MerLin integrates Strong Linear Optical Simulation with PyTorch/scikit-learn for end-to-end differentiable photonic QML training. Reproduces 18 published works across kernels, reservoirs, CNNs & generative models, with live execution on Quandela QPUs.
#PhotonicQML #QuantumMachineLearning #Research
EQPs-AIME-Net, a residual neural network, reconstructs entanglement quasiprobabilities from sparse local measurements, achieving 30x error reduction vs. tomographic methods and validated on photonic Bell states with 99% fidelity.
#QuantumEntanglement #QuantumMachineLearning #Research
Researchers introduce layered-QAS, a progressive PQC design strategy using network morphism, achieving state-of-the-art QML results on ModelNet10/40 with fewer parameters while mitigating barren plateaus for 3D point cloud classification.
#QuantumMachineLearning #QuantumArchitectureSearch #Research
New metric log-DTSAE (depth/√avg relative entropy) predicts VQC noise robustness on NISQ devices without full hardware execution, across 1,100+ models on IBM, IQM & IonQ backends.
#QuantumMachineLearning #NISQ #Research
Mindbeam AI's SpinGQE uses a transformer-based decoder to auto-generate quantum circuits for spin Hamiltonians, cutting ground state energy error by 60% vs. VQE on a 4-qubit Heisenberg model—bypassing barren plateaus without prior system knowledge.
#QuantumAlgorithms #QuantumMachineLearning #News
Join global experts at #PQML2026 — a one-day workshop on the future of quantum machine learning at Tsinghua University and online. Supported by ROPP, QST and MLST.
Be part of the next wave of quantum discovery.
Register now: https://ow.ly/9vuX50YwqgR
#QuantumMachineLearning #QuantumComputing
Join global experts at #PQML2026 — a one-day workshop on the future of quantum machine learning at Tsinghua University and online. Supported by ROPP, QST and MLST.
Be part of the next wave of quantum discovery.
Register now: https://ow.ly/9vuX50YwqgR
#QuantumMachineLearning #QuantumComputing
Researchers explore how quantum structures & quantum computers can enhance AI, focusing on interpretable, scalable quantum NLP — applying quantum circuit formalisms to language understanding tasks.
#QuantumNLP #QuantumMachineLearning #News
Join global experts at #PQML2026 — a one-day workshop on the future of quantum machine learning at Tsinghua University and online. Supported by ROPP, QST and MLST.
Be part of the next wave of quantum discovery.
Register now: https://ow.ly/9vuX50YwqgR
#QuantumMachineLearning #QuantumComputing
U. Helsinki researchers propose a metric combining relative entropy & transpilation depth to predict VQC performance on noisy NISQ hardware before execution, validated across IBM, Rigetti & IonQ devices.
#QuantumMachineLearning #NISQ #News
Tsinghua University researchers compressed large QNNs into smaller architectures using knowledge distillation, reducing qubit count and circuit depth while preserving accuracy—enabling deployment on near-term NISQ hardware.
#QuantumMachineLearning #QNN #News
A new statistical framework separates observable concentration from parameter sensitivity in PQCs, identifying mid-circuit information loss and local scrambling as independent gradient-suppression mechanisms — validated across circuits up to 60 qubits.
#QuantumMachineLearning #BarrenPlateaus #News
Preprint alert!
We show that representations based on one-electron integrals, eg the kinetic energy matrix, can effectively predict materials properties.
Preprint chemrxiv.org/doi/full/10....
Code github.com/grynova-ccc/...
#AIforMaterials #QuantumMachineLearning #ChemicalAI
Researchers at Forschungszentrum Jülich introduce a metric-guided framework using a dimension-free barren plateau diagnostic to identify PQCs balancing trainability and expressibility, achieving UCCSD-level VQE accuracy with 6× fewer parameters.
#VariationalQuantum #QuantumMachineLearning #Research
A time-conditioned Swin Transformer achieves 0.6% error rate in CV quantum error mitigation, recovering states beyond training data horizons — including non-Markovian noise — without exhaustive calibration data.
#QuantumErrorMitigation #QuantumMachineLearning #News
A novel QCNN using localized cost functions & tensor-network initialization provably avoids barren plateaus, achieving 98.7% MNIST accuracy with only 45 parameters vs ~120,000 for classical CNNs—an O(log N) parameter efficiency advantage.
