Example notebook descriptions

algorithms/amplitude_amplification_and_estimation/oblivious_amplitude_amplification/oblivious_amplitude_amplification.metadata.json

@@ -1,8 +1,15 @@
 {
   "friendly_name": "Oblivious Amplitude Amplification",
-  "description": "Oblivious Amplitude Amplification for the amplification of LCU",
-  "level": ["advanced", "demos"],
-  "problem_domain_tags": ["search"],
-  "qmod_type": ["algorithms"],
+  "description": "This demo explains the Oblivious Amplitude Amplification algorithm (OAA) , which can be used as a building block in algorithms such as Hamiltonian simulations. We start with a short recap, then show how to use it in conjunction with the Linear Combination of Unitaries (LCU) algorithm. * Promise: is unitary (or close to unitary).",
+  "level": [
+    "advanced",
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "search"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/amplitude_amplification_and_estimation/qmc_user_defined/qmc_user_defined.metadata.json

@@ -1,8 +1,16 @@
 {
   "friendly_name": "Amplitude Estimation",
-  "description": "Integral Evaluation using Amplitude Estimation",
-  "vertical_tags": ["finance"],
-  "problem_domain_tags": ["risk analysis"],
-  "qmod_type": ["algorithms"],
-  "level": ["demos"]
+  "description": "This notebook demonstrates Quantum Monte Carlo Integration by estimating the expectation value of a given function, illustrating how quantum amplitude estimation can achieve a quadratic speedup over classical Monte Carlo methods.",
+  "vertical_tags": [
+    "finance"
+  ],
+  "problem_domain_tags": [
+    "risk analysis"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
+  "level": [
+    "demos"
+  ]
 }

algorithms/amplitude_amplification_and_estimation/qsvt_fixed_point_amplitude_amplification/qsvt_fixed_point_amplitude_amplification.metadata.json

@@ -1,8 +1,15 @@
 {
   "friendly_name": "QSVT: Amplitude Amplification",
-  "description": "QSVT for Fixed-Point Amplitude Amplification",
-  "level": ["advanced", "demos"],
-  "problem_domain_tags": ["search"],
-  "qmod_type": ["algorithms"],
+  "description": "This notebook shows how to use the QSVT framework for search problems; specifically, implementing fixed-point amplitude amplification (FPAA). With FPAA, we do not know in advance the concentration of solutions for the search problem, but we want to sample a solution with high probability. In contrast, for the original Grover search algorithm, too many iterations might 'overshoot' the mark. The demo is based on the paper Grand unification of quantum algorithms.",
+  "level": [
+    "advanced",
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "search"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/amplitude_amplification_and_estimation/quantum_counting/quantum_counting.metadata.json

@@ -1,8 +1,8 @@
 {
   "friendly_name": "Quantum Counting",
-  "description": "Quantum Counting Using the Iterative Quantum Amplitude Estimation Algorithm",
-  "vertical_tags": [],
+  "description": "Implementation of the quantum counting algorithm, which efficiently estimates the number of valid solutions to a search problem based on amplitude estimation. This notebook demonstrates how to estimate the counting problem using the Iterative Quantum Amplitude Estimation (IQAE) algorithm, which reduces the required number of qubits and gates compared to Quantum Phase Estimation-based approaches, at the expense of an additional polylogarithmic factor in the error. The example applies the algorithm to count solutions to a simple arithmetic equation, showing both QPE-based and IQAE-based methods.",
+  "level": [],
   "problem_domain_tags": [],
   "qmod_type": [],
-  "level": []
+  "vertical_tags": []
 }

algorithms/foundational/bernstein_vazirani/bernstein_vazirani.metadata.json

@@ -1,8 +1,13 @@
 {
   "friendly_name": "Bernstein Vazirani",
-  "description": "Bernstein Vazirani example for secret integer 13 on 5 qubits",
-  "level": ["basic", "demos"],
+  "description": "Implementation of Bernstein-Vazirani (BV) algorithm, introduced by Ethan Bernstein and Umesh Vazirani, is a fundamental quantum algorithm that addresses a special case of the hidden-shift problem. It employs the same functional circuit structure as the Deutsch-Jozsa algorithm and achieves a linear speedup over its classical counterpart in the oracle query model.",
+  "level": [
+    "basic",
+    "demos"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/foundational/deutsch_jozsa/deutsch_jozsa.metadata.json

