Qubit Design Basics

Tell me about QUANTUM COMPUTING in 2-minutes or less, using language my kid can understand. Challenge accepted. This was a question I got recently in a Q&A. I tried to channel my inner Hemingway. Big ideas, small words and short sentences! So if you fancy learning something new today - here's my take, and some useful resources worth checking out if you want a deeper dive. ⬇️ Imagine a computer that doesn’t just think in ones and zeros, like the ones we use today. A quantum computer uses "qubits" instead of bits. A bit can be a 1 or a 0. But a qubit can be both at the same time — this is called "superposition". It’s like flipping a coin and having it be heads and tails until you look.   Quantum computers also use something called entanglement. When two qubits are entangled, what happens to one instantly affects the other, even if they’re far apart. This lets quantum computers connect ideas in powerful new ways.   Because of superposition and entanglement, a quantum computer can explore many answers at once instead of one by one. That makes it super fast for some problems. It could help discover new medicines, protect data (search “quantum safe”), fight climate change, or even train smarter (ethical) AI.   But quantum computers are very hard to build. Qubits are delicate and can lose their power if they get too hot or too noisy. Scientists all over the world are racing to make them stronger and more stable. Quantum computers have to be kept at extremely low temperatures (-459°F) which is even colder than in outer space!   If they succeed, quantum computers could solve problems so big that today’s fastest supercomputers would take thousands of years to finish. Quantum computers won’t replace classical computers – but they will help us to solve many problems that we’ve never been able to solve before.   Quantum computers are not just faster – they give us a whole new way to understand the world. [263 words / 2 minutes] ⬇️ Want a Deeper Dive? 🥶 WATCH: Quantum computers exaplained by MKBHD [17 mins] https://lnkd.in/eNdRycfu 📒 READ: Wired's Easy Guide to Quantum Computing - Why It Works & How It Could Change The World https://lnkd.in/eiuAHxnQ 📖 FREE book "The Quantum Decade" from IBM Institute for Business Value https://lnkd.in/ejMCnKTX 🗺️ FUTURE: The Next 5 Years? Technology Atlas by IBM https://lnkd.in/ePaWdATp 📝 LEARN: 10 FREE courses (Most courses cost $2,500+ These 10 will get you started) https://lnkd.in/eM3k-Dtt

We are pursuing quantum computing because there’s evidence that quantum can solve certain problems exponentially faster than any classical computer. I’m excited to share a new algorithm from our team with the potential for an exponential speedup in a real-world use case: simulating electric circuits. Circuits built from resistors, inductors, and capacitors — RLC circuits — show up across engineering, from power grids to analog filters to integrated circuit design. Predicting how voltages and currents evolve in these systems is routine. But as circuits grow large and complex, those simulations can become increasingly expensive on classical hardware. What makes RLC circuits so challenging to simulate is that they aren’t described by ordinary differential equations (ODEs), but by differential-algebraic equations (DAEs): systems that combine equations describing time evolution with constraints that must be satisfied at every instant. In the case of RLC circuits, we must solve Kirchhoff’s laws of charge and voltage conservation at every junction, but standard ODE solvers struggle to handle this mixed structure. A new paper authored by Arkopal Dutt, Anirban Chowdhury, Kristan Temme, and Hari Krovi, presents the first quantum algorithm tailored to DAEs of this kind. The approach separates the circuit’s state into two parts: one that evolves dynamically over time, and another that is fixed by the constraints. Each part is then handled with the appropriate technique. The result is an algorithm that prepares a quantum state encoding the circuit’s full time evolution, with a runtime that scales only polylogarithmically in the number of nodes — an exponential improvement over the polynomial worst-case scaling of classical methods. This speedup applies to well-conditioned networks where the circuit can be queried in superposition, meaning its structure is accessed as a function that returns entries on demand, rather than being read out element by element. From the quantum computer’s output state (the state encoding the full solution), physically meaningful quantities, like the energy stored in a set of capacitors or dissipated across a set of resistors, can be extracted directly. Interestingly, the authors also show that this energy-estimation task is as powerful as quantum computation itself: a quantum computer can solve it efficiently, and any problem that admits an efficient quantum solution can be reduced to an instance of it. In complexity-theoretic terms, this implies that, under standard assumptions, no classical algorithm can match a quantum computer on this task. Classical circuit simulation has been a workhorse of electronic design for decades. Demonstrating a provable quantum advantage on a problem this practical is an exciting step, and it lines up closely with IBM Quantum’s broader goal of identifying where quantum computing can deliver real value in engineering and industrial settings.   Full paper: https://lnkd.in/ekTFap64

