An Introduction to Quantum Computing
An Introduction to Quantum Computing ____________________________________________________________________________________
Quantum PCA (qPCA)
Potential exponential under assumptions
Principal-component analysis
Machine learning
Quantum SVM / QSVM
Potential advantage for some feature maps/data
Classification
Machine learning
Quantum Neural Networks (QNN)
Machine learning
Research-stage
AI/ML
Quantum Boltzmann Machines
Generative/learning models
Research-stage
AI/ML
Quantum Monte Carlo methods
Simulation/estimation
Often quadratic
Finance, physics
Hamiltonian Simulation
Major potential advantage
Simulate quantum dynamics
Chemistry, materials, physics
Trotter–Suzuki simulation
Efficient quantum simulation
Approximate time evolution
Chemistry/physics
Quantum Signal Processing (QSP)
Very powerful algorithmic primitive
Polynomial transformations
Simulation, linear algebra
Quantum Singular Value Transformation (QSVT)
Framework encompassing many algorithms
Matrix transformations
Linear algebra, simulation
Quantum Imaginary Time Evolution (QITE)
Potential simulation advantage
Ground-state preparation
Chemistry/materials
Quantum Approximate Counting
Estimate solution counts
Quadratic
Optimisation
Quantum Minimum Finding
Find minimum value
Quadratic
Optimisation
_______________________________________________________________________________________________ SCTE Technical Committee : Dave Hodges and Dr Anthony Basham. © 2026 Society for Broadband Professionals. All rights reserved Page 22
Made with FlippingBook - Online magazine maker