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Finding Angles for Quantum Signal Processing with Machine Precision
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We describe an algorithm for finding angle sequences in quantum signal processing, with a novel component we call halving based on a new algebraic uniqueness theorem, and another we call capitalization. We present both theoretical and experimental results that demonstrate the performance of the new algorithm. In particular, these two algorithmic ideas allow us to find sequences of more than 3000 angles within 5 minutes for important applications such as Hamiltonian simulation, all in standard double precision arithmetic. This is native to almost all hardware.
Forward citations
Cited by 4 Pith papers
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Simulation of Non-Hermitian Hamiltonians with Bivariate Quantum Signal Processing
Claims query-optimal bivariate-QSP simulation of non-Hermitian Hamiltonians, but the constructive angle-finding chain is circular and contradicted by the paper's own benchmarks.
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A tunable parallel amplitude estimation algorithm achieves near-Heisenberg query scaling and logarithmic depth via GHZ states and quantum signal processing, with a near-optimality proof using the parallel quantum adve...
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A quantum algorithm for alchemical free energy calculations that block-encodes the Liouvillian to simulate molecular dynamics with polylogarithmic precision scaling, avoiding entropy estimation.
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Nuclear Many-Body Systems as Benchmarks for Quantum Computing
NuQuLib maps realistic nuclear Hamiltonians to qubit Hamiltonians and compares T-gate costs of QPE, QKrylov, and ODMD across valence and no-core model spaces.
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