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| { | ||
| "friendly_name": "Hidden Shift", | ||
| "description": "Hidden-Shift problem for bent functions", | ||
| "qmod_type": ["algorithms"], | ||
| "level": ["demos"] | ||
| } |
7 changes: 7 additions & 0 deletions
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algorithms/amplitude_estimation/quantum_counting/quantum_counting.metadata.json
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| { | ||
| "friendly_name": "Quantum Counting QPE", | ||
| "description": "Counting Solutions to equation using Amplitude Estimation with Quantum Phase Estimation", | ||
| "problem_domain_tags": [], | ||
| "qmod_type": ["algorithms"], | ||
| "level": ["demos"] | ||
| } |
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| { | ||
| "friendly_name": "Deutsch-Jozsa", | ||
| "description": "Deutsch-Jozsa Algorithm", | ||
| "qmod_type": ["algorithms"], | ||
| "problem_domain_tags": [], | ||
| "level": ["basic", "demos"] | ||
| } |
8 changes: 8 additions & 0 deletions
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algorithms/gibbs/quantum_thermal_state_preparation.metadata.json
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| { | ||
| "friendly_name": "Quantum Thermal State Preparation", | ||
| "description": "Quantum Thermal State Preparation Algorithm Implementation", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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| { | ||
| "friendly_name": "Glued Trees Algorithm", | ||
| "description": "Glued Trees Algorithm", | ||
| "qmod_type": ["algorithms"], | ||
| "problem_domain_tags": [], | ||
| "level": ["advanced"] | ||
| } |
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| { | ||
| "friendly_name": "Grover: Max Cut", | ||
| "description": "Graph Cut search problem with Grover Oracle", | ||
| "problem_domain_tags": ["search", "optimization"], | ||
| "qmod_type": ["algorithms"], | ||
| "level": ["demos"] | ||
| } |
7 changes: 7 additions & 0 deletions
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...tonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.metadata.json
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| { | ||
| "friendly_name": "Hamiltonian simulation guide: Exponentiation with depth constraint", | ||
| "description": "A simple implementation of exponentiation with depth constraint", | ||
| "qmod_type": ["algorithms"], | ||
| "problem_domain_tags": [], | ||
| "level": ["basic", "demos"] | ||
| } |
7 changes: 7 additions & 0 deletions
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...n_simulation_with_block_encoding/hamiltonian_simulation_with_block_encoding.metadata.json
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| { | ||
| "friendly_name": "Hamiltonian Simulation with Qubitization", | ||
| "description": "Hamiltonian Simulation with Qubitization", | ||
| "qmod_type": ["algorithms"], | ||
| "problem_domain_tags": [], | ||
| "level": ["advanced", "demos"] | ||
| } |
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| { | ||
| "friendly_name": "HHL (with exact Hamiltonian simulation)", | ||
| "description": "Solving linear system of equations using HHL algorithm, with exact Hamiltonian simulation", | ||
| "problem_domain_tags": ["ml", "linear equation"], | ||
| "qmod_type": ["algorithms"], | ||
| "level": ["advanced", "demos"] | ||
| } | ||
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| { | ||
| "friendly_name": "Qgan Bars And Strips", | ||
| "description": "Quantum Generative Adversarial Networks (QGANs)", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": ["ml"], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
7 changes: 7 additions & 0 deletions
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algorithms/qml/quantum_autoencoder/quantum_autoencoder.metadata.json
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| { | ||
| "friendly_name": "Quantum Auto-Encoder", | ||
| "description": "Quantum Auto-Encoder: encoder network", | ||
| "problem_domain_tags": ["ml"], | ||
| "qmod_type": ["algorithms"], | ||
| "level": ["demos"] | ||
| } |
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| { | ||
| "friendly_name": "Quantum Phase Estimation (Exact)", | ||
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| "description": "Quantum Phase Estimation for Evaluating Matrix Eigenvalues, using exact exponentiation", | ||
| "problem_domain_tags": ["chemistry"], | ||
| "qmod_type": ["algorithms"], | ||
| "level": ["demos"] | ||
| } | ||
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| { | ||
| "friendly_name": "Simon's Algorithm Shallow Oracle", | ||
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| "description": "Solving Simon's problem for a shallow oracle with secret string of the form 000. 01..111", | ||
| "qmod_type": ["algorithms"], | ||
| "level": ["demos"] | ||
| } | ||
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applications/benchmarking/quantum_volume/quantum_volume.metadata.json
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| { | ||
| "friendly_name": "Quantum Volume", | ||
| "description": "Quantum Volume", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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applications/benchmarking/randomized_benchmarking/randomized_benchmarking.metadata.json
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| { | ||
| "friendly_name": "Randomized Benchmarking", | ||
| "description": "Randomized Benchmarking", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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applications/cfd/qls_for_hybrid_solvers/chebyshev_approximation.metadata.json
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| { | ||
| "friendly_name": "Inversion with Chebyshev LCU (Banded Diagonals Block Encoding)", | ||
| "description": "Solving Linear Equation with Chebyshev LCU, for Banded Diagonals Block Encoding", | ||
| "problem_domain_tags": ["cfd"], | ||
| "qmod_type": ["application"], | ||
| "level": ["demos"] | ||
| } |
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applications/cfd/qls_for_hybrid_solvers/qls_chebyshev_lcu.metadata.json
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| { | ||
