Maps qubit-oscillator quantum control problems to QSP to enable analytical design of operators that suppress cross-Kerr effects and selectively address Fock states.
& Economou, S
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Tensor-network decomposition converts entangled quantum wavepacket dynamics into independent lower-dimensional tasks executable asynchronously on distributed quantum hardware, demonstrated for vibrational spectra of a protonated water cluster agreeing with classical results to within 4 cm^{-1}.
Dynamical control schemes for dual-rail erasure qubits suppress transmon-induced noise, reducing erasure check errors by three orders of magnitude and logical two-qubit gate infidelities by up to three orders of magnitude.
Photonic QNNs with two trainable parameters solve nonlinear tasks like XOR at 100% accuracy where parameter-matched ANNs fail, with hardware deployment confirming the result.
citing papers explorer
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Analytic Approach to Quantum Control Using Quantum Signal Processing
Maps qubit-oscillator quantum control problems to QSP to enable analytical design of operators that suppress cross-Kerr effects and selectively address Fock states.
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Tensor-Network-Based Distributed Quantum Dynamics on Independent Quantum Computers
Tensor-network decomposition converts entangled quantum wavepacket dynamics into independent lower-dimensional tasks executable asynchronously on distributed quantum hardware, demonstrated for vibrational spectra of a protonated water cluster agreeing with classical results to within 4 cm^{-1}.
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Dynamical error reshaping for dual-rail erasure qubits
Dynamical control schemes for dual-rail erasure qubits suppress transmon-induced noise, reducing erasure check errors by three orders of magnitude and logical two-qubit gate infidelities by up to three orders of magnitude.
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Algorithmic Advantage on a Gate-Based Photonic Quantum Neural Network
Photonic QNNs with two trainable parameters solve nonlinear tasks like XOR at 100% accuracy where parameter-matched ANNs fail, with hardware deployment confirming the result.