quantum algorithms for developers to explore. Read the full article

[vijaysethupati]

Is Full Stack Development Ready for Quantum Computing?

In the rapidly evolving world of technology, Full Stack Development has become a must-have skill for developers seeking to build scalable and dynamic applications. But as quantum computing moves closer to real-world applications, a question naturally arises: Is Full Stack Development ready for the quantum leap?

To explore this, we need to look at the state of full stack technologies today, the nature of quantum computing, and how developers — especially those honing their skills through quality programs like the Best Full Stack Course in Pune — can prepare for a potential quantum future.

Understanding the Landscape: Full Stack Meets Quantum

Full Stack Development refers to the ability to work on both the front-end and back-end of a web application. It includes knowledge of:

Front-end technologies like HTML, CSS, JavaScript

Back-end technologies such as Node.js, Java, Python, and frameworks like Spring Boot or Express.js

Database management (SQL, NoSQL)

APIs and version control (e.g., Git)

In contrast, Quantum Computing operates on the principles of quantum mechanics. Instead of bits, it uses qubits, which can exist in multiple states simultaneously. This allows quantum computers to perform complex computations exponentially faster than classical computers.

Clearly, the two are fundamentally different. But are they mutually exclusive? Or can full stack developers find ways to work with, or even build for, quantum environments?

The Reality Check: Where Things Stand

Quantum computing is still in its experimental phase. Real-world applications are limited, and most systems that support quantum development use hybrid models — classical front-ends with quantum-powered back-ends.

For full stack developers, this means:

Quantum is not replacing traditional full stack anytime soon.

But it may complement it, especially in areas like cryptography, big data processing, AI/ML, and optimization.

Those taking up industry-recognized training, like the Best Java Certification Course in Pune, are already learning the foundations necessary to adapt to any paradigm — including quantum.

Skills That Will Remain Relevant

Even as quantum computing evolves, core skills from traditional full stack development will remain crucial. These include:

Proficiency in JavaScript and Java – Often used for integrating interfaces and logic layers.

Problem-solving skills – Quantum computing introduces abstract challenges that require structured thinking.

API integration – Quantum systems are often accessed through APIs; understanding REST or GraphQL is vital.

Cloud platform knowledge – Quantum computing services are primarily accessed via cloud-based platforms.

Whether you’re enrolled in the Best Full Stack Course in Pune or a Java specialization program, the foundations you're building today will prepare you for future-tech integrations.

How Full Stack Developers Can Prepare for Quantum Integration

Here are some actionable steps full stack developers can take today to prepare for the quantum future:

Learn the basics of quantum computing – Platforms like IBM Qiskit or Microsoft's Quantum Development Kit offer beginner-friendly resources.

Keep up with cloud quantum services – Azure Quantum and Amazon Braket provide APIs that allow classical front-end developers to run quantum algorithms.

Build hybrid applications – Try connecting traditional web applications to quantum algorithms via RESTful APIs.

Understand quantum-safe cryptography – Security protocols will evolve as quantum breaks traditional encryption.

Opportunities Ahead: Quantum in the Stack?

It’s unlikely that full stack developers will be writing direct quantum code (in Q#, Qiskit, etc.) in the near future. However, developers will need to understand how to integrate classical web systems with quantum processors.

Here’s how quantum might enter the full stack world:

Front-End – No major changes, but interfaces may need to interpret and display quantum results.

Back-End – Integration with quantum APIs for specialized tasks (e.g., high-level optimization).

Security Layer – Incorporating quantum-safe encryption and identity protocols.

Courses designed for comprehensive learning — like the Best Full Stack Course in Pune — already provide exposure to the kinds of architecture and logic needed to make this integration possible.

Why Java Still Matters in a Quantum World

Java might not be a quantum programming language, but its robustness, portability, and enterprise acceptance make it essential for building secure, scalable systems that might interface with quantum components. If you’re pursuing the Best Java Certification Course in Pune, you’re equipping yourself with the tools necessary to build the “glue” between classic and quantum systems.

Java’s role will likely be:

Facilitating API communication with quantum services

Running traditional business logic securely

Building scalable back-end infrastructures

So while you might not be writing quantum algorithms in Java, you’ll be building the applications that run them efficiently.

Conclusion: Bridging the Gap

Full stack developers won’t be rendered obsolete by quantum computing — quite the opposite. As the industry evolves, they’ll become even more essential in bridging the classical-quantum divide. Whether through RESTful APIs, secure cloud platforms, or hybrid architectures, full stack developers will help operationalize quantum capabilities.

To stay ahead, it's crucial to invest in holistic learning. Enrolling in theBest Full Stack Course in Pune or enhancing your backend proficiency via the Best Java Certification Course in Pune can give you a significant edge. The quantum future might still be emerging, but with the right skills, you'll be more than ready when it arrives.

