A structure-aware transformer trained on 3-14 qubit systems predicts Trotter orderings for 16-20 qubit 1D Heisenberg Hamiltonians with a mean fidelity gap of 0.00115 to the best of 24 candidates.
Quantum compiling by deep reinforcement learning
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Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.
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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Structure-Aware Transformers for Learning Near-Optimal Trotter Orderings with System-Size Generalization in 1D Heisenberg Hamiltonians
A structure-aware transformer trained on 3-14 qubit systems predicts Trotter orderings for 16-20 qubit 1D Heisenberg Hamiltonians with a mean fidelity gap of 0.00115 to the best of 24 candidates.
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High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization
Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.
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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.