Una W’s Post

Quantum Computing and Machine Learning: Promising Intersection Explored in New Study The intersection of quantum computing and machine learning is a promising area of research, with the potential to revolutionize many industries. Quantum computers, known as Noisy Intermediate Scale Quantum (NISQ) devices, can overcome limitations of classical computing, but are susceptible to noise. Machine learning, a problem-solving approach where machines learn to tackle tasks by processing large volumes of data, faces challenges due to the substantial need for data and computational resources. This study explores the effectiveness of hybrid quantum-classical algorithms on small-scale quantum devices, revealing comparable or superior performance to classical algorithms. https://lnkd.in/ec8dJvcg

Day 11 of 30: In recent years Quantum computing is gaining popularity because of its peculiar paradigm in computing. The working of quantum computing utilizes the quantum mechanics principles like Superposition, Entanglement, interference, and entropy. Because of superposition principle, the quantum computer can solve the problem in exponential speed while the same problem the classical supercomputer took hundreds of years. a typical example is the factoring a very large number. Even though the quantum computers are very fast in computing, but there are certain drawbacks because of quantum hardware issues which almost all companies are focusing to enhance their quantum hardware. Machine learning is one of the areas where quantum computer has application to solve the machine learning problem. Because of huge data points and robust algorithms classical computer take much more time to solve. So, Quantum computer can train and learn the machine learning problem using its Quantum parallelism property. How to transform oneself to Quantum Machine Learning? 1. Learn about the basics of Quantum Computing: Learning the basics principles of quantum computing helps one to better  understand how Quantum Machine learning works and how it may be used 2. Keep an eye on developments in the field 3. Learn classical machine learning 4. Explore about how quantum machine learning might be applied in your industry Computer Packages for Quantum Machine Learning 1. Qi skit: is an open-source quantum computing framework for writing, running, and debugging quantum programs. 2. IBM Quantum Experience: we can experiment with quantum algorithms using the IBM's quantum computer. It provides variety of quantum simulators and quantum hardware backends. 3. ProjectQ: An open-source quantum computing framework for writing and running quantum program. It includes tools. for working with quantum circuits and algorithms as well as a variety of quantum simulators and quantum hardware backends. #quantumComputingIndia #quantummechanics #quantummachinelearning #quantumphysics

"Microsoft demonstrated a case study combining HPC, quantum computing, and AI to study catalytic reactions, using logical qubits to improve the reliability of quantum simulations." (The Quantum Insider) PAPER: End-to-End Quantum Simulation of a Chemical System FROM ABSTRACT: We demonstrate the first end-to-end integration of high-performance computing (HPC), reliable quantum computing, and AI in a case study on catalytic reactions producing chiral molecules. We present a hybrid computation workflow to determine the strongly correlated reaction configurations and estimate, for one such configuration, its active site's ground state energy. We combine 1) the use of HPC tools like AutoRXN and AutoCAS to systematically identify the strongly correlated chemistry within a large chemical space with 2) the use of logical qubits in the quantum computing stage to prepare the quantum ground state of the strongly correlated active site, demonstrating the advantage of logical qubits compared to physical qubits, and 3) the use of optimized quantum measurements of the logical qubits with so-called classical shadows to accurately predict various properties of the ground state including energies. The combination of HPC, reliable quantum computing, and AI in this demonstration serves as a proof of principle of how future hybrid chemistry applications will require integration of large-scale quantum computers with classical computing to be able to provide a measurable quantum advantage. Read the full paper here: https://lnkd.in/g7RNwYqq #quantumcomputing #artificialintelligence #quantumchemistry #quantumsimulation #ai

Microsoft has demonstrated a case study combining HPC, quantum computing, and AI to study catalytic reactions, using logical qubits to improve the reliability of quantum simulations 👏🏻 The study involved over a million density functional theory (DFT) calculations using the Azure Quantum Elements platform to map out reaction networks, identifying over 3,000 unique molecular configurations. https://lnkd.in/esfps8hC #QuantumComputing #Microsoft #Ai #ChemicalReactions

Tensor Computing vs Quantum Computing: Tensor computing and quantum computing are two distinct fields with different applications. Tensor networks, such as MPS, PEPS, TTNs, and MERA, have been successfully used in classical machine learning and quantum machine learning, where they can be mapped to quantum computers for improved performance. These tensor networks are efficient for preparing ground states on classical computers and can be combined with quantum processors for tasks like time evolution, which can be intractable on classical computers. On the other hand, quantum computers aim to outperform classical computers in various computational tasks by utilizing the principles of quantum mechanics.  Quantum algorithms, such as the quantum Fourier transform, Grover's algorithm, and the quantum counting algorithm, have been simulated on noisy quantum processors to understand their limitations and prospects. Therefore, tensor computing and quantum computing serve different purposes, with tensor networks applying to classical and quantum machine learning, while quantum computers aim to achieve quantum advantage in various computational tasks. It's important to recognize that while tensor networks can be mapped to quantum computers for improved performance, quantum computers themselves are designed to leverage the principles of quantum mechanics to outperform classical computers in various computational tasks.  Quantum algorithms like the quantum Fourier transform, Grover's algorithm, and the quantum counting algorithm have been studied extensively on noisy quantum processors to understand their limitations and potential. In summary, quantum computing offers exponential speedups but is still in development while tensor computing using tensors is already mainstream for AI applications. The two represent different paradigms - quantum mechanics vs multidimensional arrays. Their applications and maturity differ significantly though both offer enhanced computation capabilities. #tensor #quantum #tensorcomputing #quantumcomputing

