DQMW-Sample realizes a classically hard online learning primitive via dissipative quantum dynamics with sublinear regret and proven hardness for classical simulation including PH collapse.
A review on quantum approximate optimization algorithm and its variants
8 Pith papers cite this work, alongside 346 external citations. Polarity classification is still indexing.
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Introduces Λ-lr-QAOA and piecewise-ramp QAOA that promote penalty schedules to variational parameters and use a feasibility-driven loss on budget-constrained MWIS satellite planning instances.
DQI-Kit automates encoding of objectives and constraints into Max-LINSAT instances and estimates expected DQI performance on the resulting problems.
Qudit encodings for EV trip assignments cut the Hilbert space dimension exponentially and match or exceed qubit-based QAOA performance on constrained uni- and bi-directional charging problems.
REGRID-QAOA applies coherency-informed graph reduction and structured post-processing to QAOA to match Gurobi-optimal islanding quality on 9- to 57-bus systems while using fewer quantum resources than vanilla QAOA.
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.
Pauli Correlation Encoding framework achieves competitive or superior solutions on QOPTLib benchmark instances for combinatorial optimization.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Dissipative Quantum Multiplicative Weights with Sampling Feedback: A Classically Hard Primitive Realized via Engineered Open-System Dynamics
DQMW-Sample realizes a classically hard online learning primitive via dissipative quantum dynamics with sublinear regret and proven hardness for classical simulation including PH collapse.
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Feasibility-driven QAOA with penalty scheduling
Introduces Λ-lr-QAOA and piecewise-ramp QAOA that promote penalty schedules to variational parameters and use a feasibility-driven loss on budget-constrained MWIS satellite planning instances.
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From Constraint to Code: DQI-Kit -- A Software Framework for Decoded Quantum Interferometry
DQI-Kit automates encoding of objectives and constraints into Max-LINSAT instances and estimates expected DQI performance on the resulting problems.
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Comparing Qubit and Qudit Encodings for EV Charging and Trip Assignment Problems
Qudit encodings for EV trip assignments cut the Hilbert space dimension exponentially and match or exceed qubit-based QAOA performance on constrained uni- and bi-directional charging problems.
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REGRID-QAOA: A Resource-Efficient Hybrid QAOA Framework for Physics-Constrained Power System Islanding
REGRID-QAOA applies coherency-informed graph reduction and structured post-processing to QAOA to match Gurobi-optimal islanding quality on 9- to 57-bus systems while using fewer quantum resources than vanilla QAOA.
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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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Benchmark of Pauli Correlation Encoding for different optimisation problems
Pauli Correlation Encoding framework achieves competitive or superior solutions on QOPTLib benchmark instances for combinatorial optimization.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.