PINNACLE is an open-source framework for classical and quantum PINNs that supplies modular training methods and benchmarks showing high sensitivity to architecture choices plus parameter-efficiency gains in some hybrid quantum regimes.
Quantum circuit learning
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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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PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs
PINNACLE is an open-source framework for classical and quantum PINNs that supplies modular training methods and benchmarks showing high sensitivity to architecture choices plus parameter-efficiency gains in some hybrid quantum regimes.
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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.