Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
The computational power of random quantum circuits in arbitrary geometries (2024)
5 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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Position graph abstraction plus SHAPER/SHAW heuristics enable shuttling-aware compilation on trapped-ion machines, succeeding on extreme cases where baselines fail and yielding 1.45x average (up to 4x) speedups.
Nonlinear cross-entropy benchmark and heavy-output classifier enable sample-efficient distinction between noisy quantum and classical spoofers for shallow-depth all-to-all random circuits.
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
Variational quantum circuit MPS ansatz with stochastic corrections simulates the DQPT of the TFIM on Quantinuum H1-1 hardware, demonstrating feasibility and revealing hidden simplicity in the dynamics.
citing papers explorer
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Iceberg Beyond the Tip: Co-Compilation of a Quantum Error Detection Code and a Quantum Algorithm
Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
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Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction
Position graph abstraction plus SHAPER/SHAW heuristics enable shuttling-aware compilation on trapped-ion machines, succeeding on extreme cases where baselines fail and yielding 1.45x average (up to 4x) speedups.
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Sample-efficient benchmarking of shallow all-to-all random quantum circuits
Nonlinear cross-entropy benchmark and heavy-output classifier enable sample-efficient distinction between noisy quantum and classical spoofers for shallow-depth all-to-all random circuits.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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Fully optimised variational simulation of a dynamical quantum phase transition on a trapped-ion quantum computer
Variational quantum circuit MPS ansatz with stochastic corrections simulates the DQPT of the TFIM on Quantinuum H1-1 hardware, demonstrating feasibility and revealing hidden simplicity in the dynamics.