Dr. Milton Mattox’s Post

#snsinstitutions #snsdesignthinkers #designthinking Topic : Quantum computing Quantum computing is a revolutionary field of computing that leverages the principles of quantum mechanics, the fundamental theory that governs the behavior of particles at very small scales, such as atoms and photons. Unlike classical computers, which use bits to process information as either 0 or 1, quantum computers use quantum bits or qubits, which can exist in multiple states simultaneously due to the phenomenon of superposition The key concepts and principles behind quantum computing: 1. Superposition A qubit can represent both 0 and 1 simultaneously until it is measured. This allows quantum computers to process vast amounts of information in parallel. 2. Entanglement Qubits can be entangled, meaning the state of one qubit is directly related to the state of another, even if they are separated by large distances. This allows for highly correlated operations that are not possible with classical bits. 3. Quantum Interference Quantum computers use interference to amplify the probability of correct solutions while canceling out incorrect ones. 4. Quantum Gates Quantum gates manipulate qubits through operations like rotations, entanglements, and measurements. These gates are the building blocks of quantum circuits, similar to logic gates in classical computing. Advantages of Quantum Computing Speed: For certain problems (e.g., factorizing large numbers, searching databases), quantum computers can potentially solve them exponentially faster than classical computers. Optimization: Quantum computers excel at solving complex optimization problems in logistics, finance, and material science. Cryptography: They have the potential to break widely used encryption schemes, while also enabling quantum-resistant cryptographic methods. Simulation: They can model molecular and quantum systems, aiding drug discovery and materials science. Challenges Decoherence: Qubits are extremely sensitive to their environment, leading to loss of information. Error Correction: Quantum error correction is challenging but essential for reliable computations. Scalability: Building and maintaining large-scale quantum computers is technically complex and expensive. Current State of Quantum Computing Companies like IBM, Google, Rigetti, and others are actively developing quantum hardware and software. Quantum supremacy, the milestone where a quantum computer performs a calculation infeasible for classical computers, was achieved by Google in 2019 for a specific task. Quantum computers are not yet practical for general-purpose computing but are being explored for niche applications. Potential Applications Cryptography (breaking or securing codes) Drug discovery and materials science Optimization in supply chains Financial modeling and risk analysis AI and machine learning improvements Would you like to dive deeper into any specific aspect or application of quantum computing

Hi Folks, I’ll be starting a new series of posts where I’ll try to break down tech ( it could be almost anything from system design to DSA ) in simple terms, like I am explaining it to a 5 year old and would try to corellate it with its real world application. Lets start with Quantum computers Quantum Computers - Part 1 : Imagine if Computers Were Superheroes! Let’s picture a regular computer like a librarian who organizes books one at a time. Pretty fast, right? Now, a quantum computer is like a librarian with superpowers who can check all the books at once! 🎩✨ How Quantum Computers Are Different Normal computers think in 0s and 1s (like flipping switches ON and OFF). Quantum computers, on the other hand, think in qubits – these can be 0 and 1 at the same time! It’s a bit like how a spinning coin can be heads and tails until it lands. This lets them look at many possibilities at once. What Could They Do at Full Power? If quantum computers reach their full potential, they could solve huge problems way faster than today’s supercomputers. Imagine: • Finding new medicines by instantly testing tons of molecules to see which ones fight diseases best. • Securing information with super-safe encryption to protect your data online. • Solving traffic jams by figuring out the best routes for every car on the road in a flash. Working Quantum computers are built on principles from quantum mechanics, the science that describes how very tiny particles (like atoms and electrons) behave. This world is totally different from our everyday experiences, and quantum computers take advantage of some special tricks of quantum mechanics to work. Here’s a breakdown of the key principles and how they relate to quantum computing: 1. Qubits: Quantum’s Building Blocks How it works: While a classical bit is either 0 or 1, a qubit can be in a state that’s both 0 and 1 at the same time. This is known as superposition, and it allows quantum computers to perform many calculations simultaneously. 2. Superposition: Multiple States at Once Example: Imagine you’re trying to find a treasure in a maze. A regular computer would go down each path until it found the treasure. But a quantum computer, with its qubits in superposition, could explore all paths simultaneously, finding the treasure much faster. 3. Entanglement: Linked Qubits How it works: By entangling qubits, quantum computers can perform complex calculations across multiple qubits as if they’re a single unit. This interconnectedness enables faster and more powerful computations. 4. Interference: Steering Toward the Right Answer Quantum computers also use interference to help find the right answers to problems. They amplify the probabilities of correct answers and cancel out the probabilities of incorrect ones. Interference is like nudging the system toward the best solution. Stay tuned and follow for posts like this and more. #TechBlogs #QuantumComputing #Computers