Unlocking Quantum Computing’s Potential: Future Directions in Machine Learning by Quantum News via Quantum Zeitgeist, Quantum Computing and QML ([Global] Quantum Computing) URL: https://ift.tt/mRP9UjQ The use of machine learning in quantum computing has significant potential to enhance the measurement process, enabling faster readout times and improved fidelity. Researchers can develop more sophisticated CNN architectures specifically designed for analyzing neutral atom qubit readout data, improving the accuracy and reliability of qubit state measurements. Additionally, exploring other machine learning algorithms like RNNs or LSTM networks can also improve the accuracy and reliability of qubit state measurements. The future directions for machine learning in quantum computing are vast and exciting, with potential applications in achieving scalability and quantum error correction. https://ift.tt/b8isVWw

Researchers at the University of Innsbruck have developed a new method using machine learning to create tailored quantum circuits, advancing the potential of quantum computing. This innovation, featured in Nature Machine Intelligence, could lead to significant progress in the field. #QuantumComputing #MachineLearning #Innovation #UniversityOfInnsbruck #NatureMachineIntelligence

🔍 What are Quantum Circuit Born Machines (QCBMs)? QCBMs are a powerful tool for loading classical data into quantum computers, enabling a wide range of quantum algorithms and applications, critical to our work with our partners in making useful quantum applications 💻🔌 QCBMs can handle diverse data types, from vectors and matrices to images and molecular states! QCBMs learn a quantum circuit that prepares a quantum state mirroring the desired data. This allows complex data to be efficiently encoded into the quantum system. However, traditional QCBMs often struggle with scalability and require an exponential number of gates. 🚧 At BlueQubit, we've developed an enhanced approach to QCBMs that: ✅ Introduces a hierarchical learning method for training deep variational circuits ✅ Scales to large quantum circuits with up to 30 qubits and 1000 parameters ✅ Significantly improves the efficiency and accuracy of quantum data loading 📈 By addressing the challenge of quantum data loading, we not only facilitate the practical application of quantum computing across various fields but also pave the way for exponential speedups in fundamental operations such as linear algebra, matrix multiplication, and vector products. 🧠🧪🌌 Curious to learn more? Check out our blog post for an in-depth exploration of QCBMs: https://lnkd.in/epygXCWH

An Introduction to Quantum Computers and Quantum Coding "At the core of quantum computing is the qubit, which serves as the fundamental unit of computation in a quantum computer– taking the place of a classical computer’s bit. While the bit can occupy either the 0 or 1 state exclusively, the qubit can be in a superposition of the 0 and 1 states (Microsoft, n.d.). It can be very hard to conceptualize the qubit; where the classical bit is simply an electric current or absence of electric current, the qubit can take many different physical forms." "Another key difference between the classical and quantum computer is the logic gates: while classical computers use AND, OR, NOT, etc. to perform basic logic operations, quantum computers use quantum logic gates such as X, Hadamard, Toffoli, and CNOT (Wikipedia, 2024). These quantum gates are used to perform logical operations on a single qubit or a very small number of qubits, and can be combined with others to perform more complex operations and manipulations." "A third major difference between classical and quantum computing is the presence of quantum phenomena such as superposition, superconduction, entanglement and interference. These properties are used in different ways depending on the methods used to perform quantum computations (Microsoft Azure, 2024). Another property that is present is quantum decoherence, which poses a serious problem to the development of useful or widespread quantum computing. Quantum decoherence is when a particle in superposition interacts with its environment and becomes entangled with the environment, ultimately interfering with the computational outcome (Brandt, 1999)." By Oliver W. Johnson at Towards Data Science Link https://lnkd.in/djjsh8eN

Quantum Computing Boosts Pattern Recognition Accuracy by 100%: Study Finds by Quantum News via Quantum Zeitgeist, Quantum Computing and QML ([Global] Quantum Computing) URL: https://ift.tt/rh2Maq4 Can Quantum Computing Enhance Pattern Recognition? The integration of quantum computing with machine learning algorithms has the potential to revolutionize various fields, including pattern recognition. By leveraging the principles of quantum mechanics, quantum computers can process vast amounts of data exponentially faster than classical computers. This property makes them particularly suitable for complex tasks like pattern recognition. In this study, researchers employed a quantum computational approach to enhance Adaline and Hebbian algorithms, achieving remarkable accuracy rates in test outcomes. The findings highlight the potential benefits of integrating quantum computing with machine learning algorithms in pattern recognition applications. https://ift.tt/1nZUuHB