Excited to announce a new #QuantumComputing result from JPMorganChase's Global Technology Applied Research, titled “Fast Convex Optimization with Quantum Gradient Descent,” which has just appeared on arXiv! Convex #optimization is a fundamental subroutine in #MachineLearning, engineering, and #DataScience, with many applications in financial engineering. We develop new #QuantumAlgorithms in the “derivative-free” setting where the algorithm only uses the function value and not its gradient. We show that #quantum algorithms without gradient access can match the convergence of classical gradient-descent methods, which do assume gradient access! In the derivative-free setting, this translates to an exponential speedup in terms of the dimension.   Our results also have applications outside the black-box setting. By leveraging a connection between semi-definite programming and eigenvalue optimization, we develop algorithms that exhibit the best known quantum or classical runtimes for semi-definite programming, linear programming, and zero-sum games, which are the three most well-studied classes of structured convex optimization problems. These classes model many practical problems of interest, including portfolio optimization and least-squares regression problems. Coauthors: Brandon Augustino, Dylan Herman, Enrico Fontana, Junhyung Lyle Kim, Jacob Watkins, Shouvanik Chakrabarti, and Marco Pistoia. Link to the article: https://lnkd.in/eMtqXM-r

🌟 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠: 𝐓𝐡𝐞 𝐍𝐞𝐱𝐭 𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫 𝐢𝐧 𝐕𝐋𝐒𝐈 𝐚𝐧𝐝 𝐄𝐦𝐛𝐞𝐝𝐝𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 [𝐑𝐨𝐚𝐝𝐦𝐚𝐩] The tech world is on the brink of a paradigm shift—Quantum Computing is no longer a futuristic concept; it’s becoming reality. For those in VLSI and embedded systems, the emergence of quantum computing represents an exciting yet challenging transition. Are you ready to embrace this revolution? Quantum computers promise unparalleled computational power, redefining industries like cryptography, materials science, and AI. As the hardware landscape evolves, quantum-enhanced VLSI design and embedded systems for quantum devices are rapidly gaining importance. If you want to future-proof your career, now is the time to start learning. Roadmap to Be Job-Ready in Quantum Computing for VLSI and Embedded Roles 1️⃣ Understand the Basics • Learn the principles of quantum mechanics: qubits, superposition, and entanglement. • Explore introductory resources to grasp how quantum computing differs from classical computing. 📖 Quantum Computing for Everyone (MIT Press) 📖 Quantum Computing Fundamentals - Coursera 2️⃣ Dive into Quantum Programming • Learn quantum-specific programming languages like Qiskit, Cirq, and PyQuil. • Experiment with platforms like IBM Quantum Experience and Google Cirq. 📖 Qiskit Textbook 📖 Quantum Computing and Programming - Udemy 3️⃣ Understand Hardware Implications • Study quantum hardware systems and their requirements. • Focus on how quantum concepts impact semiconductor design, low-power circuits, and reliability in embedded systems. 📖 Introduction to Quantum Hardware - IBM 4️⃣ Bridge VLSI with Quantum • Learn about quantum-enhanced VLSI design and chip architecture. • Explore cryogenic CMOS, superconducting qubits, and error correction methods. 📖 Advancing Quantum Hardware - IEEE Papers 5️⃣ Develop Embedded Expertise • Understand the role of embedded systems in controlling quantum devices. • Focus on microcontroller interfaces, signal processing, and timing synchronization. 📖 Embedded Systems and Quantum Devices - EdX 6️⃣ Build Hands-On Experience • Collaborate on open-source quantum projects. • Participate in quantum hackathons to apply your skills. 🌐 Quantum Hackathons 7️⃣ Stay Updated • Follow advancements in quantum hardware and their impact on VLSI and embedded domains. • Join forums, attend conferences, and network with experts in the quantum space. 𝑲𝒆𝒚 𝑻𝒂𝒌𝒆𝒂𝒘𝒂𝒚👇 The integration of quantum computing with VLSI and embedded systems is inevitable. The skills you develop today could position you at the forefront of this transformation tomorrow. Are you ready to embrace the quantum leap? Let’s discuss and share resources to navigate this exciting journey! #QuantumComputing #VLSI #EmbeddedSystems #FutureOfTech #CareerGrowth