Quantum computing has seen significant advancements recently, and several key developments are reshaping its trajectory. Some of the most notable breakthroughs in 2023-2024 include: Fault-Tolerant Quantum Computing: One of the biggest challenges in quantum computing has been error rates due to qubit instability. Recent progress in fault-tolerant quantum computing (FTQC) is making strides toward mitigating these errors. In 2023, researchers from Google Quantum AI demonstrated a small-scale logical qubit with error correction capabilities. This is a crucial step toward building larger, more reliable quantum systems that can perform useful computations. Quantum Supremacy Milestones: Google’s Sycamore processor previously claimed quantum supremacy by solving specific problems that would take classical computers thousands of years to solve. Now, IBM’s Eagle processor and other quantum platforms are pushing the boundaries, achieving supremacy on more complex problems. This helps validate the practical use cases for quantum computing beyond academic experiments. Quantum Advantage in Industry: Various industries are moving beyond theoretical research and piloting quantum algorithms for real-world applications: Pharmaceuticals: Quantum computers are being used to model complex molecules for drug discovery, with companies like Pfizer and Roche collaborating on quantum projects. Financial Services: JP Morgan Chase and HSBC are using quantum algorithms to optimize portfolios, manage risk, and enhance cryptographic security. Materials Science: Researchers are using quantum computers to simulate new materials with unique properties for applications in electronics, energy storage, and more. Quantum Internet and Communication: Development of the quantum internet is progressing, which would allow secure communication using the principles of quantum entanglement. China remains a leader in this field, with long-distance quantum communication experiments, including a satellite-based quantum network that transmitted quantum information over 1,200 kilometers in 2023. Europe and the U.S. are also investing heavily in this area, with DARPA’s Quantum Network Testbed making headlines. Hybrid Classical-Quantum Systems: Hybrid systems that combine classical and quantum computers are gaining traction. These systems allow for better optimization of complex problems by offloading parts of computations that are quantum-suitable while letting classical computers handle the rest. IBM's Qiskit Runtime platform exemplifies this trend, enabling efficient resource sharing between the two computing paradigms. #HybridClassicalQuantumSystems #QuantumInternetCommunication #QuantumAdvantageinIndustry #QuantumSupremacyMilestones #FaultTolerantQuantumComputing #Quantumcomputing #quantumphysics #quantummechanics #quantumin2024

IBM Quantum Boldly Goes Where No One Has Gone Before: Landmark Error Correction Paper Published on Nature 🚀💻🔒 https://lnkd.in/d5f7EPTX IBM has achieved a groundbreaking milestone in quantum computing research, publishing a paper on Nature (https://lnkd.in/d9rRcKQk) that showcases a new quantum error-correcting code 📄🔒 The code, called the "gross code" (https://lnkd.in/dq6JpA3F) is 10 times more efficient than prior methods and paves the way for running quantum circuits with billions of gates 💻🌟 The paper details the team's mathematical analysis and implementation of the code, which can correct errors in quantum information with a high threshold and large code distance 🔍🔒 The code uses a low qubit overhead, making it a promising solution for practical implementation in quantum computing systems 💻🧩 This breakthrough innovation is part of IBM's broader strategy to bring useful quantum computing to the world 🌎💻 The company is committed to advancing error correction techniques and exploring new codes that can overcome the challenges of quantum noise and errors 🌟🔒 The potential applications of this technology are vast, from solving complex problems in chemistry and materials science to optimizing supply chain logistics and financial portfolios 💡💻 With the power of quantum computing, we can unlock new possibilities and transform industries 🌟💥 IBM's quantum computing roadmap outlines the company's plan to continuously improve quantum computers over the next decade 🌆💻 This new paper is a significant step towards achieving that goal 💻💥 Join IBM on this exciting journey into the quantum realm and discover the limitless possibilities of quantum computing 🌌💻 #IBMQuantum #QuantumComputing #ErrorCorrection #Nature #Innovation #Technology #Future

Optimization problems are often cited as good candidates for quantum computing, but the execution time for constrained combinatorial optimization applications on quantum devices can be problematic. However, a new algorithm developed by researchers from Waseda University is set to change that. The post-processing variationally scheduled quantum algorithm (pVSQA) combines variational scheduling with a post-processing method that transforms infeasible solutions into feasible ones, allowing for near-optimal solutions for constrained combinatorial optimization problems (COPs) on both quantum annealers and gate-based quantum computers. “The researchers analyzed the performance of this algorithm using both a simulator and real quantum devices such as a quantum annealer and a gate-type quantum device. The experiments revealed that pVSQA achieves a near-optimal performance within a predetermined time on the simulator and outperforms conventional quantum algorithms without post-processing on real quantum devices,” explains Tatsuhiko Shirai, a leader in the work. The potential applications for this new algorithm are vast, with COPs being common in logistics, supply chain management, machine learning, material design, and drug discovery. Read more about this groundbreaking development in the EEE Transactions on Quantum Engineering this month or check out the brief account of the work posted on the Waseda University website.

