Quantum Computers Run Circuits, Not Code

I’ve decided to take a big step forward in my career as a developer: I am officially starting my journey into Quantum Computing! The shift from classical to quantum programming is mind-blowing. Moving from deterministic bits to the world of superposition and entanglement feels like learning a whole new dimension of logic. Based on my initial research, I’ve mapped out the core prerequisites I need to master: 1️⃣ Python – The backbone of quantum frameworks like Qiskit and Cirq. 2️⃣ Mathematics (Linear Algebra) – Understanding vectors, matrices, and complex numbers to navigate the Bloch sphere. 3️⃣ Gate Logic – Shifting from if/else to "Thinking in Gates" (Hadamard, CNOT, etc.). 4️⃣ Theoretical Physics Concepts – Grasping the fundamentals of how quantum states actually behave. It’s a challenging roadmap, but I’m incredibly excited about the potential of this technology to solve problems that are currently impossible for classical computers. Any tips, resource or recommendations are welcome! If you are already working in the field, studying it, or just curious about the quantum future, let’s connect! I’d love to share insights and learn from this amazing community. #QuantumDev #FutureTech #LearningJourney #SoftwareDevelopment

Exploring quantum computing beyond qubits with qutrits. We have been developing QutritLab, a Python library for simulating ternary quantum systems—and this visualization is one of our favorite results so far. In this experiment, we simulated a 3-qutrit circuit where each step applies parameterized phase rotations. While these phase shifts are invisible on their own, once combined through interference, they produce rich, evolving structures in the probability distribution. What emerges is something striking: a wave-like interference pattern across the 27-dimensional state space, where peaks form, move, and interact over time. These patterns aren’t random—they’re the result of: • coherent phase accumulation • interference between qutrit basis states • and the higher-dimensional structure of ternary quantum systems It’s essentially a glimpse into how information propagates and interferes in higher-dimensional quantum spaces. We are excited about where this can go: from richer quantum algorithms to more efficient representations of information. If you're interested in quantum simulation, higher-dimensional systems, or just cool interference patterns. #quantumcomputing #computerscience #mathematics #qc #quantummechanics #quantumphysics #technology

I am pleased to announce the publication of my first arXiv preprint: "Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking." Efficient data encoding has long been a bottleneck in hybrid quantum-classical algorithms, with traditional simulators spending significant time converting classical features into quantum rotations. To address this, our team developed the Hybriqu Encoder, a Rust-based, SIMD-aware kernel that focuses exclusively on angle encoding and integrates seamlessly with Python via CFFI. By processing multiple double-precision rotations simultaneously using AVX-class vector lanes, utilizing pre-calculated trigonometric factors, and optimizing for cache performance, we achieved a 5.4% speedup at 64 qubits on Apple Silicon. This performance scales effectively as data exceeds the L1 cache size, demonstrating that vectorization provides consistent improvements for compute-bound encoding tasks. Our benchmarking also identified that kernels applying rotations to the entire state vector remain heavily limited by memory bandwidth rather than compute power. This highlights a clear path for future optimization regarding state updates and processing methods in larger hybrid quantum-classical systems. A huge thank you to my co-authors Irwan Alnarus Kautsar, Gunawan Witjaksono, and Haza Nuzly Bin Abdull Hamed for their collaboration and support on this work. You can read the full preprint here: https://lnkd.in/gKMNt57B #QuantumComputing #MachineLearning #RustLang #HighPerformanceComputing #SIMD #QuantumSimulation #ArXiv

Exploring quantum computing beyond qubits with qutrits. I’ve been developing QutritLab, a Python library for simulating ternary quantum systems—and this visualization is one of my favorite results so far. In this experiment, I simulated a 3-qutrit circuit where each step applies parameterized phase rotations. While these phase shifts are invisible on their own, once combined through interference, they produce rich, evolving structures in the probability distribution. What emerges is something striking: a wave-like interference pattern across the 27-dimensional state space, where peaks form, move, and interact over time. These patterns aren’t random—they’re the result of: • coherent phase accumulation • interference between qutrit basis states • and the higher-dimensional structure of ternary quantum systems It’s essentially a glimpse into how information propagates and interferes in higher-dimensional quantum spaces. I’m excited about where this can go: from richer quantum algorithms to more efficient representations of information. If you're interested in quantum simulation, higher-dimensional systems, or just cool interference patterns—let’s connect. #quantumcomputing #computerscience #mathematics #qc #quantummechanics #quantumphysics #technology

Can #QuantumComputing help econometricians do their work better? Maybe someday. Carlos Madeira, Yash Rastogi, Harald Uhlig and I explore in a new Bank for International Settlements – BIS working paper how quantum computing could support #BayesianInference. We present a proof-of-concept #Quantum algorithm that performs posterior sampling, ie a step-by-step process for estimating possible outcomes based on new data and prior knowledge. We do so by showing code listings using the #Qiskit package in the #Python programming language. Importantly, our method does not yet offer faster computation than classical techniques such as Markov Chain Monte Carlo, importance sampling or particle filtering. But the approach demonstrates the feasibility of doing #Bayesian inference with quantum computation simulation. https://lnkd.in/e8ppawuG

