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).
High-order quantum algorithm for solving linear differential equations
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Linear differential equations are ubiquitous in science and engineering. Quantum computers can simulate quantum systems, which are described by a restricted type of linear differential equations. Here we extend quantum simulation algorithms to general inhomogeneous sparse linear differential equations, which describe many classical physical systems. We examine the use of high-order methods to improve the efficiency. These provide scaling close to $\Delta t^2$ in the evolution time $\Delta t$. As with other algorithms of this type, the solution is encoded in amplitudes of the quantum state, and it is possible to extract global features of the solution.
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quant-ph 3representative citing papers
Demonstration of quantum circuit implementation for 2D obstacle flow via Carleman-linearized LBM solved with QSVT, achieving logarithmic qubit and gate scaling with lattice points.
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.
citing papers explorer
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A Demonstration of Quantum Circuit Implementation for Obstacle Flow Using Carleman-Linearized Lattice Boltzmann Method
Demonstration of quantum circuit implementation for 2D obstacle flow via Carleman-linearized LBM solved with QSVT, achieving logarithmic qubit and gate scaling with lattice points.
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Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.