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.
Pa- rameter setting heuristics make the quantum approxi- mate optimization algorithm suitable for the early fault- tolerant era
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qReduMIS hybrid pipeline improves QAOA performance on real financial MIS instances up to 225 assets, achieving higher success probabilities and better scaling on Quantinuum trapped-ion hardware.
Iterative orthogonal-basis interpolation constructs high-quality QAOA parameter schedules for depths exceeding 1000 layers, outperforming prior methods on SK, portfolio, and LABS benchmarks.
Empirical evidence indicates QAOA entanglement scales like fermionic Gaussian states for MaxCut instances, unlike the annealing-schedule-dependent scaling in adiabatic quantum computation.
Systematic numerical study of QAOA parameter transfer on heavy-hex Ising models with local cubic terms shows transferred angles from small instances yield improving expectation values up to 49 layers on instances up to 156 qubits, with hardware runs confirming gains up to p=10.
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.
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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Quantum-Informed Portfolio Selection: An End-to-End Pipeline Validated on Trapped-Ion Hardware with Real Market Data
qReduMIS hybrid pipeline improves QAOA performance on real financial MIS instances up to 225 assets, achieving higher success probabilities and better scaling on Quantinuum trapped-ion hardware.
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Iterative Interpolation Schedules for Quantum Approximate Optimization Algorithm
Iterative orthogonal-basis interpolation constructs high-quality QAOA parameter schedules for depths exceeding 1000 layers, outperforming prior methods on SK, portfolio, and LABS benchmarks.
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Entanglement Scaling and Problem Structure in Quantum Approximate and Adiabatic Optimization Algorithms
Empirical evidence indicates QAOA entanglement scales like fermionic Gaussian states for MaxCut instances, unlike the annealing-schedule-dependent scaling in adiabatic quantum computation.
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Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms
Systematic numerical study of QAOA parameter transfer on heavy-hex Ising models with local cubic terms shows transferred angles from small instances yield improving expectation values up to 49 layers on instances up to 156 qubits, with hardware runs confirming gains up to p=10.
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The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
A synthesis of expert insights from the ADAC Quantum Computing Working Group and member survey on the complementary roles of quantum and classical high-performance computing in future hybrid infrastructures.