Adiabatic Quantum Computing: Model and Limits
Adiabatic quantum computing solves problems by slowly changing a Hamiltonian. The adiabatic theorem, Ising models, energy gaps and limits.
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Quantum Algorithms
Shor, Grover, QAOA, VQE and the rest — what each quantum algorithm does, the problem it targets and where the promised speedup really comes from.
85 articles
Quantum Phase Estimation: Accuracy and Cost
The accuracy and cost of quantum phase estimation: how precision scales with ancilla qubits, the role of phase kickback, and error sources.
The Quantum Fourier Transform in Algorithms
How the quantum Fourier transform is used in algorithms such as Shor’s and phase estimation, how it is built from gates and how it compares with the FFT.
Quantum Walk Search Algorithms and Their Speedups
Search algorithms based on quantum walks: continuous and discrete time walks on graphs, their relation to Grover’s algorithm and their limits.
Quantum Signal Processing: Polynomials From Rotations
Quantum signal processing builds polynomial transformations from sequences of qubit rotations. How it underlies modern quantum algorithms.
Quantum Heuristic Models: Guesswork With Qubits
Quantum heuristics combine quantum computing with approximate problem-solving methods. How they work, where they are tested and their limits.
Quantum Annealers: What the Hardware Actually Does
What quantum annealers such as D-Wave’s physically do, how they differ from gate-based computers, and what real use cases show about their limits.
Adiabatic Quantum Computers: The Machines Themselves
Adiabatic quantum computers solve problems by slowly evolving a system in its ground state. Principles, comparison with gate models and uses.
Quantum Genetic Algorithms and Their Search Space
Quantum genetic algorithms represent candidate solutions with qubits. How they differ from classical genetic algorithms and where they are used.
Quantum Algorithm Design: Patterns That Recur
Recurring patterns in quantum algorithm design: oracles, amplitude amplification, the Fourier transform and error correction constraints.
Quantum Annealing for Optimization: What It Solves
What quantum annealing can solve: how it works, how it compares with classical optimizers, its hardware and software, and applications.
Quantum Stochastic Modelling: Noise as Mathematics
Quantum stochastic modeling combines quantum mechanics with random processes to describe open, noisy systems. Methods and applications.