[govindhtech]

Quantum Portfolio Optimizer: Global Data Quantum, IBM Qiskit

Portfolio optimisation for quantum computing

Global Data Quantum introduced the Quantum Portfolio Optimiser function in IBM Qiskit. Quantum computing optimises investment portfolios.

A detailed breakdown:

Quantum Portfolio Optimiser Goal

The Quantum Portfolio Optimiser optimises investment performance while reducing transaction costs and risks. Its dynamic portfolio optimisation goal is to find the optimum investment plan across many time periods to maximise projected return and minimise risks, often while considering budget, transaction costs, and risk aversion. Dynamic portfolio optimisation modifies assets based on asset performance, unlike traditional portfolio optimisation, which uses a single rebalancing time. The program targets analysts, investors, and portfolio managers. Portfolio optimisation allows backtesting trading approaches.

Quantum Portfolio Optimiser Access:

Discover the function in IBM Qiskit Functions Catalogue. This experimental functionality is only available to IBM Quantum Premium and Flex Plan users in preview release. Request a catalogue to access Global Data Quantum.

Quantum Computing—Why?

Traditional methods become slow and inefficient as resources or limits increase. Quantum computing's capacity to analyse several variables in parallel can solve complex problems faster and more efficiently than classical solvers like CPLEX, Gurobi, and Pyscf on HPC resources.

Quantum Portfolio Optimiser Functions?

The Quantum Portfolio Optimiser has four steps:

It receives financial asset values and user-specified investing conditions.

Quantum circuits convert classical input data into a quantum-resolution problem. This requires constructing the dynamic portfolio optimisation problem using Quadratic Unconstrained Binary optimisation (QUBO) and converting it into a quantum operator (Ising Hamiltonian).

The Variational Quantum Eigensolver (VQE) algorithm is considered. The VQE was designed to determine the optimal solution-wide investment combinations. In this hybrid quantum-classical approach, the quantum circuit estimates the cost function and Differential Evolution is used for classical optimisation.

Adjusting post-processing to eliminate quantum device noise yields an optimal, trustworthy, and realistic recommendation. For optimal output, the system uses noise-aware (SQD-based) post-processing.

Formulating Problems

Portfolio optimisation uses multi-objective Quadratic Unconstrained Binary Optimisation (QUBO). The QUBO function optimises four goals:

Max out the return function (F).

Reduce investment risk (R) and transaction costs.

Respect investment limits. The QUBO function is defined as O = -F + (γ/2)R + C + ρP, where γ is the risk aversion coefficient and ρ is the constraints reinforcement coefficient (Lagrange multiplier The minimum qubit count for a problem is the number of assets (na), time periods (nt), and bit resolution (nq) used to describe the investment.

Input

This function requires several input parameters:

A dictionary of asset prices uses dates as supplementary keys. All assets must have consistent data for the same dates.

Qubo_settings: A dictionary that configures the QUBO problem with parameters like nq resolution qubits, dt time window each step, maximum investment per asset, risk aversion coefficient, transaction charge, and restriction coefficient.

Optimizer_settings (Optional): Sets up the standard optimisation technique, including primitive settings (sampler_shots, estimator_shots) and differentiation_evolution algorithm parameters (num_generations, population_size).

ansatz_settings (Optional): Select “optimized_real_amplitudes” or “tailored” and enable multiple pass managers, dynamical decoupling, and other options to configure the quantum circuit ansatz.

Optional: QPU backend name, such as “ibm_torino.”

previous_session_id (Optional): A list of past session IDs to continue execution or retrieve data.

Apply_postprocess (Optional): True applies noise-aware SQD post-processing.

tags: An optional text list to label the experiment.

Output

Function returns two dictionaries: “result” and “metadata”.

Result: optimal optimisation outcomes, such as the optimal investment strategy over time and the lowest target cost. Investment weights are normalised by total investment.

Metadata: Metadata describes all optimisation results. It includes counts, investment pathways, objective costs, Sharpe ratios, returns, limitation violations, samples/states, and transaction costs. The session ID, asset order, QUBO matrix, and resource consumption summary are all included. Return, Sharpe ratio, restriction deviation, and least objective cost are key metadata for the best solution.

Application Function Context Qiskit

Application functions like the Quantum Portfolio Optimiser provide a comprehensive quantum pipeline by abstracting the quantum workflow. Because quantum methods use conventional classical inputs and return domain-familiar classical outputs, they can be easily integrated into present application processes without quantum computing knowledge.

Analysis of Performance and Benchmarks

The function is verified using different resolution qubit, ansatz circuit, and asset grouping configurations. Benchmarks evaluate solutions using two metrics:

Objective cost: To evaluate optimisation, the objective cost compares the cost function value to Gurobi (free version) output.

Sharpe ratio: Measures portfolio risk-adjusted return. Benchmark data shows the quantum optimiser finds viable investment plans. For a test using IBEX35 assets (Set 3, 4 time steps, 2-bit encoding, 56 qubits), the Optimised Real Amplitudes ansatz had an objective cost of -3.67 and a Sharpe ratio of 14.48, while Gurobi had 16.44 and -4.11. Comparing quantum sampling to random sampling, visual inspection shows that lower prices dominate the distribution.

