MetaMorphQ defines five physics-derived invariants for VQE circuits that enable oracle-free testing with zero false positives and Youden's J of 0.57 on 500 benchmarks versus 0.02 for convergence testing.
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
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2026 3verdicts
UNVERDICTED 3representative citing papers
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
Quantum-inspired 1024-D document embeddings exhibit weak, unstable ranking performance and structural geometric limitations, performing better as auxiliary components in hybrid lexical-embedding retrieval systems.
citing papers explorer
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MetaMorphQ: Physics-Based Metamorphic Testing of Variational Quantum Circuits
MetaMorphQ defines five physics-derived invariants for VQE circuits that enable oracle-free testing with zero false positives and Youden's J of 0.57 on 500 benchmarks versus 0.02 for convergence testing.
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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
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On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework
Quantum-inspired 1024-D document embeddings exhibit weak, unstable ranking performance and structural geometric limitations, performing better as auxiliary components in hybrid lexical-embedding retrieval systems.