Practice is what makes progress.

A new PLoS One publication just out: Ugail H, Howard N (2026) CohAlign: Predicting the first-order noise susceptibility of gradients in equivariant quantum neural networks. PLoS One 21(10): e0361139. doi.org/10.1371/journal.pone…

From yesterday - at the Berlin Process side event, “From Regional Cooperation to Collective Action: Strengthening Integrity and Anti-Corruption in South-East Europe,” held in Tirana.

I look forward to speaking at this Berlin Process side event.

Quantum machines are likely to stay noisy for years. Knowing the noise in advance is how we learn to trust them. Ugail, H (2026), Predicting Noise in a Trapped-ion Quantum Simulation of a Lattice Gauge Theory, doi.org/10.48550/arXiv.2610.…

Your brainwaves are a fingerprint. When classifying conditions from brainwave data, people must be kept separate, because subject identity can really skew the results. Ugail, Wirt & Howard, Sensors 2026, doi.org/10.3390/s26196180

Simulating human consciousness - building consciousness into emerging AI and robotics is a new frontier. (Ugail & Howard, Biological Cybernetics (2026), doi.org/10.1007/s00422-026-0…, github.com/ugail/Index-for-C…)

The Last Equation In 1859, the famous German mathematician Bernhard Riemann published eight pages containing a hypothesis about the zeros of a single mathematical function, the zeta function, which quietly governs the distribution of the prime numbers. Every generation since has thrown its best minds and methods at it. Every generation has failed. Now a new kind of mind has entered the room. Machine learning systems are finding patterns in the zeta zeros, generating conjectures, and producing proofs no human can read from end to end. In our own work, we have been teaching neural networks to learn the structure of the zeta zeros. Which leaves me with a question I cannot put down. When a machine can establish truth which no human can understand, what remains of mathematics? And what remains of us?

The House of Algebra - How the Islamic World Shaped Modern Mathematics amazon.com/dp/B0HKG193F1

Our new paper published in PLoS One. We introduce PsiAudit — an open-source toolkit that checks whether quantum neural networks preserve the rules and patterns they were designed to follow before costly training begins. This is a practical step towards more transparent and reproducible quantum AI. Ugail H, Howard N (2026) PsiAudit: An open-source toolkit for auditing symmetry-organised complexity in equivariant quantum neural networks. PLoS One 21(7): e0353739. doi.org/10.1371/journal.pone…

Consciousness is one of those things we all experience, but still struggle to explain. In our new paper, we explore how the organisation of activity in the brain provides us with clues about conscious experience. We developed a mathematical model around three features of neural activity, and used them to create a new way of describing consciousness-related brain organisation. It is an early step, but we hope it offers a useful framework for researchers interested in the brain, consciousness and complex systems. We have also released a free, open-source simulator, so anyone interested can explore the model for themselves. The paper is published in Biological Cybernetics: "A three-component dynamical index of consciousness-related neural organisation", doi.org/10.1007/s00422-026-0… Simulator and code available at: github.com/ugail/Index-for-C… #Consciousness #Neuroscience #BrainScience

Quantum Neural Networks are exciting, but they are notoriously difficult to train. Training depends on gradients — signals that tell the model how to improve. In QNNs, these signals can vanish, leaving the model stuck on a “flat” learning landscape, often called a barren plateau. One cause is the "noise", i.e., disturbances from the environment, imperfect gates, measurement errors, or loss of quantum phase information. In our recent work, we focus on coherence, the quantum “harmony” that allows useful learning signals to survive. We discover that not all noise is equally harmful. A QNN may keep its symmetry, but still lose the specific coherence needed for training. So we ask, "is the noise damaging the quantum coherence that carries the learning signal?" This gives a clearer pathway to design QNNs that are more trainable on real quantum hardware. arxiv.org/abs/2606.30688

Our recent paper on quantum neural networks in IEEE Access: “A Channel-Level Diagnostic for Symmetry Breaking in Noisy Equivariant Quantum Neural Networks”, ieeexplore.ieee.org/document…

Geometric Phase-Space Structure in Cosmological Solutions of Einstein's Field Equations (arxiv.org/abs/2606.17071). Code available on GitHub: github.com/ugail/Phase-Space…

New publication: Training AI models can be expensive and resource-heavy, so it is important to know as early as possible whether a model is learning effectively, getting stuck, or wasting compute. In this paper, we propose a technique to better understand and guide the training of AI visual recognition models by looking beyond the usual measures such as accuracy and loss. Instead, we examine how the model’s internal learning behaviour changes during training, giving a clearer picture of whether it is developing useful visual understanding. Training dynamics and state taxonomy in deep visual recognition networks link.springer.com/article/10…

Our recent paper in PLOS ONE: “Verification of historical sketches via one-class learning on compact feature representations.” This work explores how compact, interpretable feature signals can support the verification of historical sketches and help identify artist-specific characteristics from the sketches they produce. journals.plos.org/plosone/ar…

Islamic Geometric Patterns through Symmetry-Structured Neural Completion, github.com/ugail/Neural-Comp…

Just published: Ugail, H., & Howard, N. (2026). Symmetry-Organised Complexity in Quantum Neural Networks. Symmetry, 18(6), 912. doi.org/10.3390/sym18060912

Just published: Ugail, H.; Howard, N.; Elmahmudi, A.A.; Mnasri, Z. Subject-Wise Depression Screening from Eight-Channel Resting-State EEG Using Asymmetry-Aware Spectral Features and Connectivity Ablation. Sensors 2026, 26, 3065. doi.org/10.3390/s26103065

The technology we have recently built to map someone's fingerprint from the tiny traces of sweat, lipids, salts, amino acids and other molecules they leave behind when they touch a surface. bbc.co.uk/news/articles/c4g4…

I have prepared some introductory material for anyone interested in learning more about the emerging area of quantum neural networks. A Technical Introduction to Quantum Neural Networks github.com/ugail/A-Technical… The material is intended as a starting point for students, researchers, and practitioners who want a technically oriented introduction to the concepts behind quantum neural networks.