A reduction from weak agnostic learning of class C to efficient tomography of states with bounded l1-extent w.r.t. C, with a concrete algorithm for stabilizer states running in poly(n, (ξ/ε)^log(ξ/ε)) time.
Childs and Nathan Wiebe
7 Pith papers cite this work, alongside 394 external citations. Polarity classification is still indexing.
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quant-ph 7years
2026 7roles
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Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
A new compilation framework treats quantum channels as first-class objects via ChannelIR and LindFront, achieving up to 99% gate count reduction on Lindbladian benchmarks versus unoptimized and Stinespring baselines.
Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
A symplectic framework links quantum evolution to classical Hamiltonian dynamics on Kähler manifolds, yielding exponentially compressed quantum representations for integrable systems and approximate versions for others via perturbation theory.
Hybrid quantum interior point methods for linear programming have no practical runtime advantage over classical solvers like HiGHS on realistic instances because their quantum lower bounds already exceed classical performance under optimistic assumptions.
citing papers explorer
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Tomography of quantum states with bounded extent
A reduction from weak agnostic learning of class C to efficient tomography of states with bounded l1-extent w.r.t. C, with a concrete algorithm for stabilizer states running in poly(n, (ξ/ε)^log(ξ/ε)) time.
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Quantum principal component analysis without eigenvector recovery
Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
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A Compilation Framework for Quantum Simulation of Non-unitary Dynamics
A new compilation framework treats quantum channels as first-class objects via ChannelIR and LindFront, achieving up to 99% gate count reduction on Lindbladian benchmarks versus unoptimized and Stinespring baselines.
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Quantum Solvers for Nonlinear Matrix Equations in Quantum Chemistry
Quantum algorithm block-encodes Riccati solutions for m-particle m-hole RPA using Riesz projectors and QSVT, claiming linear system-size scaling under sparsity and polynomial cost in excitation rank m.
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Quantum Randomized Subspace Iteration
QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
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Symplectic perspective to quantum computing for Hamiltonian systems
A symplectic framework links quantum evolution to classical Hamiltonian dynamics on Kähler manifolds, yielding exponentially compressed quantum representations for integrable systems and approximate versions for others via perturbation theory.
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Practical lower bounds for hybrid quantum interior point methods in linear programming
Hybrid quantum interior point methods for linear programming have no practical runtime advantage over classical solvers like HiGHS on realistic instances because their quantum lower bounds already exceed classical performance under optimistic assumptions.