Using optimal control to guide neural-network interpolation of continuously-parameterized gates | Paper Citation Record

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This paper cites Open Quantum Assembly Language.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Open Quantum Assembly Language

Reference 1

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This paper cites A Practical Quantum Instruction Set Architecture.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates A Practical Quantum Instruction Set Architecture

Reference 2

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This paper cites A software methodology for compiling quantum programs,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates A software methodology for compiling quantum programs,

Reference 3

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This paper cites Quantum computing in the NISQ era and beyond,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Quantum computing in the NISQ era and beyond,

Reference 4

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This paper cites Optimized compilation of aggregated instructions for realistic quantum computers,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Optimized compilation of aggregated instructions for realistic quantum computers,

Reference 5

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This paper cites Variational quantum algorithms,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Variational quantum algorithms,

Reference 6

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This paper cites Implementation of XY entangling gates with a single calibrated pulse,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Implementation of XY entangling gates with a single calibrated pulse,

Reference 7

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This paper cites Demonstrating a continuous set of two-qubit gates for near-term quantum algorithms,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Demonstrating a continuous set of two-qubit gates for near-term quantum algorithms,

Reference 8

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Observation fa2ce943-4808-461b-931e-120abb1b45a4 · outbound

This paper cites Improving the performance of deep quantum optimization algorithms with continuous gate sets,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Improving the performance of deep quantum optimization algorithms with continuous gate sets,

Reference 9

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Observation 04ab7b51-29b9-473c-86b6-cf29162cd8ff · outbound

This paper cites Simulating non-native cubic interactions on noisy quantum machines,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Simulating non-native cubic interactions on noisy quantum machines,

Reference 10

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This paper cites An adaptive variational algorithm for exact molecular simulations on a quantum computer,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates An adaptive variational algorithm for exact molecular simulations on a quantum computer,

Reference 11

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This paper cites Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.691575Z

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Observation 3ad0ecf6-9c66-4451-a95e-9cf2dfaa1079 · outbound

This paper cites Symmetry breaking slows convergence of the ADAPT Variational Quantum Eigensolver,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Symmetry breaking slows convergence of the ADAPT Variational Quantum Eigensolver,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.677237Z

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Observation 46974bb6-2912-4b1b-8f1f-bca89008f718 · outbound

This paper cites Efficient quantum circuits for quantum computational chemistry,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Efficient quantum circuits for quantum computational chemistry,

Reference 14

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Observation d5bb53f1-039a-44b0-89be-9cb65b9a8040 · outbound

This paper cites Optimal control of families of quantum gates,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Optimal control of families of quantum gates,

Reference 15

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Observation e955780f-328f-4387-be4f-b0e6e93540f5 · outbound

This paper cites Continuous quantum gate sets and pulse-class meta-optimization,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Continuous quantum gate sets and pulse-class meta-optimization,

Reference 16

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Observation acb1a04c-fdf7-407f-aa6b-a62b5374e980 · outbound

This paper cites Efficient control pulses for continuous quantum gate families through coordinated re-optimization,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Efficient control pulses for continuous quantum gate families through coordinated re-optimization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.612032Z

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Observation 7d4b7b7e-3bed-4278-91af-1d15a081576b · outbound

This paper cites Bilinear dynamic mode decomposition for quantum control,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Bilinear dynamic mode decomposition for quantum control,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.596125Z

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This paper cites Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe,

Reference 19

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Observation a47c5159-369a-4cd6-a335-d015e08e9552 · outbound

This paper cites Active learning with statistical models,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Active learning with statistical models,

Reference 20

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This paper cites Direct collocation for quantum optimal control,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Direct collocation for quantum optimal control,

Reference 21

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This paper cites Pad´e Integrator Direct Collocation (Piccolo.jl),.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Pad´e Integrator Direct Collocation (Piccolo.jl),

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.533775Z

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This paper cites Goodfellow, Y.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Goodfellow, Y

Reference 23

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This paper cites Neural network approximation of piecewise continuous functions: application to friction compensation,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Neural network approximation of piecewise continuous functions: application to friction compensation,

Reference 24

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Observation 2bd16664-114b-47bf-9e7a-21c4c9a1573e · outbound

This paper cites Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation,

Reference 25

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Observation 8c00e060-ac6c-4d92-8c5c-bd6529e82a58 · outbound

This paper cites Arbitrary quantum control of qubits in the presence of universal noise,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Arbitrary quantum control of qubits in the presence of universal noise,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.320213Z

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This paper cites Optimal control of coupled spin dynamics: design of nmr pulse sequences by gradient ascent algorithms,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Optimal control of coupled spin dynamics: design of nmr pulse sequences by gradient ascent algorithms,

Reference 27

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This paper cites One decade of quantum optimal control in the chopped random basis,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates One decade of quantum optimal control in the chopped random basis,