[cybersecurityict]

Quantum Computing Market Size, Share, Analysis, Forecast, and Growth Trends to 2032: North America and Asia-Pacific

The Quantum Computing Market Size was valued at USD 1.03 Billion in 2023 and is expected to reach USD 10.31 Billion by 2032 and grow at a CAGR of 29.1% over the forecast period 2024-2032.

Quantum Computing Market is rapidly evolving, poised to revolutionize industries by delivering unprecedented computational power. With increasing investments from both public and private sectors, the market is witnessing accelerated innovation, especially across the USA and Europe. Leading technology firms and startups are pioneering advancements that promise to unlock new potentials in data analysis, cryptography, and artificial intelligence.

Quantum Computing Market continues to gain momentum as governments and enterprises in the USA and Europe intensify their focus on research and development. Strategic partnerships, increased funding, and robust infrastructure development are key drivers propelling market growth. The rising demand for enhanced processing capabilities to tackle complex problems positions quantum computing as a game-changer in global technology landscapes.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/2740 

Market Keyplayers:

IBM (IBM Quantum System One, Qiskit)

D-Wave Quantum Inc. (Advantage Quantum Processor, Leap Quantum Cloud Service)

Microsoft (Azure Quantum, Quantum Development Kit (Q#))

Amazon Web Services (Amazon Braket, Quantum Solutions Lab)

Rigetti Computing (Aspen Series Quantum Processors, Forest Development Kit)

Fujitsu (Digital Annealer, Quantum-Inspired Optimization Services)

Hitachi (Quantum Annealing System, CMOS-Based Quantum Computing)

Toshiba (Quantum Key Distribution (QKD) System, Quantum Cryptography Solutions)

Google (Sycamore Processor, Quantum AI Platform)

Intel (Horse Ridge Cryogenic Controller, Quantum Dot Qubits)

Quantinuum (H-Series Ion Trap Processors, Quantum Origin (QKD))

Huawei (HiQ Cloud Quantum Computing Service, Quantum Computing Simulator)

NEC (Quantum Annealing Cloud Service, Quantum Neural Network Solutions)

Accenture (Quantum Computing Consulting Services, Quantum Impact Simulation Tool)

Nippon Telegraph and Telephone (NTT QKD Platform, Quantum Node Integration)

Bosch (Quantum Sensing Devices, Quantum-Inspired Optimization Tools)

Quantum Computing Inc. (Qatalyst Software, Entropy Quantum Computing Platform)

PsiQuantum (Photon-Based Quantum Processors, Quantum Foundry Services)

Alpine Quantum Technologies GmbH (Ion Trap Qubit Solutions, Quantum Research Platform)

Xanadu (Borealis Quantum Processor, PennyLane Software)

Zapata Computing (Orquestra Platform, Quantum Workflow Automation Tools)

Northrop Grumman (Quantum Sensor Technologies, Advanced Quantum Communication Systems)

Market Analysis

The USA leads in quantum computing R&D funding, supported by initiatives such as the National Quantum Initiative Act.

Europe focuses on collaborative projects under the Quantum Flagship program to foster innovation across member states.

Both regions emphasize talent development and cross-industry collaboration to accelerate commercialization.

Key sectors adopting quantum technologies include finance, healthcare, cybersecurity, and logistics.

Market Trends in the USA and Europe

Surge in public-private partnerships to boost research capabilities and market readiness.

Expansion of quantum hardware and software startups driving competitive innovation.

Increasing government policies promoting quantum technology as a strategic priority.

Growth in quantum-as-a-service platforms enabling easier access for enterprises.

Heightened focus on quantum-safe cryptography to address emerging security challenges.

Market Scope Quantum computing is not just a futuristic concept but a tangible technology transforming multiple industries. In the USA and Europe, the market scope extends beyond pure research—targeting practical applications like drug discovery, financial modeling, and optimization problems. The convergence of cloud-based quantum services and AI integration opens new horizons, ensuring that quantum computing becomes an accessible and scalable solution for businesses aiming to gain competitive advantage.

Forecast Outlook Looking ahead, the quantum computing market in the USA and Europe is set for robust expansion, marked by increasing collaborations and commercialization efforts. As technological barriers are gradually overcome, expect a surge in industry-specific quantum solutions driving efficiency and innovation. The focus will remain on creating scalable quantum systems, expanding the ecosystem, and nurturing a skilled workforce, ensuring that quantum computing transitions from experimental to mainstream.

Access Complete Report: https://www.snsinsider.com/reports/quantum-computing-market-2740 

Conclusion The quantum computing market holds transformative potential for the USA and Europe, positioning these regions as global leaders in next-generation technology. For businesses and investors, this presents an exciting opportunity to harness cutting-edge advancements that will redefine problem-solving and competitive strategies.

