Update pre-commit

Author: classiqdor

algorithms/amplitude_amplification_and_estimation/quantum_counting/quantum_counting.metadata.json

@@ -3,7 +3,6 @@
   "subtitle": "Quantum Counting Using the Iterative Quantum Amplitude Estimation Algorithm",
   "description": "Implementation of the quantum counting algorithm, which efficiently estimates the number of valid solutions to a search problem based on amplitude estimation. The example shows both Quantum Phase Estimation (QPE) and Iterative Quantum Amplitude Estimation (IQAE); IQAE reduces qubits and gates at the expense of a polylogarithmic factor in the error. The algorithm is applied to count solutions to a simple arithmetic equation using Classiq's grover_operator and qpe library functions.",
   "friendly_name": "Quantum Counting",
-  "vertical_tags": [],
   "problem_domain_tags": [],
   "qmod_type": [],
   "vertical_tags": []

algorithms/foundational/quantum_teleportation/quantum_teleportation.metadata.json

@@ -2,7 +2,6 @@
   "title": "Quantum Teleportation Protocol",
   "subtitle": "Quantum Teleportation Protocol",
   "description": "Implementation of quantum teleportation protocol, first proposed by bennett et al., is a foundational quantum communication method that enables the transfer of an arbitrary qubit state from one location (alice) to another (bob) using a combination of quantum entanglement and classical communication. it does not involve physically moving the qubit but rather transmitting its quantum information through shared entanglement.",
-  "description": "Quantum Teleportation Protocol",
   "friendly_name": "Quantum Teleportation Protocol",
   "vertical_tags": [],
   "problem_domain_tags": [],

algorithms/foundational/simon/simon.metadata.json

@@ -2,7 +2,6 @@
   "title": "Simon's Algorithm",
   "subtitle": "Solving Simon's problem for min(x,x^s) with secret string s",
   "description": "Implementation of one of the basic quantum algorithms, providing an exponential speedup over its classical counterpart. After defining the quantum and classical parts of the algorithm, it is run on two examples of Simon’s function: one definable with simple arithmetic, and another with a shallow low-level implementation.",
-  "description": "Solving Simon's problem for min(x,x^s) with secret string s",
   "friendly_name": "Simon's Algorithm",
   "vertical_tags": [],
   "problem_domain_tags": [],

algorithms/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_with_block_encoding.metadata.json

@@ -3,7 +3,6 @@
   "subtitle": "Hamiltonian Simulation with Quantum Signal Processing and Qubitization",
   "description": "Implementation of time-independent Hamiltonian simulation via block-encoding: approximating the evolution operator e^{-iHt} to precision ε using polynomial transformations of the encoded operator. The notebook demonstrates three state-of-the-art methods—QSVT, GQSP, and Qubitization (Chebyshev)—which give optimal scaling in time and error and are the core building blocks for many higher-level algorithms (e.g. HHL, quantum Gibbs state sampling). It compares trade-offs: Qubitization avoids classical preprocessing but uses more qubits; QSVT uses two auxiliary qubits and no controlled block-encoding; GQSP uses one auxiliary qubit and no amplification. This routine is central to simulating physical and chemical systems on quantum hardware.",
   "friendly_name": "Hamiltonian Simulation With Block Encoding",
-  "vertical_tags": [],
   "problem_domain_tags": [],
   "qmod_type": [],
   "vertical_tags": []

algorithms/number_theory_and_cryptography/shor/shor.metadata.json

@@ -3,12 +3,7 @@
   "subtitle": "Full Integer factorization using Shor's Algorithm and Modular Exponentiation",
   "description": "A full implementation of Shor’s factoring algorithm, one of the foundational quantum algorithms, providing an exponential speedup over currently known classical algorithms. The quantum component is naturally structured as a Quantum Phase Estimation (QPE) routine, utilizing Classiq’s built-in `flexible_qpe` and modular arithmetic.",
   "friendly_name": "Shor's Algorithm Modular Exponentiation",
-  "vertical_tags": ["cyber"],
   "problem_domain_tags": [],
-  "qmod_type": [
-    "algorithms"
-  ],
-  "vertical_tags": [
-    "cyber"
-  ]
+  "qmod_type": ["algorithms"],
+  "vertical_tags": ["cyber"]
 }

algorithms/qml/qgan/qgan_bars_and_strips.metadata.json

@@ -4,9 +4,7 @@
   "description": "Implementation of a Quantum Generative Adversarial Network (QGAN) using the Classiq SDK. The example uses the Bars and Stripes dataset: it builds a classical GAN first, then a hybrid quantum-classical GAN with a quantum generator, and trains and evaluates both.",
   "friendly_name": "QGAN Bars And Strips",
   "vertical_tags": [],
-  "problem_domain_tags": [
-    "machine learning"
-  ],
+  "problem_domain_tags": ["machine learning"],
   "qmod_type": [],
   "level": []
 }

algorithms/quantum_phase_estimation/qpe_with_qubitization/qpe_for_molecule_with_qubitization.metadata.json

@@ -3,7 +3,6 @@
   "subtitle": "Qubitization based Quantum Phase Estimation (QPE) for Solving Molecular Energies",
   "description": "Implementation of Quantum Phase Estimation for a molecule Hamiltonian using qubitization: given a block-encoding of the Hamiltonian, the algorithm performs efficient QPE. The notebook uses Classiq's Qmod built-in operations (control, power, within_apply, invert) to build the circuit.",
   "friendly_name": "Qpe For Molecule With Qubitization",
-  "vertical_tags": [],
   "problem_domain_tags": ["chemistry"],
   "qmod_type": [],
   "vertical_tags": []

algorithms/search_and_optimization/grover/grover.metadata.json

@@ -3,7 +3,6 @@
   "subtitle": "Grover's Search Algorithm",
   "description": "Implementation of Grover’s search algorithm, one of the canonical quantum algorithms. A general routine is defined and then applied to generic use cases, including the 3-SAT problem and the Max-Cut problem on a graph. The use of the phase_oracle quantum function from the Classiq open library, together with the Qmod language for high-level arithmetic operations, helps avoid the low-level implementation details typically required on other platforms.",
   "friendly_name": "Grover",
-  "vertical_tags": [],
   "problem_domain_tags": [],
   "qmod_type": [],
   "vertical_tags": []

algorithms/search_and_optimization/qaoa/qaoa.metadata.json

@@ -5,6 +5,5 @@
   "friendly_name": "QAOA",
   "vertical_tags": [],
   "problem_domain_tags": [],
-  "qmod_type": [],
-  "vertical_tags": []
-}
+  "qmod_type": []
+}