QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
A variational eigenvalue solver on a photonic quantum processor
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For diagonal Hamiltonians like MaxCut, hardware-efficient ansatze drive entanglement down during training and are outperformed by separable circuits in a monotonic relationship, while QAOA's problem-derived entanglement remains competitive.
A graph neural network trained on H4 and H6 predicts optimized orbitals for larger unseen H8-H12 systems with O(10-100) milli-Hartree energy errors and provides effective warm-starts for VQE optimization.
RFOX maintains a flat spectral gap via non-stoquastic XX catalyst plus analytic counter-diabatic ZX driving, yielding near-optimal solutions on random-field Ising models with up to 10x fewer Trotter steps.
Diagonal ANOs are mathematically equivalent to full ANOs modulo unitary similarity, reducing k-local observable complexity from O(4^k) to O(2^k) and lowering measurement-side classical computation while including conventional VQCs as a special case.
D-QEO framework uses quantum topographical preconditioning on separable functions via small parallel subcircuits to generate seeds that accelerate classical global optimization and avoid exponential failure rates.
TUSQ reduces redundant work in noisy quantum simulations via error tallying, commutation, importance sampling, and depth-first tree traversal with compute/uncompute reuse, reporting large speedups over Qiskit, CUDA-Q, and TQSim on 198 benchmarks.
Iterative refinement boosts LLM success in generating quantum solvers that match classical results, but more advanced models shift from execution errors to hard-to-detect numerical inaccuracies.
citing papers explorer
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QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling
QUACOD decomposes drone scheduling into quantum-solvable subproblems via coordinate descent, outperforming prior quantum methods in completion time while scaling to 5x more drones and 35x more routes.
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Detrimental Agnostic Entanglement: The Case Against Hardware-Efficient Ans\"atze for Combinatorial Optimization
For diagonal Hamiltonians like MaxCut, hardware-efficient ansatze drive entanglement down during training and are outperformed by separable circuits in a monotonic relationship, while QAOA's problem-derived entanglement remains competitive.
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A Transferable Machine Learning Approach to Predict Optimized Orbitals for Electronic Structure Problems
A graph neural network trained on H4 and H6 predicts optimized orbitals for larger unseen H8-H12 systems with O(10-100) milli-Hartree energy errors and provides effective warm-starts for VQE optimization.
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RFOX (Rotated-Field Oscillatory eXchange) quantum algorithm: Towards Parameter-Free Quantum Optimizers
RFOX maintains a flat spectral gap via non-stoquastic XX catalyst plus analytic counter-diabatic ZX driving, yielding near-optimal solutions on random-field Ising models with up to 10x fewer Trotter steps.
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Diagonal Adaptive Non-local Observables on Quantum Neural Networks
Diagonal ANOs are mathematically equivalent to full ANOs modulo unitary similarity, reducing k-local observable complexity from O(4^k) to O(2^k) and lowering measurement-side classical computation while including conventional VQCs as a special case.
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Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search
D-QEO framework uses quantum topographical preconditioning on separable functions via small parallel subcircuits to generate seeds that accelerate classical global optimization and avoid exponential failure rates.
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Noisy Quantum Simulation Using Tracking, Uncomputation and Sampling
TUSQ reduces redundant work in noisy quantum simulations via error tallying, commutation, importance sampling, and depth-first tree traversal with compute/uncompute reuse, reporting large speedups over Qiskit, CUDA-Q, and TQSim on 198 benchmarks.
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Can LLMs Solve Science or Just Write Code? Evaluating Quantum Solver Generation
Iterative refinement boosts LLM success in generating quantum solvers that match classical results, but more advanced models shift from execution errors to hard-to-detect numerical inaccuracies.