Hardware-constrained learning for quantum computing and artificial intelligence
Lesson
Uses quINR, QuCoWE, and QGSHAP to show how hybrid quantum components are often justified by representational density or combinatorial structure rather than generic speedup claims.
Video Lesson
Opens with the broad framing of quantum computing and AI as mutually enabling disciplines under hardware constraints.
Curated chapter summary for local development. Final production should use aligned transcript segments.
Emphasizes that the quantum role in practical models often sits at a compact, expressive bottleneck.
The video visually reinforces that representation density matters more than speculative end-to-end replacement.
Returns to hardware and systems limitations, including resource bottlenecks and the need for disciplined orchestration.
It ties model design back to what the hardware can sustain physically and operationally.
Surveys application stories in optimization and hybrid learning with an emphasis on workable interfaces between classical and quantum components.
The strongest message is that useful workflows are hybrid by construction.
Ends on a roadmap of sustainable hybrid systems, including the resource and thermodynamic framing of quantum agents.
The closing emphasizes future systems design, not merely isolated algorithmic novelty.
Key ideas
Source-grounded sections
Ali, Chicano, and Moraglio (Eds.), QC+AI 2025 Proceedings
Multimedia signal compression is increasingly relying on Implicit Neural Representations (INR)
Multimedia signal compression is increasingly relying on Implicit Neural Representations (INR). In this paradigm, a neural network is deliberately overfitted to a specific, single multimedia signal, effectively learning a direct coordinate-to-value mapping.1 The optimized weights and biases of this network subsequently act as the compressed format of the signal, replacing traditional pixel grids or voxel arrays.1 However, classical Multilayer Perceptrons (MLPs) struggle significantly to capture
Ali, Chicano, and Moraglio (Eds.), QC+AI 2026 Proceedings
Moving into the realm of Natural Language Processing (NLP), the paper "QuCoWE: Quantum Contrastive Word Embeddings with Variational Circuits for Near-Term Quantum Devices" by Rabimba Karanjai, Hemanth Hegadehalli Madhavarao, Lei Xu, and Weidong Shi explores the mapping of lexical distributional semantics directly into quantum state vectors.1 Classical embeddings, such as Word2Vec and GloVe, require hundreds of real-valued dimensions to encode complex semantic phenomena such as polysemy, synonymy
Moving into the realm of Natural Language Processing (NLP), the paper "QuCoWE: Quantum Contrastive Word Embeddings with Variational Circuits for Near-Term Quantum Devices" by Rabimba Karanjai, Hemanth Hegadehalli Madhavarao, Lei Xu, and Weidong Shi explores the mapping of lexical distributional semantics directly into quantum state vectors.1 Classical embeddings, such as Word2Vec and GloVe, require hundreds of real-valued dimensions to encode complex semantic phenomena such as polysemy, synonymy
Ali, Chicano, and Moraglio (Eds.), QC+AI 2026 Proceedings
As Graph Neural Networks (GNNs) permeate critical infrastructure such as drug discovery and social network analysis, the necessity for robust Explainable AI (XAI) grows
As Graph Neural Networks (GNNs) permeate critical infrastructure such as drug discovery and social network analysis, the necessity for robust Explainable AI (XAI) grows. However, theoretically rigorous attribution frameworks, such as Shapley values, suffer from #P-complete computational intractability. Because the Shapley formula requires the marginal evaluation of every node across all possible subset permutations, calculating exact explanations requires evaluating possible node coalitions.1 I
Notes
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Source assets
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