Reference 28

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This paper cites Tunable, flexible, and efficient optimization of control pulses for practical qubits,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Tunable, flexible, and efficient optimization of control pulses for practical qubits,

Reference 29

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Observation c1c64fd5-5220-41b0-af36-8014c6b6eb9d · outbound

This paper cites Krotov: A Python implementation of krotov’s method for quantum optimal control,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Krotov: A Python implementation of krotov’s method for quantum optimal control,

Reference 30

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Observation 63717509-648b-40b8-b2fd-ddc8e3d2e99f · outbound

This paper cites Optimal Control of Closed Quantum Systems via B-Splines with Carrier Waves.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Optimal Control of Closed Quantum Systems via B-Splines with Carrier Waves

Reference 31

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Observation be37d495-b17e-4bba-b6de-b08c534b6957 · outbound

This paper cites Software tools for quantum control: Improving quantum computer performance through noise and error suppression,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Software tools for quantum control: Improving quantum computer performance through noise and error suppression,

Reference 32

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Observation b4d623a2-b04e-4641-b7a9-24391c1fba07 · outbound

This paper cites Robust quantum optimal control with trajectory optimization,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Robust quantum optimal control with trajectory optimization,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.189658Z

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Observation 7ea5a850-2d3a-439f-b507-8d351b5f83e9 · outbound

This paper cites Sampling-based learning control for quantum systems with uncertainties,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Sampling-based learning control for quantum systems with uncertainties,

Reference 34

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Observation bee597fe-418e-439d-88c4-7d2a7af8fd19 · outbound

This paper cites Control optimization for parametric hamiltonians by pulse reconstruction,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Control optimization for parametric hamiltonians by pulse reconstruction,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.145704Z

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Observation 4f753718-51e6-4ea2-b380-d35d46f9a7f9 · outbound

This paper cites Exploring adiabatic quantum trajectories via optimal control,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Exploring adiabatic quantum trajectories via optimal control,

Reference 36

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Observation db202b04-2339-49e4-9e05-7d91e844dd93 · outbound

This paper cites Second order gradient ascent pulse engineering,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Second order gradient ascent pulse engineering,

Reference 37

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This paper cites A sequential algorithm for training text classifiers: Corrigendum and additional data,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates A sequential algorithm for training text classifiers: Corrigendum and additional data,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.092944Z

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Observation 3b1bdd68-3499-460e-9f92-799a67d67e3d · outbound

This paper cites Characterizing errors on qubit operations via iterative randomized benchmarking,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Characterizing errors on qubit operations via iterative randomized benchmarking,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.078175Z

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This paper cites Adaptive hybrid optimal quantum control for imprecisely characterized systems,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Adaptive hybrid optimal quantum control for imprecisely characterized systems,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.061700Z

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Observation f1f4fe1f-9bfa-420e-a814-9e77d1b5fd77 · outbound

This paper cites Optimal quantum control using randomized benchmarking,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Optimal quantum control using randomized benchmarking,

Reference 41

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This paper cites Quantum supremacy using a programmable superconducting processor,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Quantum supremacy using a programmable superconducting processor,

Reference 42

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Observation f44f1278-67be-424e-a937-e6200ffdc22c · outbound

This paper cites The Snake Optimizer for Learning Quantum Processor Control Parameters.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates The Snake Optimizer for Learning Quantum Processor Control Parameters

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T19:34:52.772646Z

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Observation ca2acf39-f2b5-46cf-bd1b-28324e013ebe · outbound

This paper cites Experimental deep rein- forcement learning for error-robust gate-set design on a superconducting quantum computer,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Experimental deep rein- forcement learning for error-robust gate-set design on a superconducting quantum computer,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:53.010599Z

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Observation dfb24dd5-d04f-4085-b0e0-57cd3607ad92 · outbound

This paper cites A quantum engineer’s guide to superconducting qubits,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates A quantum engineer’s guide to superconducting qubits,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:52.982134Z

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Observation fccd47a5-884e-4fb4-9faa-6774efd300be · outbound

This paper cites Qubit-excitation-based adaptive variational quantum eigen- solver,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Qubit-excitation-based adaptive variational quantum eigen- solver,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:52.965353Z

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Observation 5cccdec4-b434-462d-aee1-b0af8056d362 · outbound

This paper cites Metrology of quantum control and measurement in super- conducting qubits,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates Metrology of quantum control and measurement in super- conducting qubits,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:52.947272Z

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Observation af163f76-5112-4fae-b8d8-6e96e334e262 · outbound

This paper cites IBM Quantum documentation: System information,.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates IBM Quantum documentation: System information,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:52.931095Z

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Observation f4d77c52-6e36-4246-a789-f8c1c53d1984 · outbound

This paper cites d’Alessandro, Introduction to quantum control and dynamics.

Using optimal control to guide neural-network interpolation of continuously-parameterized gates d’Alessandro, Introduction to quantum control and dynamics

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:34:52.917442Z

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