0012: Variational Quantum Eigensolver (VQE) - Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the ground state energy of a quantum system. VQE leverages the variational principle, which states that the ground state energy of a system is the minimum expectation value of the Hamiltonian, and uses a quantum computer to evaluate this expectation value. VQE is particularly useful in quantum chemistry for finding the ground state energies of molecules, which is a computationally intensive task for classical computers. Below is an example of implementing VQE using IBM's Qiskit. In this example, we perform the following steps: ## Quantum Phase Estimation Quantum Phase Estimation (QPE) is a crucial algorithm in quantum computing with wide-ranging applications in chemistry, cryptography, and beyond. It allows us to estimate the phase (or eigenvalue) of an eigenvector of a unitary operator. QPE is the backbone of many quantum algorithms, such as Shor's algorithm for factoring and algorithms for solving linear systems. Below is a step-by-step implementation of the Quantum Phase Estimation algorithm using IBM's Qiskit. In this example, we perform the following steps: ✅ Hamiltonian Definition: We define the Hamiltonian of the system. Here, we use a simple Hamiltonian H=ZI+IZ+ZZH = ZI + IZ + ZZH=ZI+IZ+ZZ. ✅ Variational Ansatz: We create a quantum circuit for the variational ansatz using the EfficientSU2 circuit from Qiskit's circuit library. ✅ Simulator Initialization: We initialize the AerSimulator to simulate the quantum circuit. ✅ Optimizer Definition: We use the COBYLA optimizer to find the parameters that minimize the expectation value of the Hamiltonian. ✅ Objective Function: We define an objective function that computes the expectation value of the Hamiltonian for given parameters. ✅ Parameter Optimization: We optimize the parameters of the variational ansatz to minimize the objective function. ✅ Results and Visualization: We print the optimized parameters and the corresponding minimum energy. We also visualize the final optimized circuit. VQE is a powerful technique that bridges quantum and classical computing, enabling us to solve complex problems in quantum chemistry and other fields. By understanding and implementing VQE, we can harness the power of quantum computing to find solutions to challenging computational problems. Stay tuned for more quantum computing concepts and implementations! #QuantumComputing #Qiskit #VQE #QuantumChemistry #TechInnovation

A collaborative study led by Algorithmiq and IBM Quantum researchers has demonstrated that current quantum computers, utilizing up to 91 qubits, can effectively simulate many-body quantum chaos—a complex phenomenon involving unpredictable behaviors in systems with numerous interacting particles. The team employed dual-unitary circuits to model this chaotic behavior and applied tensor-network error mitigation techniques to address computational noise, thereby enhancing result accuracy. These findings suggest that even in their developmental stages, quantum computers hold significant potential for tackling intricate problems in fields such as weather forecasting, fluid dynamics, and materials science. For more details, please continue reading the full article under the following link: https://lnkd.in/etsyGhPc -------------------------------------------------------- Please consult also the Quantum Server Marketplace platform for the outsourcing of computational science R&D projects to external expert consultants through remote collaborations: https://lnkd.in/eRmYbj4x #materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation

As we start our project quantum computing was dominating the era of new generation computing Build up a structure of knowledge for humanity for generation to generation Question is if quantum computing is enough for a light beam network or a space encryption We start doing a new experiment based on certain quantum properties and we found out it is not enough Quantum computers will break encryption one day. But converting data into light particles and beaming them around using thousands of satellites might be one way around this problem Currently, messaging technology relies on mathematical, or cryptographic, methods of protection, including end-to-end encryption. This technology is used in WhatsApp — as well as by corporations, the government and the military — to protect sensitive data from being intercepted Encryption works by scrambling data or text into what appears to be nonsense, using an algorithm and a key that only the sender and recipient can use to unlock the data. These algorithms can, in theory, be cracked. But they are designed to be so complex that even the fastest supercomputers would take millions of years to translate the data into something readable quantum computing will change the equation. Although the field is young, scientists predict that such machines will be powerful enough to easily break encryption algorithms someday. This is because they can process exponentially greater calculations in parallel (depending on how many qubits they use), whereas classical computers can process calculations only in sequence The subatomic world is similar. Albert Einstein won a Nobel Prize for proving that energy is quantized. Just as you can only buy shoes in multiples of half a size, so energy only comes in multiples of the same "quanta" — hence the name quantum physics The quanta here is the Planck constant, named after Max Planck, the godfather of quantum physics. He was trying to solve a problem with our understanding of hot objects like the sun. Our best theories couldn’t match the observations of the energy they kick out. By proposing that energy is quantized, he was able to bring theory neatly into line with experiment Wave-particle duality is an example of superposition. That is, a quantum object existing in multiple states at once. An electron, for example, is both ‘here’ and ‘there’ simultaneously. It’s only once we do an experiment to find out where it is that it settles down into one or the other This makes quantum physics all about probabilities. We can only say which state an object is most likely to be in once we look. These odds are encapsulated into a mathematical entity called the wave function. Making an observation is said to ‘collapse’ the wave function, destroying the superposition and forcing the object into just one of its many possible states This idea is behind the famous Schrödinger’s cat thought experiment I sunny faridi Resemble with the idea of futuristic technology to take a great effort In computing

A collaborative study led by Algorithmiq and IBM Quantum researchers has demonstrated that current quantum computers, utilizing up to 91 qubits, can effectively simulate many-body quantum chaos—a complex phenomenon involving unpredictable behaviors in systems with numerous interacting particles. The team employed dual-unitary circuits to model this chaotic behavior and applied tensor-network error mitigation techniques to address computational noise, thereby enhancing result accuracy. These findings suggest that even in their developmental stages, quantum computers hold significant potential for tackling intricate problems in fields such as weather forecasting, fluid dynamics, and materials science. For more details, please continue reading the full article under the following link: https://lnkd.in/e6YPm5hW -------------------------------------------------------- Please consult also the Quantum Server Marketplace platform for the outsourcing of computational science R&D projects to external expert consultants through remote collaborations: https://lnkd.in/eCb9Tanv #materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation

For Day 2 of #Quantum30 Cohort 5, I studied 1.1 and 1.2 sections of Quantum Computer Systems Research for Noisy Intermediate Scale Quantum Computers. Birth of Quantum Computer: Influenced by the classical reversible Turing Machines, Paul Benioff put forward the possibility of building a Quantum Computer which resulted in Quantum Turing Machine. (Turing Machines are mathematical models, only used for computable machines, and thus not fulfilling its conditions shows that a decision problem is not computable.) Some uses include material and chemistry computation which otherwise would be impractical due to the probabilistic nature of electrons; and prime factorization of very large numbers which could take billions of years for a classical computer (but posing a risk to our private information relying on the difficulty of prime factorization) Quantum Computer and Types of Models: A quantum computer is a device that uses qubits to represent information and uses quantum phenomena to perform instructions on the qubits. There are three types of Quantum Computers: 1.      Analog QC: The state of a system is evolved by quantum operations (note: a qubit is a two-level state system). It can further be divided into two more categories: Adiabatic Quantum Computing where the state is not allowed to evolve and thus remains in the ground state; and Quantum Annealing where the system is allowed to evolve by interaction with the thermal environment. 2.      Digital Gate-based QC: The information is encoded in a discrete and finite set of qubits, as well as the quantum operations are divided into sequential quantum gates. In both the above cases, the desired outcome is the final outcome with the highest probability.   3.      Measurement-based QC: In this model, the qubits are initiated in a cluster state i.e., a multi-qubit state. For example, starting with l+> and then applying controlled-Z gate to the states. After taking measurement in some basis, the outcome is fed back into the system taking the basis from the previous measurement.    Errors in QC: While in a classical computer, an error is unlikely to happen in billions of device hours, however, Quantum computers are prone to errors depending on the model. For instance, an adiabatic QC (part of analog QC) is less sensitive to qubit decoherence than a digital QC. This is because, in an adiabatic QC, it helps the system reduce to its ground state, whereas in a digital QC, it is mostly undesirable except during initialization and measurement. However, the discretization of information in digital QC allows the discretization of errors, leading to the concept of Quantum Error Correction. Main source: Ding, Y., & Chong, F. T. (2020). Quantum Computer Systems: Research for noisy Intermediate-Scale Quantum Computers. Synthesis Lectures on Computer Architecture, 15(2), 1–227. Other sources: 1. McMahon, D. (2007). Quantum Computing explained. 2. Turing machine. Wikipedia. #Quantum30 Quantum Computing India