An ansatz-free Lindbladian learning algorithm via Bell sampling with a SPAM-robust extension for gauge-independent parts of sparse Lindbladians under constant noise.
Improved hamiltonian learning and sparsity testing through bell sampling
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We consider the problem of learning an $M$-sparse Hamiltonian and the related problem of Hamiltonian sparsity testing. Through a detailed analysis of Bell sampling, we reduce the total evolution time required by the state-of-the-art algorithm for $M$-sparse Hamiltonian learning to $\widetilde{\mathcal{O}}(M/\epsilon)$, where $\epsilon$ denotes the $\ell^{\infty}$ error, achieving an improvement by a factor of $M$ (ignoring the logarithmic factor) while only requiring access to forward time-evolution. We then establish a connection between Hamiltonian learning and Hamiltonian sparsity testing through Bell sampling, which enables us to propose a Hamiltonian sparsity testing with state-of-the-art total evolution time scaling.
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quant-ph 6representative citing papers
Establishes n^{Ω(k)} lower bounds for learning k-local Hamiltonians from time evolution, including single-coefficient and effective Hamiltonian learning, via a new connection to Boolean function analysis.
Local Lindbladians can be learned with Õ(Λ²/ε²) channel uses and Õ(Λ/ε²) total time; matching lower bounds prove this optimal even for adaptive, entangling strategies.
Ansatz-free Hamiltonian learning with product Pauli states and no control achieves optimal total evolution time Θ(Λ/ε² log(Λ/ε)), with a matching new lower bound over all control-free protocols.
A provable exponential quantum-classical learning separation is established for predicting expectation values of time-evolved quantum states under unknown low-intersection Hamiltonians, assuming BQP ⊄ P/poly.
A randomized algorithm detects dissipation of magnitude at least epsilon in unknown Lindbladian dynamics with optimal total evolution time O(epsilon^{-1}) under bounded strength and locality assumptions.
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Efficient and SPAM-Robust Ansatz-Free Lindbladian Learning
An ansatz-free Lindbladian learning algorithm via Bell sampling with a SPAM-robust extension for gauge-independent parts of sparse Lindbladians under constant noise.
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Lower Bounds for Learning Hamiltonians from Time Evolution
Establishes n^{Ω(k)} lower bounds for learning k-local Hamiltonians from time evolution, including single-coefficient and effective Hamiltonian learning, via a new connection to Boolean function analysis.
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Near-Optimal Learning of Local Lindbladians
Local Lindbladians can be learned with Õ(Λ²/ε²) channel uses and Õ(Λ/ε²) total time; matching lower bounds prove this optimal even for adaptive, entangling strategies.
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Optimal Ansatz-free Hamiltonian Learning In Situ
Ansatz-free Hamiltonian learning with product Pauli states and no control achieves optimal total evolution time Θ(Λ/ε² log(Λ/ε)), with a matching new lower bound over all control-free protocols.
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Provable learning separation for predicting time-evolution of quantum many-body systems
A provable exponential quantum-classical learning separation is established for predicting expectation values of time-evolved quantum states under unknown low-intersection Hamiltonians, assuming BQP ⊄ P/poly.
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Optimal detection of dissipation in Lindbladian dynamics
A randomized algorithm detects dissipation of magnitude at least epsilon in unknown Lindbladian dynamics with optimal total evolution time O(epsilon^{-1}) under bounded strength and locality assumptions.