#QuantumLearning
LightNews LightNews
informaq.bsky.social
Breakthrough quantum learning algorithm: efficiently learns low-bond-dimension quantum states from data without assuming the unknown state belongs to the model class, using compression and dynamic programming with polynomial complexity.

#QuantumLearning #TensorNetworks #Research
Proper Agnostic Learning of Matrix Product States and Tree Tensor Networks
arxiv.org
September 25, 2026 at 4:01 AM
informaq.bsky.social
New quantum score matching framework extends classical learning technique to quantum states, achieving optimal sample complexity for high-temperature Gibbs states. NISQ-friendly implementation on IBM hardware reduces parameter error from 64% to 10%.

#QuantumLearning #QuantumAlgorithms #Research
Quantum Score Matching Framework for Learning Thermal Gibbs States
arxiv.org
September 24, 2026 at 7:06 AM
qbitphased.com
The University of Alabama in Huntsville's new framework uses quantum noise to enhance data privacy and model security, keeping raw data secure for safer collaborations. How do you think this will impact data privacy's future? 🤔 #QuantumLearning #DataPrivacy #Cybersecurity LINK
August 30, 2025 at 10:20 AM
informaq.bsky.social
Researchers identified an intermediate 'learning phase' in quantum circuits where spectral nonflatness and metrological response maximize information processing power before systems descend into quantum chaos.

#QuantumCircuits #QuantumLearning #News
Learning Phase in Quantum Circuits: Spectral Nonflatness and Computational Power Before Chaos
quantumzeitgeist.com
September 4, 2026 at 4:16 PM
informaq.bsky.social
KAIST researchers reveal that bound entanglement alone cannot deliver exponential speedups in quantum learning tasks. The unrestricted nature of entanglement, not just its presence, is essential for achieving quantum computational advantage.

#QuantumLearning #Entanglement #News
Bound Entanglement Insufficient for Exponential Quantum Learning Advantage
quantumzeitgeist.com
August 8, 2026 at 6:17 PM
informaq.bsky.social
Resolved: the sample complexity of learning Gaussian quantum states. Both bosonic and fermionic m-mode systems require O(m²) copies, proving non-Gaussian operations fundamentally outperform Gaussian-only measurement strategies for tomography.

#QuantumTomography #QuantumLearning #Research
Optimal Tomography of Bosonic and Fermionic Gaussian States
arxiv.org
July 14, 2026 at 6:34 AM
informaq.bsky.social
Shows VC-dimension alone fails to characterize quantum PAC-learning sample complexity. New bounds incorporate quantum Chernoff distance; classical-like results emerge when domain states are linearly independent.

#QuantumLearning #PAC-Learning #QuantumAlgorithms
Quantum Sample Complexity for PAC-Learning Functions Over Quantum States
arxiv.org
July 9, 2026 at 2:18 AM
informaq.bsky.social
Unified framework linking learning-induced spectral reorganization to experimentally measurable quantum interference and Bloch-space geometry, validated on quantum hardware for anomaly detection in network traffic.

#QuantumMachineLearning #QuantumLearning #Research
Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learning
arxiv.org
July 2, 2026 at 10:36 AM
getnews-me.bsky.social
Framework tailors VQCNN depth with PSO and aggregates models via knowledge distillation that sends only soft‑label outputs, reducing communication and preserving privacy. Read more: https://getnews.me/quantum-distillation-improves-variational-cnns-for-mixed-data/ #quantumlearning #distillation
Quantum Distillation Improves Variational CNNs for Mixed Data
September 25, 2025 at 8:52 AM
ananox.bsky.social
Closer to the New Year. 6G is just one of the many beyond ventures of wonder. Upon a Teraflop 'a unit of measurement that measures a computers computations to perform one trillion floating -point preps per second'.

We are here at the Mainframe of it all.

#Quantum #Quantumlearning #Mainframe #MSP
December 29, 2024 at 7:40 AM
ananox.bsky.social
Fun fact -Binary; A computial sets of output and input. Has a numeric system. But not numerical inclined or arranged...

#Binary #Q #Quantumcomputing #Quantumlearning #QL
February 14, 2024 at 11:39 AM
scheffknecht.bsky.social
Want to stay ahead in the Quantum AI era? 🚀 Here’s how: Learn from experts, stay updated and dive into cutting-edge research. #QuantumLearning #TechInsights #ThoughtLeadership

News: IBM and Terra Quantum are using hybrid quantum systems in finance, healthcare & energy.

www.wsj.com/articles/the...
January 4, 2025 at 4:28 PM
informaq.bsky.social
New algorithm learns n-qubit Lindbladian coefficients with O(gd²log(n)/ε²) evolution time using simple Pauli measurements, extending prior Hamiltonian learning techniques to dissipative open quantum systems.