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Contact Us:

Jagney Dave - Vice President of Client Engagement

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[lunarsilkscreen]

Quantum [Assembly Language]

There's a lot of confusion as to what people are talking about about when it comes to programming with qBits; and a lot of [verbage used by professionals] that seems very *rough* transitioning from Digital space to Quantum space.

There are two ways to approach the qAssembly; sometimes directly through Qiskit, Circle, and Q#, and other ways through standard high-level Binary Languages like Python.

Python has a library which allows access to the Quantum Assembly layer, and developers may have a preference with this kind of interface until qAssembly is more understood.

Now; Unlike Binary-Languages, which at their rudimentary level is all about pushing 1s and 0s in interesting ways; and has been built on top of with high-level languages (which don't particularly care that typical computer systems are in Binary) makes a bunch of different ways for developers to interact with computers these days.

Right now, qAssembly is only at the level of trying to figure out how to control and use logic gates. Again; they're not binary logic gates which are more readily understood these days; they're quantum logic gates.

And most of our understanding is through binary systems simulating a quantum environment.

And the way that we understand *how they work* is through vectors, angles, and rotational matrices.

I heavily suspect this complexity can be broken down into simpler concepts, especially as we get more used to developing with quantum systems.

For *most* developers; they won't really swap to quantum systems until higher-level languages are developed for the quantum computing landscape.

For cutting-edge developers; we're literally reinventing the wheel. We're developing Quantum Systems and therefore; Quantum Languages, from the ground up.

So a lot of these explanations are very rough shod if you're already a software development expert. And aren't familiar with how Assembly and Binary Systems were developed originally.

However; if you've a basic understanding of the history, were looking much at the same paths that binary computers have taken to get here.

We have Quantum Systems (Very Large due to need for Super Cooled environments) and aren't readily accessible by the laymen (hence the simulated languages on binary systems).

The current way qAssembly works is by literally following a qBit through the various logic gates and seeing what they do.

And for many applications; if you actually understand the logic; it can have exponential speed gains in many applications in comparison to binary systems.

Yet; that's due to the qBit being a fundamentally different [object] than the Binary Bit. Binary bits can be 0 or 1 (low or High) yet a qBit is simply... Well ... It can be any sided Dice you want, as long as you label your states correctly.

This was a way to explain *where* our understanding of quantum computers are at currently.

[atplblog]

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[codefrombasics12345]

Quantum Computing & Python: What Developers Need to Know

Quantum computing is one of the most exciting frontiers in technology, promising to revolutionize industries by solving problems that are currently impossible for classical computers. Python, being a versatile and widely used programming language, has become the go-to tool for quantum computing simulations and research. In this blog, we’ll explore the fundamentals of quantum computing, how Python fits into the ecosystem, and what developers should know to get started.

Understanding Quantum Computing Basics

Unlike classical computers that use bits (0s and 1s), quantum computers use qubits, which leverage the principles of superposition and entanglement to process information in fundamentally different ways. This allows quantum computers to perform calculations exponentially faster for specific tasks like cryptography, optimization, and complex simulations.

Superposition – A qubit can exist in multiple states (0 and 1) simultaneously, enabling parallel computations.

Entanglement – Qubits can be interconnected, meaning the state of one qubit can instantly influence another, even over vast distances.

Quantum Gates – Instead of traditional logic gates, quantum computing relies on quantum gates that manipulate qubits in unique ways.

Python’s Role in Quantum Computing

Python plays a crucial role in quantum computing, providing user-friendly libraries and frameworks for quantum programming. Here are some of the most popular quantum computing libraries in Python:

Qiskit (by IBM) – A powerful open-source framework for working with quantum computers and simulators.

Cirq (by Google) – Focuses on building, simulating, and running quantum circuits.

PennyLane (by Xanadu) – An advanced library for quantum machine learning.

QuTiP – Designed for quantum physics simulations.

Getting Started with Quantum Computing in Python

If you’re a Python developer looking to explore quantum computing, follow these steps:

Install Qiskit – pip install qiskit

Create a Simple Quantum Circuit

from qiskit import QuantumCircuit circuit = QuantumCircuit(2) circuit.h(0) # Apply Hadamard gate circuit.cx(0, 1) # Apply CNOT gate print(circuit)

Run on a Simulator or Real Quantum Computer – IBM provides free cloud access to quantum computers via IBM Quantum Experience.

Future of Quantum Computing

Quantum computing is still in its early stages, but its potential impact is vast. Fields like cryptography, AI, drug discovery, and climate modeling stand to benefit immensely. As quantum hardware advances, Python developers equipped with quantum programming skills will be in high demand.

Conclusion

Quantum computing is no longer just theoretical—it’s becoming an essential technology for the future. Python developers interested in innovation should start exploring quantum programming today. With tools like Qiskit and Cirq, diving into quantum computing has never been easier. Stay ahead of the curve and be part of the quantum revolution!