| "friendly_name": "Inversion with Chebyshev LCU (Banded Diagonals Block Encoding)", | ||
| "description": "Solving Linear Equation with Chebyshev LCU, for Banded Diagonals Block Encoding", | ||
| "problem_domain_tags": ["cfd"], | ||
| "qmod_type": ["application"], | ||
| "level": ["demos"] | ||
| } |
7 changes: 7 additions & 0 deletions
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applications/cfd/qls_for_hybrid_solvers/qls_qsvt.metadata.json
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| { | ||
| "friendly_name": "Inversion with Chebyshev LCU (Banded Diagonals Block Encoding)", | ||
| "description": "Solving Linear Equation with Chebyshev LCU, for Banded Diagonals Block Encoding", | ||
| "problem_domain_tags": ["cfd"], | ||
| "qmod_type": ["application"], | ||
| "level": ["demos"] | ||
| } |
7 changes: 7 additions & 0 deletions
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applications/cfd/qls_for_hybrid_solvers/verify_block_encoding.metadata.json
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| { | ||
| "friendly_name": "Inversion with Chebyshev LCU (Banded Diagonals Block Encoding)", | ||
| "description": "Solving Linear Equation with Chebyshev LCU, for Banded Diagonals Block Encoding", | ||
| "problem_domain_tags": ["cfd"], | ||
| "qmod_type": ["application"], | ||
| "level": ["demos"] | ||
| } |
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...tions/chemistry/classiq_chemistry_application/classiq_chemistry_application.metadata.json
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| { | ||
| "friendly_name": "Classiq Chemistry Application", | ||
| "description": "Classiq Chemistry Application", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": ["chemistry"], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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applications/chemistry/molecule_eigensolver/molecule_eigensolver.metadata.json
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| { | ||
| "friendly_name": "VQE: Molecule Ground State (HWEA)", | ||
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| "description": "Molecule Eigensolver (VQE method): Evaluating the Ground State of a Molecular Hamiltonian with Hardware Efficient Ansatz", | ||
| "problem_domain_tags": ["chemistry"], | ||
| "qmod_type": ["application"], | ||
| "level": ["demos"] | ||
| } | ||
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...ications/finance/autocallable_options/partial_exponential_state_preparation.metadata.json
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| { | ||
| "friendly_name": "Quantum Autocallable Option Pricing", | ||
| "description": "Autocallables with Integration Amplitude Loading", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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applications/finance/autocallable_options/quantum_autocallable_option_pricing.metadata.json
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| { | ||
| "friendly_name": "Quantum Autocallable Option Pricing", | ||
| "description": "Autocallables with Integration Amplitude Loading", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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applications/logistics/vehicle_routing_problem/vehicle_routing_problem.metadata.json
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| { | ||
| "friendly_name": "Vehicle Routing Problem", | ||
| "description": "Vehicle Routing Problem (VRP)", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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| { | ||
| "friendly_name": "Min Graph Coloring", | ||
| "description": "Solving Min Graph Coloring Problem Using the QAOA Algorithm", | ||
| "problem_domain_tags": ["optimization"], | ||
| "qmod_type": ["application"], | ||
| "level": ["demos"] | ||
| } |
8 changes: 8 additions & 0 deletions
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applications/optimization/kidney_exchange/kidney_exchange_problem.metadata.json
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| { | ||
| "friendly_name": "Kidney Exchange Problem", | ||
| "description": "Kidney Exchange QAOA Example", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
8 changes: 8 additions & 0 deletions
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applications/optimization/rectangles_packing/rectangles_packing_grid.metadata.json
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| { | ||
| "friendly_name": "Rectangles Packing Problem", | ||
| "description": "Solving the Rectangles Packing Problem using the QAOA Algorithm", | ||
| "vertical_tags": ["retail"], | ||
| "problem_domain_tags": ["optimization"], | ||
| "qmod_type": ["application"], | ||
| "level": ["demos"] | ||
| } |
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applications/plasma/vlasov_ampere/vlasov_ampere.metadata.json
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| { | ||
| "friendly_name": "Vlasov Ampere", | ||
| "description": "Quantum Simulation of Linear Kinetic Plasma Models", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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applications/plasma/vlasov_ampere/vlasov_ampere_qiskit.metadata.json
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,8 @@ | ||
| { | ||
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| "friendly_name": "Vlasov Ampere", | ||
| "description": "Quantum Simulation of Linear Kinetic Plasma Models", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } | ||
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tutorials/advanced_tutorials/discrete_quantum_walk/discrete_quantum_walk.metadata.json
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| { | ||
| "friendly_name": "Discrete Quantum Walk on a Circle", | ||
| "description": "Discrete Quantum Walk on a Circle with an Unbalanced Coin", | ||
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| "qmod_type": ["algorithms"], | ||
| "problem_domain_tags": [], | ||
| "level": ["basic", "demos"] | ||
| } | ||
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...tutorials/high_level_modeling_flexible_qpe/high_level_modeling_flexible_qpe.metadata.json