#QuantumLearning #OpenQuantumSystems #Research
Efficient Algorithm for Structure Learning of Local Lindbladians
arxiv.org
June 30, 2026 at 3:36 PM
informaq.bsky.social
Proves optimal sample complexity O(ηᵏ/ε²) for learning k-body fermionic correlations scales with particle number η rather than system size N, establishing provable advantage of particle-number conservation in quantum state tomography.

#QuantumSimulation #QuantumLearning #Research
Efficient Fermionic Correlation Learning with Particle-Number Symmetry
arxiv.org
June 30, 2026 at 1:41 PM
informaq.bsky.social
Researchers achieved optimal O(dε⁻¹) query complexity for learning quantum evolutions using classical shadow estimation, reducing resource demands and establishing new benchmarks for quantum system characterization.

#QuantumLearning #QuantumTomography #News
Query-Optimal Quantum Process Tomography via Classical Shadow Estimation
quantumzeitgeist.com
June 22, 2026 at 3:38 PM
informaq.bsky.social
Researchers achieved query-optimal protocol for classical shadow estimation of unitary channels, reaching O(dε⁻¹) complexity at Heisenberg scaling. Key breakthrough: optimal unitary tomography can now be parallelized without sacrificing efficiency.

#QuantumLearning #QuantumTomography #Research
Optimal Classical Shadow Estimation of Unitary Channels at Heisenberg Limit
arxiv.org
June 14, 2026 at 1:25 AM
informaq.bsky.social
Establishes fundamental limits on quantum machine learning: finite samples can only uniformly support circuit ansätze with expressibility G ≤ Mε² gates, bridging expressibility with statistical learnability.

#QuantumML #QuantumLearning #Research
Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
arxiv.org
June 13, 2026 at 12:07 PM
informaq.bsky.social
Noise fundamentally limits quantum learning speedups in practical devices. While exponential advantages vanish under depolarizing noise, researchers identify persistent polynomial speedups when interfacing noise-robust physics with quantum algorithms.

#QuantumLearning #QuantumNoise #News
Noisy Quantum Learning Theory
www.nature.com
May 30, 2026 at 12:06 AM
informaq.bsky.social
Learning algorithms can erase unknown quantum states at optimal energy cost while learning itself carries no fundamental energy cost. This bridges quantum learning theory and thermodynamics, enabling efficient work extraction and new protocol design.

#QuantumThermodynamics #QuantumLearning #News
Learning to Erase Quantum States: Thermodynamic Implications of Quantum Learning Theory
www.nature.com
May 27, 2026 at 3:11 PM
informaq.bsky.social
Introducing QTERM: a robust quantum learning framework that handles noisy experimental data and outliers using tunable loss functions, enabling practical quantum machine learning in real-world conditions.

#QuantumLearning #QuantumML #News
Quantum Learning with Tunable Loss Functions
iq.fp2.dev
May 19, 2026 at 3:42 PM
informaq.bsky.social
Extends quantum state learning with minimal cumulative disturbance from qubits to arbitrary dimensions, overcoming geometric obstacles through tangent-space linearization and achieving O(d³log²T) regret bounds.

#QuantumTomography #QuantumLearning #Research
Adaptive Pure Quantum State Learning in Higher Dimensions With Minimal Cumulative Disturbance
arxiv.org
May 12, 2026 at 7:13 AM
informaq.bsky.social
Uploading quantum states into surface codes concentrates noise into a one-time cost, enabling exponential speedups in learning algorithms like shadow tomography and moment estimation—even when uploading incurs significant additional noise.

#QuantumErrorCorrection #QuantumLearning #Research
Fault-Tolerant Quantum Uploading Enables Exponential Learning Speedups from Noisy Quantum Experiments
iq.fp2.dev
May 5, 2026 at 9:06 AM
informaq.bsky.social
Curated 2026 collection organized by skill level and use case, ranging from Bernhardt's math-free introduction to Nielsen-Chuang's graduate reference, with emphasis on pedagogical value over promotional hype.

#QuantumComputing #QuantumLearning #News
The 2026 Guide to Essential Quantum Computing Books
quantumzeitgeist.com
May 4, 2026 at 6:02 PM
informaq.bsky.social
Proves that cloning n-qubit stabilizer states requires Θ(n) samples, matching learning complexity. First no-cloning theorem for structured states, connecting quantum learning theory to foundations via sample amplification lower bounds.

#QuantumLearning #StabilizerStates #Research
Cloning is as Hard as Learning for Stabilizer States
iq.fp2.dev
April 19, 2026 at 1:40 AM
informaq.bsky.social
Chen et al. (IQIM) establish tight bounds: n³/ε² samples for Gaussian measurements, n²/ε² for all types. Non-Gaussian measurements are provably required for optimal passive-state learning; adaptive schemes enable near energy-independent scaling.

#QuantumLearning #QuantumSensing #News
Sample-Optimal Learning of Bosonic Gaussian Quantum States
iq.fp2.dev
March 21, 2026 at 10:09 AM