GQKAE uses quantum-inspired Kolmogorov-Arnold networks to reduce parameters by 66% in generative quantum eigensolvers while achieving chemical accuracy on H4, N2, LiH, and other molecules.
Quantum architecture search via deep reinforcement learning,
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A Monte Carlo Tree Search with GNN-based magic estimation biases quantum circuit search toward target nonstabilizerness levels and yields better results on ground-state energy and state approximation problems.
Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compilation tasks.
Progressive widening MCTS with sampling action space automates quantum circuit design, cutting evaluations 10-100x and CNOT gates up to 3x versus prior MCTS on chemistry and linear-equation tasks.
Hamiltonian-reconstruction distance is shown to correlate with ground-state fidelity and serves as a practical success metric for VQE on 1D and 2D Ising models in simulation and on trapped-ion hardware.
MZeQAS accelerates quantum architecture search for VQAs by replacing full training of candidates with a zero-shot performance estimate derived from QNTK Gram-matrix convergence.
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
A survey of core concepts, representative methodologies, applications, challenges, and future directions in Quantum Architecture Search for variational quantum algorithms.
citing papers explorer
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Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
GQKAE uses quantum-inspired Kolmogorov-Arnold networks to reduce parameters by 66% in generative quantum eigensolvers while achieving chemical accuracy on H4, N2, LiH, and other molecules.
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Magic-Informed Quantum Architecture Search
A Monte Carlo Tree Search with GNN-based magic estimation biases quantum circuit search toward target nonstabilizerness levels and yields better results on ground-state energy and state approximation problems.
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Replay-buffer engineering for noise-robust quantum circuit optimization
Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compilation tasks.
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Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
Progressive widening MCTS with sampling action space automates quantum circuit design, cutting evaluations 10-100x and CNOT gates up to 3x versus prior MCTS on chemistry and linear-equation tasks.
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Hamiltonian-reconstruction distance as a success metric for the Variational Quantum Eigensolver
Hamiltonian-reconstruction distance is shown to correlate with ground-state fidelity and serves as a practical success metric for VQE on 1D and 2D Ising models in simulation and on trapped-ion hardware.
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Zero-shot Quantum Neural Architecture Search
MZeQAS accelerates quantum architecture search for VQAs by replacing full training of candidates with a zero-shot performance estimate derived from QNTK Gram-matrix convergence.
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Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
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Hybrid Quantum-Classical Neural Architecture Search
Demonstrates FLOPs-aware neural architecture search for hybrid quantum-classical neural networks to produce accurate yet computationally efficient models suitable for NISQ hardware.
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A Review of Variational Quantum Algorithms: Insights into Fault-Tolerant Quantum Computing
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
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Recent Advances in Quantum Architecture Search
A survey of core concepts, representative methodologies, applications, challenges, and future directions in Quantum Architecture Search for variational quantum algorithms.