Python Training In Chennai

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[devipatil]

Quantum Literacy in Education: Preparing Students for a Quantum-Powered World

Quantum technology is no longer a distant dream—it’s a reality that’s transforming industries like healthcare, finance, and cybersecurity. As the world moves toward a quantum-powered future, the need for quantum literacy is growing rapidly. To prepare the next generation for this new era, schools and universities are integrating quantum concepts into their curricula, equipping students with the skills they need to thrive in a quantum-driven world. Here’s how education is adapting to this technological shift.

How Quantum Concepts Are Being Integrated into Education

Educational institutions are taking proactive steps to introduce students to quantum computing and its applications. Here’s a look at how they’re making it happen:

1. Adding Quantum Computing to STEM Curricula

Universities such as MIT, Stanford, and Caltech have introduced quantum computing courses as part of their physics, computer science, and engineering programs.

High schools are also beginning to incorporate quantum concepts into their STEM education, often through collaborations with tech companies and research organizations.

2. Hands-On Learning with Quantum Tools

Platforms like IBM Quantum Experience and Qiskit are being used in classrooms to give students hands-on experience with quantum programming and algorithms.

Quantum simulators and cloud-based quantum computers are becoming essential tools, allowing students to experiment with real-world quantum applications.

Key Initiatives Driving Quantum Education

Several programs and partnerships are making quantum literacy more accessible to students at all levels:

1. The Qubit by Qubit Program

This global initiative, led by The Coding School, offers free quantum computing courses to high school and university students. The program has reached over 10,000 students worldwide, providing foundational knowledge and practical experience.

2. Collaborations Between Tech Companies and Schools

Companies like IBM, Google, and Microsoft are partnering with educational institutions to develop quantum curricula and provide access to quantum computing resources.

For example, IBM’s Quantum Educator Program trains teachers to deliver quantum computing lessons, ensuring students receive high-quality instruction.

3. Government Support for Quantum Education

Programs like the U.S. National Quantum Initiative and the EU’s Quantum Flagship include funding for educational initiatives aimed at building a quantum-ready workforce.

Success Stories: Students Thriving Through Quantum Literacy

Quantum literacy programs are already making a significant impact, opening doors to new opportunities and career paths. Here are a few inspiring examples:

1. High School Students Solving Real-World Problems

A group of high school students in the U.S. participated in the Qubit by Qubit program and developed a quantum algorithm to optimize traffic flow in their city. Their project won a national science competition and earned them scholarships to top universities.

2. University Students Leading Quantum Research

At the University of Waterloo, undergraduate students used IBM’s quantum computers to simulate molecular interactions, contributing to groundbreaking research in drug discovery. Their work was published in a leading scientific journal.

3. Encouraging Diversity in Quantum Computing

Programs like IBM’s Quantum Women’s Summit are inspiring more young women to pursue careers in quantum computing. One participant, a college sophomore, interned at a quantum startup and is now pursuing a PhD in quantum physics.

Call-to-Action: Advocate for More Quantum Education Funding

While progress is being made, there’s still much to be done to ensure quantum literacy becomes a standard part of education. Here’s how you can contribute:

Support Educational Programs: Advocate for increased funding for quantum education initiatives at local, national, and international levels.

Foster Partnerships: Encourage collaborations between schools, universities, and tech companies to expand access to quantum resources.

Raise Awareness: Educate others about the importance of quantum literacy and its potential to transform industries and create new opportunities.

Conclusion: INA Solutions’ Role in Shaping the Quantum Future

At INA Solutions, we are committed to empowering businesses and communities with emerging technologies like quantum computing. Our mission is to optimize processes, uncover insights, and drive growth through innovative solutions, expert knowledge, and exceptional service. While we are not an educational institution, we recognize the critical role that education plays in preparing the next generation for a quantum-powered world.

We support initiatives that promote quantum literacy and innovation, whether through strategic consulting, training programs, or partnerships with educational organizations. By working together, we can ensure that quantum literacy becomes a cornerstone of education and a driver of future success.

[aishwaryaanair]

AI and Quantum Computing: Certifications Shaping the Future of Tech

The convergence of artificial intelligence (AI) and quantum computing represents a groundbreaking shift in technology. Together, these fields promise to solve problems once considered insurmountable, from advanced drug discovery to optimizing global supply chains. As this fusion reshapes industries, professionals equipped with the right knowledge and certifications will lead the way in innovation.

In this article, we explore how AI and quantum computing are transforming the tech landscape, the certifications helping professionals excel in this space, and why investing in education is vital to staying ahead of the curve.

The Synergy of AI and Quantum Computing

AI excels in pattern recognition and decision-making by processing large amounts of data. Quantum computing, on the other hand, leverages quantum mechanics principles to perform complex calculations at unprecedented speeds.

When combined, AI and quantum computing offer immense potential:

Accelerated Machine Learning: Quantum algorithms can speed up AI training processes, leading to faster insights.

Enhanced Problem Solving: From financial modeling to climate simulations, the combined power of AI and quantum computing solves problems traditional computing cannot handle.

