Qutrits in Quantum Computing: Visualizing Higher-Dimensional Systems

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

🚀 What if geometry could unlock more efficient quantum computing? ⚛️ Meet QGeo—a Python package that turns abstract quantum complexity into computable geodesics, helping researchers design and analyze quantum circuits in entirely new ways. 📄 QGeo: A Python Package for Calculating Geodesic Control Functions for Quantum Computing ✍️ Sean T. Crowe et al. 🔗 https://brnw.ch/21x1SOY #QuantumTech #FutureComputing #PythonTools #QuantumPhysics #Algorithms #ScientificComputing

Day 20, 21 and 22 of Building Quantum Projects! Happy World Quantum Day my friends! I am celebrating this day with a very interesting project you all can learn from. To be honest this project stretched me! It took me 3 days to perfect it. In this project I had to find a way to simulate quantum concepts like superposition, entanglement and measurement in python, the CLI worked perfectly, so I thought to make it accessible on the web so that people can learn how it works and how to build theirs. Because it is world quantum day, I added a simulation to teach how to play the game. The game allows one to play with human and AI. I have also open sourced the codebase with detailed explanation of how I modelled the quantum concepts in python. Please star the repo and contribute, let's improve this and make it an impactful project for the quantum community. This all started from the tasks we were given in the GDG OAU QUANTUM COMPUTING COMMUNITY. The most supportive quantum community I have belonged to. GitHub Repo: https://lnkd.in/e-_KTrJ4 Live Link: https://lnkd.in/euK_HEbp #quantum #computing #programming #world #quantum #day

I am eager to announce my first journal paper "Two qubits spin entanglement in Coupled Quantum Dots within Strained silicon Quantum well" We did a theoretical work and numerical simulation on python coding. Our findings, how the Ge concentration does exactly tune the entanglement with the external magnetic field, why the Dresselhaus term is dominant in Si/SiGe heterostructure and How does the increasing Ge concentration affect the valley splitting and spin orbit safety conditions.

Excited to share our new preprint: "Discovering Quantum phenomena with Interpretable Machine Learning" https://lnkd.in/dnsaSjkr We introduce QDisc, a pipeline that combines variational autoencoders with symbolic regression to automatically extract interpretable physical descriptions directly from raw quantum data. Important: no prior knowledge required!! ⚛️ We tested it on three very different types of quantum data: experimental Rydberg-atom snapshots, classical shadows, and hybrid discrete-continuous fermionic measurements. In each case, the pipeline not only recovered the known physics but also revealed previously unreported features, including a corner-ordering pattern in the Rydberg platform. 💻 Importantly, we also release qdisc, an open-source Python library that makes the full pipeline accessible and easy to use: https://lnkd.in/d3xZ7AA9 🔎 This is the next step in our ongoing effort to let interpretable ML guide the discovery of new quantum phenomena and we believe there's much more to find as quantum simulators continue to scale. This work has been speareheaded by Paulin de Schoulepnikoff, and with the amazing help of Hendrik Poulsen Nautrup and Hans Briegel.

Quantum computers don’t run code. They run circuits. That’s the first mental shift you need to make. In classical computing, we write programs step by step instructions executed sequentially in languages like Python or C++. Quantum computing works differently. Instead of code, you design a quantum circuit a sequence of quantum gates applied to qubits. Each gate doesn’t “compute” in the classical sense. It transforms the state of a qubit shaping probabilities, not flipping bits. You’re not telling the computer what to do step by step. You’re constructing a system that evolves according to quantum mechanics. And at the end of the circuit? You measure. That’s when probabilities collapse into an actual result. This is why quantum programming feels less like coding… and more like designing an experiment. From instructions → to transformations From logic → to physics This is Day 2 of understanding how quantum computers actually compute. Next: What exactly is a quantum gate? #QuantumComputing #QuantumAlgorithms #QuantumCircuits #DeepTech #FutureOfComputing #EmergingTech #day2 #computation

A few years ago I started asking a simple question: what if zero wasn't the end of a computation, but the beginning of a trace? That question became Zero Domain Algebra: a formal framework where dividing by zero produces a labeled object instead of an exception, and where those objects can be combined, reduced, and under the right conditions, brought back into something meaningful. It's part mathematics, part philosophy, part engineering. The "Grand Absorber" (the absolute zero ○ that swallows everything) is very much intentional. The paper is now on Zenodo, with full proofs, a Python prototype, and deliberately open applications. Some bridges are left to the reader. 📄 https://lnkd.in/eD82fets Curious to hear from anyone working on formal verification, SIEM pipelines, numerical analysis, or algebraic structures — this might resonate. #Mathematics #AbstractAlgebra #ErrorProvenance #Observability #Research

Recent research has yielded significant advancements in our understanding of aging and lifespan. Utilizing the capabilities of Python, researchers are now able to analyze extensive datasets to explore the complexities surrounding immortality and the aging process. This innovative methodology facilitates the modeling of biological systems, helps identify critical factors influencing longevity, and may reveal pathways for extending lifespan. Given these advancements, the future of aging research appears promising, providing new insights into maintaining health and vitality in later years. For further details, please refer to the full article here: https://lnkd.in/ePmQYTyR #AgingResearch #Longevity #Bioinformatics #PublicHealth #PythonInResearch

This research pushes the Lorenz-63 system into an extreme turbulence regime (ρ=99.96) with 20% Gaussian noise. We demonstrate that RNN, GRU, and LSTM architectures act as robust non-linear observers, successfully decoupling deterministic physics from stochastic noise while maintaining topological integrity. Full technical details, analysis, and results are available in the attached paper. Links and Repositories: Source Code: https://lnkd.in/dMZ69YNT. Official DOI: https://lnkd.in/dBMYCgFJ #DeepLearning #DynamicSystems #ChaosTheory #MachineLearning #PhysicsInformed #QuantymaResearch #Python #Lorenz63 #ResearchLab

Happy to share my latest research at Quantyma! Check out the full study on the resilience of recurrent networks in high-turbulence chaotic regimes below. 👇 #DeepLearning #ChaosTheory #PhysicsInformed #MachineLearning #DynamicalSystems #QuantymaResearch #ResearchLab