Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
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Logical error rates in [[7,1,3]] and [[12,2,4]] codes are suppressed 9.8-800 times below physical rates on trapped-ion hardware, with repeated correction cycles approaching the error rate of two physical CNOTs.
A new compiler for surface codes on QCCD trapped-ion hardware shows that 2-ion traps outperform larger traps in logical clock speed and hardware efficiency, beating prior compilers by 3.8X on average.
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
citing papers explorer
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Single-shot quantum neural networks with amplitude estimation
Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
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Demonstration of logical qubits and repeated error correction with better-than-physical error rates
Logical error rates in [[7,1,3]] and [[12,2,4]] codes are suppressed 9.8-800 times below physical rates on trapped-ion hardware, with repeated correction cycles approaching the error rate of two physical CNOTs.
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Architecting Scalable Trapped Ion Quantum Computers using Surface Codes
A new compiler for surface codes on QCCD trapped-ion hardware shows that 2-ion traps outperform larger traps in logical clock speed and hardware efficiency, beating prior compilers by 3.8X on average.
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Learning-Optimized Qubit Mapping and Reuse to Minimize Inter-Core Communication in Modular Quantum Architectures
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.