Quantum-Supported Bayesian Inference Source: Bank for International Settlements – BIS Working Paper (Jon Frost, Carlos Madeira, Yash Rastogi, Harald Uhlig, et al.) Overview This proof-of-concept (#PoC) explores the #integration of #QuantumComputing into #econometrics. Specifically, it investigates how #quantumalgorithms can be applied to #BayesianInference, a statistical method essential for #economic #forecasting and #riskassessment. The study serves as a foundational #bridge between #theoretical #quantummechanics and #practical #macroeconomic #modeling. Core Objectives . Feasibility Testing: To determine if #quantum #architectures can successfully #execute the #mathematical rigors of #posteriorsampling. . Algorithmic Development: The creation of a novel #quantumalgorithm designed to #estimate #outcomes by #combining #prior #knowledge with #new #data. . Open-Source Implementation: Providing code listings via the Qiskit (Python) framework to allow the broader research community to simulate and verify the findings. Key Findings . Functional Viability: The researchers successfully demonstrated that Bayesian inference can be performed within a quantum computation simulation environment. . Performance Comparison: Currently, the quantum method does not outperform classical statistical techniques. Established methods such as Markov Chain Monte Carlo (MCMC), importance sampling, and particle filtering remain more efficient on traditional hardware. . Technical Framework: The study utilizes "posterior sampling," a step-by-step process that allows econometricians to update probability distributions as new economic data becomes available. Strategic Implications While the technology is not yet at the "quantum advantage" stage (where it beats classical computers), this paper establishes the theoretical groundwork for future applications. As #quantum #hardware matures and error rates decrease, the algorithms developed in this PoC could eventually allow for: . Increased Model #Complexity: Processing more variables than currently possible. . Enhanced #Precision: Better capturing "tail risks" or extreme economic events. . Real-time #PolicyAnalysis: Faster processing of global data for #centralbank #decisionmaking. Conclusion The paper concludes that while #quantumcomputing is not a current #replacement for classical #econometrictools, it is a #viable future-state #technology. This #PoC marks a significant #shift from "if" #quantumcomputing can be used in #economics to "how" it will be implemented as #hardware #capabilities catch up to the theory. Thanks for sharing Jon Frost

Excited to share our latest publication: 🎉 GRAPHCODEBERT-GAT: Hierarchical Graph Attention Networks for Explainable Neural Code Retrieval Our approach captures both local variable interactions and global semantic dependencies across multiple programming languages, enabling more robust and interpretable code intelligence. This work advances explainable and efficient neural code retrieval, particularly for cross-language scenarios.

A young engineering student once struggled to simulate a simple physical system. Equations filled his notebook, but the results remained unclear. One day, he wrote a few lines of Python to model the system. Suddenly, the equations came alive. Waves moved, particles interacted, and predictions appeared on his screen. Physics was no longer abstract — it became visual, programmable, and powerful. Python and AI Methods for Physics and Engineering brings this same transformation to students, researchers, and engineers. It shows how Python and Artificial Intelligence can be used to model physical systems, solve engineering problems, and turn theory into real computational solutions. Don’t just solve equations. Simulate them. Train models with them. Engineer the future with them. Step into the new era of Physics and Engineering — where science meets Python and intelligence meets innovation. 🚀📘https://lnkd.in/d8nZNyc7  #Python #ArtificialIntelligence #Physics #Engineering #ComputationalScience #FutureOfEngineering

LLMs are not an alien technology, they are the continuation of the power of text: books, Wikipedia, LLMs… and the continuation of the power of malleable computation: programming languages, spreadsheets, LLMs. Crossing text and computation is a multiple decade endeavors that was explored by software engineers, digital artists, computational designers, computer scientists of all kind, and it was statisticians who found the first general purpose application!

Quantum error correction shouldn’t be manual. But today, it is. Most quantum stacks like Qiskit and Q# push error correction into libraries, forcing developers to hand-build syndrome circuits with no compile-time guarantees. So I built something different: QSHL (Quantum Self-Healing Language) A programming language where error correction is built in—not bolted on. What’s different: → Heal Blocks Define error correction once. The compiler generates the full syndrome + correction loop automatically. → Linear Qubit Ownership Prevents use-after-measurement and invalid quantum states at compile time. → Coherence-Aware Compilation If your program exceeds the hardware’s coherence window, it fails before it runs. → Rust-native pipeline No Python overhead. Low-latency classical control for real-time decoding. Results: In simulation, QSHL achieved a 55.6% error “heal rate” at a 5% gate error, completing correction cycles within the coherence window using a Sparse Blossom decoder. We’ve filed a provisional patent on the architecture at Korelis Labs. I’m currently exploring QIR / OpenQASM 3.0 integrations and early pilot opportunities with teams working on real quantum systems. If you’re in the quantum space—let’s talk. #QuantumComputing #QEC #RustLang #DeepTech #KorelisLabs