Optimized Resource Allocation: Industries like logistics and energy benefit from quantum-optimized AI models, improving efficiency.

This synergy underscores the importance of acquiring skills in both AI and quantum computing — a demand that specialized certifications aim to fulfill.

Top Certifications in AI and Quantum Computing

1. AI+ Quantum™ by AI Certs

AI+ Quantum™ is an advanced course that bridges the gap between AI and quantum computing, making it an ideal choice for professionals aiming to lead in this transformative era.

Key Features:

Covers quantum computing fundamentals and their applications in AI.

Explores quantum machine learning algorithms and their potential use cases.

Hands-on workshops and case studies for real-world application.

Why Choose AI+ Quantum™:

This certification empowers professionals with the skills to understand and implement quantum-enhanced AI solutions, making them indispensable in future tech landscapes.

Use the coupon code NEWCOURSE25 to get 25% OFF on AI CERTS’ certifications. Don’t miss out on this limited-time offer! Visit this link to explore the courses and enroll today.

2. Quantum Computing for Artificial Intelligence by edX

This course, offered by prominent institutions, provides an excellent foundation in quantum computing with a focus on its integration with AI.

Key Features:

An introduction to quantum mechanics and quantum computing basics.

Applications of quantum algorithms in AI and data analysis.

Practical insights into using quantum platforms like IBM Q.

Why Choose This Certification:

The course caters to professionals looking for a solid understanding of quantum computing and its real-world implications in AI.

3. Quantum Machine Learning by Coursera (offered by the University of Toronto)

This program dives into the intersection of machine learning and quantum computing, making it ideal for those aiming to specialize in quantum AI.

Key Features:

Comprehensive modules on quantum-enhanced learning techniques.

Exposure to quantum programming languages like Qiskit.

Collaboration with experts in the field for practical projects.

Why Choose This Certification:

This course offers an in-depth look at how quantum computing enhances machine learning processes, preparing professionals for cutting-edge research and development roles.

Why AI and Quantum Computing Certifications Matter

Competitive Advantage:

With industries rapidly adopting quantum-enhanced AI, certified professionals are in high demand. Certifications validate your expertise, setting you apart in a competitive job market.

Future-Proof Skills:

AI and quantum computing are at the forefront of tech innovation. Learning these skills ensures you remain relevant as these technologies evolve.

Interdisciplinary Expertise:

Understanding the intersection of AI and quantum computing positions you as a leader capable of tackling multifaceted challenges.

Career Growth:

Certifications in AI and quantum computing open doors to roles like Quantum AI Specialist, Quantum Data Scientist, and AI Research Scientist.

Industries Driving AI and Quantum Computing

Several industries are already leveraging the power of AI and quantum computing, highlighting the need for skilled professionals:

Healthcare: Revolutionizing drug discovery and genomic analysis.

Finance: Enhancing risk modeling and fraud detection with quantum algorithms.

Energy: Optimizing renewable energy grids and reducing costs.

Logistics: Solving complex routing and scheduling problems.

Professionals in these sectors benefit immensely from certifications that provide hands-on experience and domain-specific knowledge.

How to Choose the Right Certification

When selecting an AI and quantum computing certification, consider the following factors:

Career Goals:

Identify whether you aim to specialize in research, development, or strategic implementation.

Program Content:

Look for certifications that balance theoretical knowledge with practical applications.

Reputation:

Opt for programs from credible organizations or academic institutions.

Flexibility:

Choose courses that accommodate your schedule, especially if you are working full-time.

Industry Recognition:

Certifications like AI+ Quantum™ by AI Certs are widely recognized and valued by employers.

The Road Ahead for AI and Quantum Computing

As quantum computing becomes more accessible, its integration with AI will unlock unprecedented possibilities. Some emerging trends include:

Quantum Neural Networks: Pushing the boundaries of AI capabilities.

AI-Driven Quantum Simulations: Revolutionizing industries like material science and climate modeling.

Democratization of Quantum AI Tools: Making these technologies accessible to businesses of all sizes.

Professionals who invest in certifications now will be at the forefront of these advancements, driving innovation in their respective fields.

Conclusion

AI and quantum computing are not just technological advancements; they are paradigm shifts that redefine what is possible. For tech professionals, understanding these technologies is essential for driving innovation and solving complex challenges.

Certifications like AI+ Quantum™ by AI Certs, Quantum Computing for Artificial Intelligence by edX, and Quantum Machine Learning on Coursera equip professionals with the skills to lead in this transformative era.

By gaining expertise in these fields, you position yourself as a pioneer, ready to shape the future of technology. Don’t just witness the revolution — be part of it.

Invest in an AI and quantum computing certification today, and take the first step toward a career that defines the future.

[job-seekers-page]

IT Sectors jobs in Jobseekerspage.com

JobseekersPage.com offers IT sector job listings across various roles, including software development, cybersecurity, and data analytics. The platform supports advanced search filters for location, experience level, and job type, simplifying the job search process for candidates. Visit JobseekersPage.com to explore current opportunities and apply.

