Quantum Machine Learning covers research at the intersection of quantum computing and artificial intelligence. Articles here explore quantum self-attention in vision transformers, photonic quantum-enhanced knowledge distillation, quantum-inspired uncertainty and evidence models, and quantum approaches to medical image diagnosis — with a focus on how quantum circuits reduce parameter counts and computational cost compared with classical deep learning.
Every time a bank issues a session key, a federated learning server masks a client update, or a privacy system adds noise to a query, it needs numbers nobody could have guessed. Most of the time those numbers come from…
Laser Shot Noise on a Camera Becomes Random Numbers Read More »
Most object detectors were raised on clean daylight photographs. Sharp edges, rich color, familiar shapes. Sonar throws almost all of that away. A forward looking sonar returns a single intensity channel that maps how sound bounces back, so a bottle…
Quantum-Gated LiteSSD Detects Sonar Objects Read More »
Entanglement is the resource that makes quantum technology different, the property behind secure communication, sensing beyond the classical limit, and quantum computing. When only two parties share a state, checking whether they are entangled is well understood. The problem gets…
Neural Networks Classify Quantum Entanglement Structures Read More »
Robot path planning has been reviewed before, more than once, and the authors say so themselves. What makes this paper different is scope rather than depth. It does not stop at classical search and sampling methods the way older surveys…
Robot Path Planning Evolution From Search To Quantum Read More »
A classical genetic algorithm is a loop with three moving parts. Crossover swaps segments between two candidate solutions the way chromosomes recombine during reproduction. Mutation flips a random element to introduce fresh variation. Fitness selection scores every candidate and keeps…
Quantum Genetic Algorithms From Circuits to Chemistry Read More »
Knowledge tracing sounds like a narrow academic exercise until you notice how often it quietly runs underneath the tools students already use. Every time an intelligent tutoring system decides which problem to show next, or a course dashboard flags a…
How Quantum Focal Elements Fix the Collapse Problem in Knowledge Tracing Read More »
A research team spanning Imperial College London, Brookhaven National Laboratory, NVIDIA, and ORCA Computing just showed that quantum light circuits can replace thousands of trainable neural network parameters — and the AI still works just as well. Here’s why that…
In the rapidly evolving field of biomedical image classification, deep learning models like Vision Transformers (ViTs) have set new performance benchmarks. However, their high computational cost and massive parameter counts—often in the millions—pose significant challenges for deployment in resource-constrained clinical…
A patient sits under a dermatoscope while a clinician tries to decide, in under a minute, whether an irregular brown patch on their shoulder is an ordinary mole or something that needs a biopsy. That judgment call happens millions of…
Quantum Layer Boosts CNN Melanoma Classification Accuracy Read More »