QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Quantum algorithms: A survey of applications and end-to-end complexities
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The paper gives a QLSS with query complexity (1+O(ε))κ ln(2√2/ε) using one kernel reflection when ||x|| is known, or O(κ log(1/ε)) overall, with explicit bound 56κ + 1.05κ ln(1/ε).
Purely dissipative Lindbladians without Hamiltonian part can approximate unitary dynamics to ε error in diamond norm with O(t²/ε) time, which is optimal for time-independent cases.
WPGS algorithm enforces inter-frame phase continuity in holographic tweezers to suppress refresh-induced atom loss and speed up updates for large neutral-atom arrays.
Adiabatic evolution prepares local thermal states from initial Gibbs states while conserving entropy density in the thermodynamic limit, with mirror-circuit benchmarking of hardware noise entropy demonstrated experimentally on a 5x4 Ising model.
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.
Comparative analysis of fault-tolerant interfaces for modular quantum computing using surface codes, including novel grow-and-distil protocols, to determine optimal strategies across hardware parameters for low logical error rates.
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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QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
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A shortcut to an optimal quantum linear system solver
The paper gives a QLSS with query complexity (1+O(ε))κ ln(2√2/ε) using one kernel reflection when ||x|| is known, or O(κ log(1/ε)) overall, with explicit bound 56κ + 1.05κ ln(1/ε).
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Hamiltonian dynamics from pure dissipation
Purely dissipative Lindbladians without Hamiltonian part can approximate unitary dynamics to ε error in diamond norm with O(t²/ε) time, which is optimal for time-independent cases.
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Phase-Stable Hologram Updates for Large-Scale Neutral-Atom Array Reconfiguration
WPGS algorithm enforces inter-frame phase continuity in holographic tweezers to suppress refresh-induced atom loss and speed up updates for large neutral-atom arrays.
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Adiabatic preparation of thermal states and entropy-noise relation on noisy quantum computers
Adiabatic evolution prepares local thermal states from initial Gibbs states while conserving entropy density in the thermodynamic limit, with mirror-circuit benchmarking of hardware noise entropy demonstrated experimentally on a 5x4 Ising model.
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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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Fault-tolerant interfaces for modular quantum computing on diverse qubit platforms
Comparative analysis of fault-tolerant interfaces for modular quantum computing using surface codes, including novel grow-and-distil protocols, to determine optimal strategies across hardware parameters for low logical error rates.
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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.