@@ -1,8 +1,13 @@
 {
   "friendly_name": "Deutsch-Jozsa",
-  "description": "Deutsch-Jozsa Algorithm",
-  "level": ["basic", "demos"],
+  "description": "Implementation of the famous Deutsch–Jozsa algorithm. A general quantum algorithm is defined, which takes a quantum predicate as a parameter, and different examples are explored. Using Classiq’s quantum arithmetic, compiling both simple and complex functions is equally easy.",
+  "level": [
+    "basic",
+    "demos"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/foundational/quantum_teleportation/quantum_teleportation.metadata.json

@@ -1,8 +1,12 @@
 {
   "friendly_name": "Quantum Teleportation Protocol",
-  "description": "Quantum Teleportation Protocol",
-  "level": ["basic"],
+  "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.",
+  "level": [
+    "basic"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/foundational/simon/simon.metadata.json

@@ -1,8 +1,12 @@
 {
   "friendly_name": "Simon's Algorithm",
-  "description": "Solving Simon's problem for min(x,x^s) with secret string s",
-  "level": ["demos"],
+  "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.",
+  "level": [
+    "demos"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/hamiltonian_simulation/hamiltonian_simulation_guide/hamiltonian_simulation_guide.metadata.json

@@ -1,6 +1,6 @@
 {
   "friendly_name": "Hamiltonian Simulation Guide",
-  "description": "Hamiltonian Simulation",
+  "description": "Implementation of quantum hamiltonian simulation. Hamiltonian simulation considers one of the most important problems in quantum computing. It consists of modeling the time evolution of a complex quantum system using more controllable devices; the quantum computers. Simulations of this type are essential to comprehend quantum systems that are described by complex dynamics, such as molecules in chemistry or materials in condensed matter physics, where classical computers fail due to the exponential scale up required by larger quantum systems.",
   "vertical_tags": [],
   "problem_domain_tags": [],
   "qmod_type": [],

algorithms/hamiltonian_simulation/hamiltonian_simulation_with_block_encoding/hamiltonian_simulation_with_block_encoding.metadata.json

@@ -1,8 +1,8 @@
 {
   "friendly_name": "Hamiltonian Simulation With Block Encoding",
-  "description": "Hamiltonian Simulation with Quantum Signal Processing and Qubitization",
-  "vertical_tags": [],
+  "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.",
+  "level": [],
   "problem_domain_tags": [],
   "qmod_type": [],
-  "level": []
+  "vertical_tags": []
 }

algorithms/number_theory_and_cryptography/discrete_log/discrete_log.metadata.json

@@ -1,8 +1,12 @@
 {
   "friendly_name": "Discrete Logarithm",
-  "description": "Solving Discrete Logarithm Problem using Shor's Algorithm",
-  "level": ["advanced"],
+  "description": "Implementation of Discrete Logarithm Problem  was shown by Shor  to be solved in a polynomial time using quantum computers, while the fastest classical algorithms take a superpolynomial time. The problem is at least as hard as the factoring problem. In fact, the hardness of the problem is the basis for the Diffie-Hellman  protocol for key exchange. The algorithm is a specific instance of the Abelian Hidden Subgroup Problem .",
+  "level": [
+    "advanced"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/number_theory_and_cryptography/elliptic_curves/elliptic_curve_discrete_log.metadata.json

@@ -1,8 +1,12 @@
 {
   "friendly_name": "Elliptic Curve Discrete Logarithm",
-  "description": "Solving Elliptic Curve Discrete Logarithm Problem using Shor's Algorithm",
-  "level": ["advanced"],
+  "description": "Implementation of Elliptic Curve Discrete Logarithm Problem (ECDLP) is a fundamental cryptographic challenge that underlies the security of elliptic curve cryptography (ECC). While classical algorithms require exponential time to solve ECDLP, Shor's quantum algorithm can solve it efficiently in polynomial time. An elliptic curve is a special type of mathematical curve defined by a cubic equation. For our purposes, we can think of it as the set of points that satisfy the Weierstrass equation:",
+  "level": [
+    "advanced"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/number_theory_and_cryptography/hidden_shift/hidden_shift.metadata.json

