For n-qubit stabilizer states the optimal sample complexity of approximate cloning is Θ(n), matching the complexity of learning.
Plenio, Steven T
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Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
A parametrized variational quantum tomography framework unifies existing VQT variants and improves fidelity to MaxEnt reconstructions.
A stochastic framework for entropy production in coherent scattering theory is introduced that separates information and thermodynamic contributions and links them to transport fluctuations.
New combinatorial proofs and circuit designs for quantum error correction reduce physical qubit overhead by up to 10x and time overhead by 2-6x for codes including Steane, Golay, and surface codes.
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Cloning is as Hard as Learning for Stabilizer States
For n-qubit stabilizer states the optimal sample complexity of approximate cloning is Θ(n), matching the complexity of learning.
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Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
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Parametrized Variational Quantum Tomography
A parametrized variational quantum tomography framework unifies existing VQT variants and improves fidelity to MaxEnt reconstructions.
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Stochastic entropy production in scattering theory
A stochastic framework for entropy production in coherent scattering theory is introduced that separates information and thermodynamic contributions and links them to transport fluctuations.
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Lower overhead fault-tolerant building blocks for noisy quantum computers
New combinatorial proofs and circuit designs for quantum error correction reduce physical qubit overhead by up to 10x and time overhead by 2-6x for codes including Steane, Golay, and surface codes.