qaoa, qaoa, quantum ai
Variational algorithm for optimization.
102 technical terms and definitions
Variational algorithm for optimization.
QA on scientific papers.
Vector similarity search engine.
Qdrant is vector database in Rust. High performance.
Whether to use bias in attention projections.
Combines quantization with LoRA for memory-efficient fine-tuning on consumer GPUs.
QMIX is a value-based multi-agent RL method that learns centralized but factorized Q-functions to enable decentralized policy execution.
QPLEX factorizes joint action-values through dueling network architecture enabling scalable value decomposition in multi-agent settings.
Quantile Regression DQN approximates return distribution through fixed quantiles improving robustness in distributional reinforcement learning.
Information-seeking conversations.
Question Answering in Context evaluates information-seeking conversations.
No leads extending from body.
Four-sided package with leads.
Differences between wafer quadrants.
Quadrant patterns divide wafers into sections suggesting equipment or process asymmetry.
Quadratic loss functions penalize deviations proportional to squared error.
Special lot run to validate new process or equipment.
Process test wafers after PM to verify tool performance.
Qualification status indicates equipment readiness for production use.
Qualification testing validates new products meet reliability requirements before production release.
Wafers used to qualify process or equipment.
Testing and validation that equipment or process meets specifications.
Quality at source requires each worker to ensure perfect output.
Quality at source ensures suppliers deliver defect-free materials eliminating incoming inspection.
Build quality into process.
Sample with known properties for QC.
Classification of quality costs.
Translate customer needs to design.
Cost of deviation from target.
Framework for managing quality.
Quality rate is ratio of good units to total units produced.
Fraction of good output.
Assign quality scores to data.
Adjust quality vs energy.
Long document comprehension.
Quantization stores weights in fewer bits (e.g. 8-bit, 4-bit) to save memory and speed up inference, with a small accuracy tradeoff if done well.
Lowest reliably measured amount.
Optimize specific quantiles.
Learn quantiles of returns.
Predict conditional quantiles instead of mean.
Relate structure to biological activity.
Reduce precision for edge inference.
Training with quantization in mind so the model learns to work well at lower precision.
Quantization-aware training simulates quantization during training improving low-precision accuracy.
Reducing model weight precision (32-bit → 8-bit/4-bit) to save memory and speed up inference.
Cases where quantum helps ML.
When quantum computer outperforms classical.
Estimate amplitudes in quantum states.
Quantum approach to combinatorial problems.
Quantum version of Boltzmann machines.