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| { | ||
| "friendly_name": "High Level Modeling Flexible Qpe", | ||
| "description": "Designing Quantum Algorithms with Second Order Functions: A Flexible QPE", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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tutorials/basic_tutorials/entanglement/entanglement.metadata.json
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| { | ||
| "friendly_name": "Entanglement", | ||
| "description": "Quantum Entanglement with Classiq", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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| { | ||
| "friendly_name": "MCX Function", | ||
| "description": "MCX Function: 50 Control Qubits", | ||
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| "problem_domain_tags": [], | ||
| "qmod_type": ["function"], | ||
| "level": ["demos"] | ||
| } | ||
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tutorials/basic_tutorials/optimization/learning_optimization.metadata.json
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| { | ||
| "friendly_name": "Learning Optimization", | ||
| "description": "Learning Optimization", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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tutorials/basic_tutorials/qml_with_classiq_guide/qml_with_classiq_guide.metadata.json
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| { | ||
| "friendly_name": "QNN with PyTorch", | ||
| "description": "A simple QNN example using the PyTorch integration", | ||
| "qmod_type": ["algorithms"], | ||
| "problem_domain_tags": [], | ||
| "level": ["basic", "demos"] | ||
| } |
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tutorials/basic_tutorials/the_classiq_tutorial/Qmod_tutorial_part1.metadata.json
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| { | ||
| "friendly_name": "Qmod Tutorial Part1", | ||
| "description": "Qmod Tutorial - Part 1", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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tutorials/basic_tutorials/the_classiq_tutorial/Qmod_tutorial_part2.metadata.json
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| { | ||
| "friendly_name": "Qmod Tutorial Part2", | ||
| "description": "Qmod Tutorial - Part 2", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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tutorials/basic_tutorials/the_classiq_tutorial/classiq_overview_tutorial.metadata.json
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| { | ||
| "friendly_name": "Qmod Tutorial Overview", | ||
| "description": "Qmod Tutorial - Overview", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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tutorials/basic_tutorials/the_classiq_tutorial/execution_tutorial.metadata.json
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| { | ||
| "friendly_name": "Qmod Tutorial Execution", | ||
| "description": "Qmod Tutorial - Execution", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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tutorials/basic_tutorials/the_classiq_tutorial/execution_tutorial_part2.metadata.json
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| { | ||
| "friendly_name": "Qmod Tutorial Part2", | ||
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| "description": "Qmod Tutorial - Part 2", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } | ||
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tutorials/basic_tutorials/the_classiq_tutorial/synthesis_tutorial.metadata.json
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| { | ||
| "friendly_name": "Qmod Tutorial Synthesis", | ||
| "description": "Qmod Tutorial - Synthesis", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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...emonstrations/approximated_state_preparation/approximated_state_preparation.metadata.json
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| { | ||
| "friendly_name": "Approximated State Preparation", | ||
| "description": "Approximated State Preparation", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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...als/technology_demonstrations/arithmetic_expressions/arithmetic_expressions.metadata.json
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| { | ||
| "friendly_name": "Arithmetic Model", | ||
| "description": "Complex Arithmetic Expression", | ||
| "problem_domain_tags": [], | ||
| "qmod_type": ["function"], | ||
| "level": ["basic"] | ||
| } |
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tutorials/technology_demonstrations/auxiliary_managment/auxiliary_management.metadata.json
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| { | ||
| "friendly_name": "Auxiliary Management", | ||
| "description": "Auxiliary Reuse and Management", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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tutorials/technology_demonstrations/classiq_paper/qsvt/classiq_qsvt.metadata.json
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| { | ||
| "friendly_name": "Classiq Qsvt", | ||
| "description": "Classiq code for QSVT example", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } |
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...als/technology_demonstrations/classiq_paper/qsvt/pennylane_cat_qsvt_example.metadata.json
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| { | ||
| "friendly_name": "Pennylane Qsvt", | ||
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| "description": "Pennylane code for QSVT example", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } | ||
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tutorials/technology_demonstrations/classiq_paper/qsvt/qiskit_qsvt.metadata.json
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| { | ||
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| "friendly_name": "Qiskit Qsvt", | ||
| "description": "Qiskit code for QSVT example", | ||
| "vertical_tags": [], | ||
| "problem_domain_tags": [], | ||
| "qmod_type": [], | ||
| "level": [] | ||
| } | ||
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