Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
Hardware-efficient varia- tional quantum eigensolver for small molecules and quantum magnets
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Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
Hybrid agent with variational quantum circuits for feature extraction in hierarchical RL outperforms classical baselines with 66% parameter savings, but quantum value estimation degrades results.
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
ZAPT2 frozen natural orbitals reduce virtual space for systematic convergence of open-shell T1-S0 gaps in CASCI and iQCC quantum eigensolvers, demonstrated on H2O2, O2, CH2 and Ir(ppy)3.
Quantum circuits for single and double fermionic excitations on ion traps reduce MS gate counts by factors of 2 and 4 respectively by using global interactions for optimal parallelism.
NISQ quantum simulation of spin-wave spectra in 2D chromium tri-halide magnets achieves agreement with classical benchmarks at quasi-constant wall-time scaling.
Review of proposals and experiments using coupled cavity arrays and superconducting circuits to realize many-body physics with photons, including Mott transitions, fractional quantum Hall states, and dissipative phase transitions.
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Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
Hybrid agent with variational quantum circuits for feature extraction in hierarchical RL outperforms classical baselines with 66% parameter savings, but quantum value estimation degrades results.