Hamming Weight Operators and an adaptive QAOA variant confine evolution to feasible states by construction, delivering faster convergence and roughly half the gate count versus penalty methods on finance and physics tasks.
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Quantum supremacy through the quantum approx- imate optimization algorithm
12 Pith papers cite this work. Polarity classification is still indexing.
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TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
A nonlinear custom penalty without slack variables plus CVaR sampling improves optimality gaps and consistency on knapsack instances for quantum constrained optimization.
Presents a quantum Hamiltonian whose ground state encodes equivalence classes of expressions, enabling verification, counting, and structural queries on instances far beyond classical reach.
QAOA with default parameters is compared per-shot to Goemans-Williamson on realistic Max-Cut instances, highlighting practical limitations under black-box use.
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
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.
IQPopt is a JAX-based software tool enabling classical optimization of IQP circuits with thousands of qubits via efficient simulation of Pauli-Z expectation values, plus a module for quantum generative model training.
QAOA achieves approximation ratios of 0.90-0.95 on N=5-20 Euclidean graphs, outperforming classical baselines by 2.7-4.4% with 2-3x faster runtimes and picojoule-scale energy use, projecting 8.2% real-world routing efficiency gains and 2.62 EJ annual US fuel savings.
Classical simulation of quantum annealing for the 1D Hubbard model up to 40 qubits reports substantial speed-up over Bethe-ansatz methods for half-filled cases.
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.
citing papers explorer
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Constraint-Aware Quantum Optimization via Hamming Weight Operators
Hamming Weight Operators and an adaptive QAOA variant confine evolution to feasible states by construction, delivering faster convergence and roughly half the gate count versus penalty methods on finance and physics tasks.
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TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency
TuniQ uses RL with a dual-encoder, shaped rewards, and action masking to autotune quantum compilation passes, improving fidelity and speed over Qiskit while generalizing across backends and scaling to large circuits.
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CVaR-Assisted Custom Penalty Function for Constrained Optimization
A nonlinear custom penalty without slack variables plus CVaR sampling improves optimality gaps and consistency on knapsack instances for quantum constrained optimization.
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Quantum algorithms for equational reasoning
Presents a quantum Hamiltonian whose ground state encodes equivalence classes of expressions, enabling verification, counting, and structural queries on instances far beyond classical reach.
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Per-Shot Evaluation of QAOA on Max-Cut: A Black-Box Implementation Comparison with Goemans-Williamson
QAOA with default parameters is compared per-shot to Goemans-Williamson on realistic Max-Cut instances, highlighting practical limitations under black-box use.
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Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
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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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IQPopt: Fast optimization of instantaneous quantum polynomial circuits in JAX
IQPopt is a JAX-based software tool enabling classical optimization of IQP circuits with thousands of qubits via efficient simulation of Pauli-Z expectation values, plus a module for quantum generative model training.
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Potential Energy Savings from Quantum Computing-Based Route Optimization
QAOA achieves approximation ratios of 0.90-0.95 on N=5-20 Euclidean graphs, outperforming classical baselines by 2.7-4.4% with 2-3x faster runtimes and picojoule-scale energy use, projecting 8.2% real-world routing efficiency gains and 2.62 EJ annual US fuel savings.
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Quantum speed-up for solving the one-dimensional Hubbard model using quantum annealing
Classical simulation of quantum annealing for the 1D Hubbard model up to 40 qubits reports substantial speed-up over Bethe-ansatz methods for half-filled cases.
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Mind the gaps: The fraught road to quantum advantage
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.
- A sharp interaction-degree threshold for simulating QAOA