GQKAE uses quantum-inspired Kolmogorov-Arnold networks to reduce parameters by 66% in generative quantum eigensolvers while achieving chemical accuracy on H4, N2, LiH, and other molecules.
The generative quantum eigensolver (GQE) and its application for ground state search
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Generative optimization of quantum embedding circuits improves supervised classification on some datasets, with derived bounds showing performance saturation governed by Wasserstein distance of the classical input data.
SCALAR generates conjectures linking optimal QAOA parameters to graph invariants, recovers known periodicity and parameter-transfer properties, and identifies correlations with optimization landscapes across thousands of graphs up to 77 qubits.
A Transformer policy optimizes quantum circuit ansatzes for QSCI, yielding up to 98% reduction in two-qubit gates while reaching chemical accuracy on N2 and competitive compactness with classical methods.
Multimodal diffusion model generates discrete gate selections and continuous parameters for quantum circuit compilation, claiming better gate counts and noise resilience than prior methods.
A layered framework with physical gatekeepers, fidelity analysis against reference VQE circuits, and a consistency metric identifies five LLM failure modes in quantum circuit generation and reveals that some apparent model errors originated in the evaluation harness itself.
An MCP server framework lets LLM agents run quantum primitives like sampling and expectation value computation on hybrid platforms by interpreting prompts and invoking tools for OpenQASM and CUDA-Q.
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
The authors extend generative quantum eigensolver to produce circuits with upper-bounded quantum circuit-cutting overhead for molecular ground-state search, tested via transformer decoder on BeH2 with a new loss function and hybrid training strategy.
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.
citing papers explorer
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Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
GQKAE uses quantum-inspired Kolmogorov-Arnold networks to reduce parameters by 66% in generative quantum eigensolvers while achieving chemical accuracy on H4, N2, LiH, and other molecules.
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Generative Quantum Data Embeddings for Supervised Learning
Generative optimization of quantum embedding circuits improves supervised classification on some datasets, with derived bounds showing performance saturation governed by Wasserstein distance of the classical input data.
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SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis
SCALAR generates conjectures linking optimal QAOA parameters to graph invariants, recovers known periodicity and parameter-transfer properties, and identifies correlations with optimization landscapes across thousands of graphs up to 77 qubits.
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Generative Circuit Design for Quantum-Selected Configuration Interaction
A Transformer policy optimizes quantum circuit ansatzes for QSCI, yielding up to 98% reduction in two-qubit gates while reaching chemical accuracy on N2 and competitive compactness with classical methods.
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Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Multimodal diffusion model generates discrete gate selections and continuous parameters for quantum circuit compilation, claiming better gate counts and noise resilience than prior methods.
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Gatekeepers and Hallucinations: A Layered Evaluation Framework for LLM-Driven Quantum Circuit Generation
A layered framework with physical gatekeepers, fidelity analysis against reference VQE circuits, and a consistency metric identifies five LLM failure modes in quantum circuit generation and reveals that some apparent model errors originated in the evaluation harness itself.
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A Model Context Protocol Server for Quantum Execution in Hybrid Quantum-HPC Environments
An MCP server framework lets LLM agents run quantum primitives like sampling and expectation value computation on hybrid platforms by interpreting prompts and invoking tools for OpenQASM and CUDA-Q.
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Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
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Generative quantum eigensolver with constrained circuit-cutting overhead
The authors extend generative quantum eigensolver to produce circuits with upper-bounded quantum circuit-cutting overhead for molecular ground-state search, tested via transformer decoder on BeH2 with a new loss function and hybrid training strategy.
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The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.