IT (Information Technology) sectors offer a diverse range of jobs, covering everything from software development to cybersecurity. Below is a breakdown of key areas within the IT sector and the types of jobs commonly found in each:

1. Software Development

Roles:

Front-end Developer

Back-end Developer

Full-stack Developer

Mobile App Developer

Game Developer

Skills Required:

Programming languages (e.g., Java, Python, JavaScript, C++)

Frameworks (e.g., React, Angular, Spring)

Version control (e.g., Git)

2. Data Science and Analytics

Roles:

Data Scientist

Data Analyst

Machine Learning Engineer

Business Intelligence Analyst

Skills Required:

Data analysis tools (e.g., Excel, Tableau, Power BI)

Programming languages (e.g., Python, R)

Machine learning frameworks (e.g., TensorFlow, Scikit-learn)

SQL and data visualization

3. Cybersecurity

Roles:

Cybersecurity Analyst

Ethical Hacker/Penetration Tester

Security Architect

Incident Response Analyst

Skills Required:

Network security

Ethical hacking tools (e.g., Metasploit, Wireshark)

Certifications (e.g., CISSP, CEH, CompTIA Security+)

4. Cloud Computing

Roles:

Cloud Engineer

Cloud Architect

DevOps Engineer

Site Reliability Engineer (SRE)

Skills Required:

Cloud platforms (e.g., AWS, Azure, Google Cloud)

Containerization tools (e.g., Docker, Kubernetes)

Automation and CI/CD pipelines

5. Networking

Roles:

Network Engineer

Network Administrator

Wireless Communication Specialist

Skills Required:

Networking protocols (e.g., TCP/IP, DNS)

Hardware configuration

Certifications (e.g., CCNA, CCNP)

6. Artificial Intelligence and Robotics

Roles:

AI Engineer

Robotics Engineer

Natural Language Processing (NLP) Specialist

Skills Required:

AI tools (e.g., OpenAI, IBM Watson)

Robotics programming (e.g., ROS, MATLAB)

Deep learning techniques

7. IT Management and Support

Roles:

IT Project Manager

IT Support Specialist

System Administrator

Database Administrator

Skills Required:

Project management methodologies (e.g., Agile, Scrum)

ITIL and service management

Database tools (e.g., Oracle, MySQL)

8. Web Development

Roles:

Web Developer

Web Designer

SEO Specialist

Skills Required:

HTML, CSS, JavaScript

Content management systems (e.g., WordPress)

SEO and web analytics

9. Emerging Technologies

Roles:

Blockchain Developer

IoT Engineer

Quantum Computing Researcher

Skills Required:

Blockchain frameworks (e.g., Ethereum, Hyperledger)

IoT platforms and sensors

Quantum programming (e.g., Qiskit)

10. Product and UX Design

Roles:

UX/UI Designer

Product Manager

Interaction Designer

Skills Required:

Design tools (e.g., Figma, Adobe XD)

User research and prototyping

Agile product development

11. IT Consulting

Roles:

IT Consultant

Systems Analyst

Technology Strategist

Skills Required:

Business acumen

Systems integration

Technical writing and documentation

[ai-certs]

AI+ Quantum Certification: Your Pathway to Quantum Computing Excellence

Introduction

Quantum computing is revolutionizing the tech industry, offering unparalleled problem-solving capabilities in fields ranging from cryptography to artificial intelligence. As businesses and research institutions embrace this transformative technology, there’s a growing demand for professionals who can bridge the gap between artificial intelligence (AI) and quantum computing. The AI+ Quantum Certification is a game-changing program designed to equip you with the skills and knowledge necessary to excel in this cutting-edge domain.

Unlock a 25% discount on all AI certification courses with code AICERT25 at checkout! Enhance your AI skills with our expert-led certifications designed to advance your career. Don’t miss out on this limited-time offer – sign up now at - https://www.aicerts.ai/ai-certification/

Why AI+ Quantum Certification Matters

As the world shifts towards adopting quantum technologies, having a certification that combines AI and quantum computing is no longer just an advantage—it’s a necessity. AI+ Quantum Certification validates your expertise, making you stand out in a competitive job market. It provides you with a comprehensive understanding of quantum computing principles, AI applications, and how these technologies converge to address real-world challenges.

Key Benefits of AI+ Quantum Certification

Mastery of Quantum and AI Fundamentals This certification ensures you gain a deep understanding of quantum mechanics and AI algorithms. From learning about quantum circuits to exploring quantum neural networks, the program is tailored to help you navigate the complexities of these advanced technologies.

Practical, Hands-On Experience The program emphasizes practical learning through hands-on labs and projects. Participants work with quantum simulators and frameworks like Qiskit and TensorFlow Quantum, enabling them to apply theoretical concepts to solve real-world problems effectively.