@@ -1,8 +1,12 @@
 {
   "friendly_name": "Hidden Shift",
-  "description": "Hidden-Shift problem for bent functions",
-  "level": ["demos"],
+  "description": "Implementation of here we implement the hidden shift algorithm for the family of boolean bent functions using the classiq platform.",
+  "level": [
+    "demos"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/number_theory_and_cryptography/shor/shor.metadata.json

@@ -1,8 +1,14 @@
 {
   "friendly_name": "Shor's Algorithm Modular Exponentiation",
-  "description": "Full Integer factorization using Shor's Algorithm and Modular Exponentiation",
-  "vertical_tags": ["cyber"],
+  "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.",
+  "level": [
+    "advanced"
+  ],
   "problem_domain_tags": [],
-  "qmod_type": ["algorithms"],
-  "level": ["advanced"]
+  "qmod_type": [
+    "algorithms"
+  ],
+  "vertical_tags": [
+    "cyber"
+  ]
 }

algorithms/qml/hybrid_qnn/hybrid_qnn_for_subset_majority.metadata.json

@@ -1,8 +1,14 @@
 {
   "friendly_name": "Hybrind QNN",
-  "description": "Hybrid Quantum Neural Network for Subset Majority",
-  "level": ["demos"],
-  "problem_domain_tags": ["machine learning"],
-  "qmod_type": ["algorithms"],
+  "description": "Implementation of neural networks is one of the major branches in machine learning, with wide use in applications and research. a neural network—or, more generally, a deep neural network—is a parametric function of a specific structure (inspired by neural networks in biology), which is trained to capture specific functionality. in its most basic form, a neural network for learning a function looks as follows: 1. there is an input vector of size (red circles in fig. 1).",
+  "level": [
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "machine learning"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/qml/qgan/qgan_bars_and_strips.metadata.json

@@ -1,8 +1,10 @@
 {
   "friendly_name": "QGAN Bars And Strips",
-  "description": "Quantum Generative Adversarial Networks (QGANs)",
+  "description": "Implementation of generative ai, especially through generative adversarial networks (gans), revolutionizes content creation across domains by producing highly realistic output. quantum gans further elevate this potential by leveraging quantum computing, promising unprecedented advancements in complex data simulation and analysis. in this notebook, we explore the concept of quantum generative adversarial networks (qgans) and implement a simple qgan model using the classiq sdk.",
   "vertical_tags": [],
-  "problem_domain_tags": ["machine learning"],
+  "problem_domain_tags": [
+    "machine learning"
+  ],
   "qmod_type": [],
   "level": []
 }

algorithms/qml/qsvm/qsvm.metadata.json

@@ -1,8 +1,14 @@
 {
   "friendly_name": "QSVM: Bloch Sphere Kernel",
-  "description": "QSVM Algorithm (with Bloch Sphere and Pauli feature Kernels)",
-  "level": ["demos"],
-  "problem_domain_tags": ["machine learning"],
-  "qmod_type": ["algorithms"],
+  "description": "Implementation of quantum support vector machines is the quantum version of classical support vector machines (svm); i.e., a data classification method that separates the data by performing a mapping to a high-dimensional space, in which the data is separated by a hyperplane . qsvm is a hybrid quantum–classical classification algorithm in which classical data are embedded into a high-dimensional quantum hilbert space using a parameterized quantum feature map.",
+  "level": [
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "machine learning"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/qml/quantum_autoencoder/quantum_autoencoder.metadata.json

@@ -1,8 +1,14 @@
 {
   "friendly_name": "Quantum Auto-Encoder",
-  "description": "Quantum Auto-Encoder: encoder network",
-  "level": ["demos"],
-  "problem_domain_tags": ["machine learning"],
-  "qmod_type": ["algorithms"],
+  "description": "A Quantum Machine Learning (QML) example in which a quantum program is trained to reduce the memory required to encode data with a given structure. In addition, the example demonstrates how to use the encoder for anomaly detection. Two training approaches for the quantum autoencoder are presented, leveraging Classiq’s integration with PyTorch. This example also illustrates the use of the RESET operation.",
+  "level": [
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "machine learning"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/quantum_differential_equations_solvers/discrete_poisson_solver/discrete_poisson_solver.metadata.json