The Schrödinger Equation Gets Practical: Quantum Algorithm Speeds Up Real-World Simulations Quantum computing has taken a major leap forward with a new algorithm designed to simulate coupled harmonic oscillators, systems that model everything from molecular vibrations to bridges and neural networks. By reformulating the dynamics of these oscillators into the Schrödinger equation and applying Hamiltonian simulation methods, researchers have shown that complex physical systems can be simulated exponentially faster on a quantum computer than with traditional algorithms. This breakthrough demonstrates not only a practical use of the Schrödinger equation but also the deep connection between quantum dynamics and classical mechanics. The study introduces two powerful quantum algorithms that reduce the required resources to only about log(N) qubits for N oscillators, compared to the massive computational demands of classical methods. This exponential speedup could transform fields such as engineering, chemistry, neuroscience, and material science, where coupled oscillators serve as the backbone of real-world modeling. By bridging theory and application, this research underscores how quantum computing is redefining problem-solving in physics and beyond. With proven exponential advantages and the ability to simulate systems once thought computationally impossible, this quantum algorithm marks a milestone in quantum simulation, Hamiltonian dynamics, and real-world physics applications. The findings point toward a future where quantum computers can accelerate scientific discovery, optimize engineering designs, and even open new frontiers in AI and computational neuroscience. #QuantumComputing #SchrodingerEquation #HamiltonianSimulation #QuantumAlgorithm #CoupledOscillators #QuantumPhysics #ComputationalScience #Neuroscience #Chemistry #Engineering

China's Zuchongzhi-3 quantum computer and Google's Willow processor represent two contrasting approaches in the race for quantum supremacy. Both 105-qubit superconducting systems push computational boundaries but prioritize different technical milestones. Zuchongzhi-3, developed by Chinese researchers, demonstrates remarkable raw computational power. In a demanding random circuit sampling task, it processed one million samples using 83 qubits over 32 cycles in just a few hundred seconds—a feat that would take the world’s fastest classical supercomputer billions of years to replicate. This achievement highlights China’s rapid progress in scaling up quantum hardware and achieving unprecedented quantum speedup. In contrast, Google’s Willow chip focuses on improving reliability through advanced quantum error correction. Willow was the first to show that logical, error-corrected qubits can outperform physical qubits in fidelity, reaching a logical qubit error rate below 0.2% per cycle. While Willow’s coherence and gate fidelities are marginally higher, its main contribution is laying the groundwork for scalable, fault-tolerant quantum computing—an essential step for practical real-world applications. https://lnkd.in/dXJwYnxt

🚀 Exploring the Frontier of AI with Quantum Machine Learning (QML) 🚀 Let me share this comprehensive 260-page tutorial designed to bridge the gap between classical #machine #learning and #quantum computing. This resource dives deep into Quantum Machine Learning (QML), an evolving field with the potential to reshape the future of AI. 🔍 What is covered inside : • Foundational principles of QML • Representative algorithms and their potential applications • Critical insights on trainability, generalization, and computational complexity • A special focus on Quantum Transformers, the backbone of future quantum #LLMs • Practical code demos for hands-on learning: qml-tutorial.github.io Whether you’re an AI enthusiast, researcher, or quantum computing explorer, this tutorial is a new possible gateway to understanding how quantum technologies can elevate machine learning.✨ #QuantumMachineLearning #AI #QuantumComputing #QML #QuantumLLM #TechInnovation #ML

> Sharing Resource < I like the title of this one: "How to Train Your Dragon: Quantum Neural Networks" by Hao Zhang and Alex Kamenev Abstract: Training of neural networks (NNs) has emerged as a major consumer of both computational and energy resources. We demonstrate that quantum annealing platforms, such as D-Wave, can enable fast and efficient training of classical NNs, which are then deployable on conventional hardware. From a physics perspective, NN training can be viewed as a dynamical phase transition: the system evolves from an initial spin glass state to a highly ordered, trained state. This process involves eliminating numerous undesired minima in its energy landscape—akin to cutting off the ever-regenerating heads of a dragon. The advantage of annealing devices is their ability to rapidly find multiple deep states (dragon heads to be cut). We found that this quantum-assisted training achieves superior performance scaling compared to classical backpropagation methods, with a notably higher scaling exponent (1.01 vs. 0.78). It may be further increased up to a factor of 2 with a fully coherent quantum platform using a variant of the Grover algorithm. Furthermore, we argue that even a modestly sized annealer can be beneficial to train a deep NN by being applied sequentially to a few layers at a time. Link: https://lnkd.in/ebBUxAgG #quantummachinelearning #quantumneuralnetworks #neuralnetworks #ai #ml #research #paper