Career Advancement Opportunities Quantum computing and AI are among the most lucrative tech fields. Holding an AI+ Quantum Certification opens doors to high-paying roles such as Quantum AI Engineer, Quantum Data Scientist, and Quantum Researcher in top companies and institutions.

Who Should Pursue AI+ Quantum Certification?

This certification is ideal for:

IT Professionals seeking to expand their expertise in cutting-edge technologies.

Data Scientists who want to leverage quantum computing for advanced analytics.

Students and Researchers interested in gaining a competitive edge in quantum and AI fields.

Whether you’re a seasoned professional or just beginning your journey, the AI+ Quantum Certification is tailored to suit varying skill levels.

Real-World Applications of AI+ Quantum Certification

Healthcare and Drug Discovery With quantum computing, simulating complex molecular interactions becomes feasible. AI+ Quantum-certified professionals can contribute to breakthroughs in drug discovery and personalized medicine.

Cryptography and Cybersecurity Quantum algorithms like Shor's and Grover's are reshaping encryption and security. Certification holders can lead advancements in secure communication systems.

Financial Modeling and Optimization Quantum computing is transforming risk management and portfolio optimization. Professionals with AI+ Quantum Certification are well-positioned to tackle these challenges effectively.

Conclusion

The AI+ Quantum Certification is not just a credential—it’s your gateway to a thriving career in quantum computing and AI. By mastering the integration of these groundbreaking technologies, you can position yourself at the forefront of innovation, driving solutions to some of the world’s most pressing challenges.

Invest in your future with the AI+ Quantum Certification and join the next generation of tech leaders transforming industries worldwide.

[isfeed]

IBM makes developing for quantum computers easier with the Qiskit Functions Catalog

IBM today launched the Qiskit Functions Catalog, a new set of services that aims to make programming quantum computers easier by abstracting away many of the complexities of working with these machines. “I do think it’s the next big transition ince we put the quantum computer on the cloud,” Jay Gambetta, IBM’s VP in charge […] © 2024 TechCrunch. All rights reserved. For personal use only. Source:…

[govindhtech]

PyQBench: Quantum Noise-based Qubit Fidelity Benchmark

PyQBench

Qubit readings on noisy devices are confirmed by open-source Python utility PyQBench.

Researchers released a crucial update to PyQBench, an open-source Python framework for assessing gate-based quantum computers. A major innovation to the newest edition is qubit von Neumann measurement certification. This addition is necessary to evaluate Noisy Intermediate-Scale Quantum (NISQ) devices.

The noise, errors, and low qubit counts of NISQ devices make  quantum computing unreliable. Benchmarking techniques are becoming more important to evaluate their performance. Quantum volume metrics, randomised benchmarking, and cross-entropy benchmarking provide useful data, however they often focus on device features or gate-level faults. Precise qubit measurement assessment is critical since errors add up and limit gadget capability.

The new PyQBench uses a qubit von Neumann measurement certification approach to evaluate NISQ device correctness. This expands PyQBench's von Neumann measurement discrimination functionality. Researchers can better assess quantum measurement quality via certification.

Core certification uses hypothesis-testing. It determines if a gadget is measuring the null hypothesis (such the computational Z-basis) or another one. To do this, an initial state must be established, the first qubit measured, and then a second, auxiliary qubit measured based on the first.

Final measurement results accept or reject the null hypothesis. To reduce the risk of type II error (accepting the null hypothesis when the alternative is true) for a given type I error.

Present NISQ devices cannot do conditional measurements, making direct implementation of this technology challenging. PyQBench avoids this via postselection and direct sum. Both methods adapt the strategy to available hardware.

PyQBench is versatile due to its Python modules and simple CLI. The Python package lets users build circuits, integrate error models, specify specific measurement schemes, benchmark on chosen backends, and assess results.

For parametrised Fourier family qubit von Neumann measurements, the CLI simplifies benchmarking scenarios. The CLI supports synchronous or asynchronous benchmarking, asynchronous process monitoring, work outcome resolution, and data tabulation.

The current version integrates with the Qiskit library to handle IBM Q devices enabling seamless benchmarking on quantum technology.

Benchmarking on IBM Q devices shows certification program success. In parametrised Fourier family of observations type II error probability experiments, empirical results match theoretical expectations. Deviations are caused by readout errors, decoherence, and quantum noise.

Most importantly, error mitigation strategies improved experimental data accuracy. Mthree mitigation reduced experimental and theoretical value deviations, especially in low-type II error probability conditions. This highlights the importance of noise-aware approaches. Error mitigation improves results when the ideal probability is near zero and uncontrolled data deviate more. The architecture was resilient since the mean absolute error was always less than 0.01.

PyQBench provides reliable performance metrics in the NISQ era with adaptive and reliable benchmarking tools. It helps develop strategies to reduce mistakes and verify quantum measurements.

PyQBench may be updated to support other quantum hardware platforms and improve error mitigation and benchmarking.

PyQBench's open-source code on GitHub encourages community collaboration and quantum device benchmarking advancements.