@@ -1,8 +1,14 @@
 {
   "friendly_name": "Poisson's Equation Solver",
-  "description": "Solving the discrete Poisson's equation using the HHL Algorithm",
-  "level": ["advanced"],
-  "problem_domain_tags": ["linear equation"],
-  "qmod_type": ["algorithms"],
+  "description": "Implementation of a quantum solver for the discrete Poisson equation, a partial differential equation (PDE) widely used in physics and engineering. The equation models the distribution of a potential field due to a prescribed source term. The problem is reformulated as a system of linear equations and solved using the HHL algorithm. Leveraging quantum cosine and sine transforms from the open library enables a concise and clear implementation that can be generalized to higher dimensions.",
+  "level": [
+    "advanced"
+  ],
+  "problem_domain_tags": [
+    "linear equation"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/quantum_differential_equations_solvers/time_marching/time_marching.metadata.json

@@ -1,8 +1,14 @@
 {
   "friendly_name": "Time Marching Based Quantum Solver",
-  "description": "Solving time-dependent linear equations using a Time-Marching based strategy and QSVT",
-  "level": ["advanced"],
-  "problem_domain_tags": ["linear equation"],
-  "qmod_type": ["algorithms"],
+  "description": "This demonstration is based on the  paper. The notebook was written in collaboration with Prof. Di Fang, the first author of the paper. Time marching is a method for solving differential equations in time by integrating the solution vector through time in small discrete steps, where each timestep depends on previous timesteps. This paper applies an evolution matrix sequentially on the state and makes it evolve through time, as done in time-dependent Hamiltonian simulations.",
+  "level": [
+    "advanced"
+  ],
+  "problem_domain_tags": [
+    "linear equation"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/quantum_linear_solvers/adiabatic_linear_solvers/solving_qlsp_with_aqc.metadata.json

@@ -1,8 +1,16 @@
 {
   "friendly_name": "Solving QLSP with AQC",
-  "description": "Solving the Quantum Linear Systems Problem (QLSP) using Adiabatic Quantum Computing (AQC)",
-  "level": ["basic", "demos"],
-  "problem_domain_tags": ["adiabatic", "linear equation"],
-  "qmod_type": ["algorithms"],
+  "description": "Implementation of adiabatic quantum computing (aqc) leverages the adiabatic theorem to solve computational problems by gradually evolving a quantum system from an initial ground state to the ground state of a problem-specific hamiltonian (see the aqc tutorial). this tutorial focuses on applying the aqc approach to solve the quantum linear systems problem (qlsp), a cornerstone problem in quantum computing with significant applications in fields like machine learning, physics, and optimization.",
+  "level": [
+    "basic",
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "adiabatic",
+    "linear equation"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/quantum_linear_solvers/hhl/hhl.metadata.json

@@ -1,8 +1,16 @@
 {
   "friendly_name": "HHL",
-  "description": "Solving linear system of equations using HHL algorithm",
-  "level": ["advanced", "demos"],
-  "problem_domain_tags": ["machine learning", "linear equation"],
-  "qmod_type": ["algorithms"],
+  "description": "Implementation of HHL algorithm [\\[1\\]](#HHL), named after Harrow, Hassidim and Lloyd, is a fundamental quantum algorithm designed to solve a set of linear equations: where is an matrix, and and are vectors of size . The algorithm prepares a quantum state that encodes the solution vector proportional to , starting from the input state .",
+  "level": [
+    "advanced",
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "machine learning",
+    "linear equation"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }

algorithms/quantum_linear_solvers/qsvt_matrix_inversion/qsvt_matrix_inversion.metadata.json

@@ -1,8 +1,15 @@
 {
   "friendly_name": "QSVT: Matrix Inversion",
-  "description": "QSVT for Matrix Inversion",
-  "level": ["advanced", "demos"],
-  "problem_domain_tags": ["linear equation"],
-  "qmod_type": ["algorithms"],
+  "description": "Implementation of a general framework for solving linear systems based on the Quantum Singular Value Transform (QSVT). Given an efficient routine to embed the classical matrix as a quantum function (block-encoding), this algorithm provides a clean and optimal approach to implementing matrix inversion compared to other quantum methods. The use of the qsvt_inversion function from the open library, together with classical auxiliary functions from our qsp application, allows for an easy implementation of the algorithm, given two inputs: a block-encoding quantum function for the matrix and the matrix condition number.",
+  "level": [
+    "advanced",
+    "demos"
+  ],
+  "problem_domain_tags": [
+    "linear equation"
+  ],
+  "qmod_type": [
+    "algorithms"
+  ],
   "vertical_tags": []
 }