⚛️ Hybrid Sequential Quantum Computing 📑 We introduce hybrid sequential quantum computing (HSQC), a paradigm for combinatorial optimization that systematically integrates classical and quantum methods within a structured, stagewise workflow. HSQC may involve an arbitrary sequence of classical and quantum processes, as long as the global result outperforms the standalone components. Our testbed begins with classical optimizers to explore the solution landscape, followed by quantum optimization to refine candidatesolutions, and concludes with classical solvers to recover nearby or exact-optimal states. We demonstrate two instantiations: (i) a pipeline combining simulated annealing (SA), bias-field digitized counterdiabatic quantum optimization (BF-DCQO), and memetic tabu search (MTS); and (ii) a variant combining SA, BF-DCQO, and a second round of SA. This workflow design is motivated by the complementary strengths of each component. Classical heuristics efficiently find low-energy configurations, but often get trapped in local minima. BF-DCQO exploits quantum resources to tunnel through these barriers and improve solution quality. Due to decoherence and approximations, BF-DCQO may not always yield optimal results. Thus, the best quantum-enhanced state is used to continue with a final classical refinement stage. Applied to challenging higher-order unconstrained binary optimization (HUBO) problems on a 156-qubit heavy-hexagonal superconducting quantum processor, we show that HSQC consistently recovers ground-state solutions in just a few seconds. Compared to standalone classical solvers, HSQC achieves a speedup of up to 700× over SA and up to 9× over MTS in estimated runtimes. These results demonstrate that HSQC provides a flexible and scalable framework capable of delivering up to two orders of magnitude improvement at runtime quantum-advantage level on advanced commercial quantum processors. ℹ️ Chandarana et al - 2025

If you're looking for a quantum computing book that goes beyond surface level explanations and provides a strong foundation in both theory and implementation, 'Fundamentals of Quantum Computing: Theory and Practice' by Venkateswaran Kasirajan is worth adding to your reading list. Book Link: https://lnkd.in/ggQbkjEY Starting with the mathematical and quantum mechanical foundations, it gradually builds the knowledge required to understand modern quantum computing, making it suitable for students, software developers, researchers, and professionals entering the field. What I particularly appreciated is that the book doesn't stop at theory. It combines detailed explanations with practical implementations, allowing readers to explore quantum algorithms through working code on IBM Qiskit and Microsoft QDK. Some of the topics covered include: 🔹 Mathematical foundations and quantum mechanics 🔹 Quantum superposition, entanglement, interference, teleportation, and the no-cloning theorem 🔹 Quantum algorithms including Deutsch-Jozsa, Bernstein-Vazirani, Simon's Algorithm, Quantum Fourier Transform, Phase Estimation, Shor's Algorithm, Grover's Algorithm, and Quantum K-Means 🔹 Quantum cryptography and Quantum Key Distribution 🔹 Density Matrix and Stabilizer Formalisms 🔹 Adiabatic Quantum Computing and Quantum Annealing 🔹 Quantum Error Correction and Fault-Tolerant Quantum Computing 🔹 Surface Codes, Protected Qubits, and Topological Quantum Computing Rather than viewing quantum computers as black boxes, readers are introduced to multiple qubit technologies, including trapped-ion systems, superconducting transmons, quantum dots, and topological qubits. This provides valuable insight into how today's quantum computers actually work under the hood. The book also includes worked examples, illustrations, chapter-end exercises, reading guides, and references for deeper exploration, making it a strong resource for both structured learning and self-study. As quantum technologies continue to advance, understanding both the theoretical and practical aspects of the field is becoming increasingly important. This book does an excellent job of bridging that gap. Have you read it? What quantum computing books would you recommend to aspiring quantum professionals? Happy Learning! Save this post for later. Repost ♻️ for Quantum Learners! Check my profile for more resources on Quantum Tech! Follow Kiran Kaur Raina here: 📌LinkedIn: https://lnkd.in/gEpKMQ7z 📌YouTube: https://lnkd.in/gTTv2ewB 📌Topmate: https://lnkd.in/gDj-kmYW 📌Medium: https://lnkd.in/gWBppT7G 📌Instagram: https://lnkd.in/g8qZKHe7 📌Twitter/X: https://x.com/NatashiaKaur 📌Substack: https://lnkd.in/gZ2tsY6Q? 📌GitHub: https://lnkd.in/gRKc4vDt Follow NucleQi here: 📌Linkedin: https://lnkd.in/gSEEfXsD 📌Instagram: https://lnkd.in/gUNQHEej