#QuantumMachineLearning #QCNN #Research
New Publication: Quantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets 🚀
🔗https://pubmed.ncbi.nlm.nih.gov/41728802/
#ArtificialIntelligence #BiomedicalInformatics #MedSky #AISky #MedAI #QML #AIML #QuantumMachineLearning
@teknikeroficial.bsky.social presenta un innovador sistema para acelerar la #fabricación de una nueva generación de sensores #cuánticos
➡️ fedit.com/2026/03/tekn... 🔬
#CentrosTecnológicos #TransferenciaTecnológica #GeneraciónDeConocimiento #InnovaciónConImpacto #QuantumMachineLearning
The Quantum Dragon isn't the newsletter you need right now, but it's the newsletter you deserve.
bsiegelwax.substack.com/p/merry-chri...
#quantumcomputing #qubits #quantumcarousel #quantummachinelearning #quantumnoise @insidequantumtech.bsky.social
Quantum Computing's Potential: A Business Guide #businessapplicationsofquantumcomputing #quantumadvantage #quantumalgorithmsforbusiness #quantumasaservice #preparingforquantum #NISQera #quantummachinelearning #qubittechnology #quantumcryptography #quantumtechnologyinvestment
Open up AI's black box with Quantum Computing. This article explains how quantum kernels and QNLP enhance machine learning explainability and traceability. #quantummachinelearning
Promotional flyer for the ScaDS.AI Colloquium showing headshots of Edoardo Altamura and Choy Boy; event title "Colloquium Session"; date and time Thursday, 16 October 2025, 10:30 AM CEST; location TUD Dresden, Strehlener Straße 12/14, room 745; ScaDS.AI Dresden/Leipzig and TUD Dresden logos.
📅New #colloquium on October 16, 2025!
Organized by @dobrautz.bsky.social, Edoardo Altamura will discuss #QuantumMachineLearning for chemistry uses.
Choy Boy will present on energy landscapes for variational quantum algorithms.
Join us @tudresden.bsky.social & online:
🔗https://tinyurl.com/43hhur49
The German saying goes "wer schön sein will muss leiden". My German is not so good, so I understood "wer schön sein will muss *nach* Leiden". Off to spend a couple months @unileiden.bsky.social, to start new projects on #QuantumMachineLearning
"Ever wondered how structured circuits can halve your quantum model training costs? Discover their impact on gradient estimation and how they make quantum computing more accessible! Have you tried them? Share your experiences! #QuantumComputing #QuantumMachineLearning #Innovation" LINK
New Study Reduces Quantum Model Parameters via Frequency Selection
Frequency‑selection cuts trainable parameters to 78 % of the best conventional method while retaining a median R² of ~0.95 across ten target functions. Read more: getnews.me/new-study-reduces-quantu... #quantummachinelearning #frequencyselection
Hybrid Quantum-Classical Model Boosts Anomaly Detection in ADS‑B Data
A hybrid quantum‑classical neural network (H‑FQNN) achieved 90.17%–94.05% accuracy on ADS‑B anomaly detection, matching a traditional FNN’s 91.50%–93.37%. Read more: getnews.me/hybrid-quantum-classical... #quantummachinelearning #adsb
Quantum Machine Learning Models Aim to Improve Weather Forecasts
The study tested five quantum machine‑learning models on ERA5 climate data; results were comparable to classical baselines, but the paper has been withdrawn. Read more: getnews.me/quantum-machine-learning... #quantummachinelearning #weatherforecasting
"Exciting research reveals a paradox in quantum machine learning: entanglement boosts expressivity but may hinder optimization. What could this mean for future quantum algorithms? 🤔 #QuantumMachineLearning #QuantumComputing #AIResearch" LINK
So excited to be speaking at the 6th #QuantumComputing Opportunities in #Energy Innovation #Workshop
🕑10:00-11:30am MDT at #IEEEQuantumWeek!
I will be discussing two projects I lead in this space on:
#QuantumMachineLearning for smart electrical grids 🔋🔌
and
#Nuclear modeling ⚛️
See you there! 🤹♀️💃
"Did you know that data encoding can dramatically boost quantum machine learning model performance? Discover how different methods impact classification accuracy! Share your thoughts on data encoding's role in AI. #QuantumMachineLearning #AIResearch #QuantumComputing" LINK
