Quantum algorithms and benchmarks in plain language: what each family of algorithms does, which problems it addresses, how mature it is, which hardware platforms fit it and what it takes to run it, from a laptop simulation to a fault-tolerant machine.
22 algorithm families, 29 further algorithms from the Zoo index, 17 benchmarks, 11 resource estimates with 25 figures, each quoted with the assumptions and the publication it comes from.
Educational overview compiled by the portal from primary sources: papers (arXiv abstract pages and open-access articles), benchmark repositories and official pages, each read on the date shown. Resource estimates are quoted with the assumptions their authors state and change as methods improve; they are not forecasts, procurement guidance or investment advice. Summaries are in the portal’s own words. Owner-reviewed for demonstration, 28 October 2026. Compiled 2026-10-27.
3 algorithm families shown; 0 further algorithms shown.
Algorithm families
Fault-tolerant Factoring and cryptanalysis
Finds the prime factors of a large integer, or a discrete logarithm, in time that grows only polynomially with the number of digits. A large enough error-corrected quantum computer running it would break RSA and elliptic-curve public-key cryptography, which is why post-quantum cryptography is being deployed now.
Platforms: Superconducting qubits, Trapped ions, Photonic qubits
6 sourced resource estimates · 2 documented demonstrations
Details, requirements and sources Shor's algorithm: factoring and discrete logarithms
Noisy intermediate-scale (NISQ) Chemistry and materials
A hybrid method that estimates ground-state energies of molecules and magnets with short circuits whose parameters a classical optimiser tunes. It was designed for noisy devices without error correction.
Platforms: Photonic qubits, Superconducting qubits
0 sourced resource estimates · 2 documented demonstrations
Details, requirements and sources Variational quantum eigensolver (VQE)
Noisy intermediate-scale (NISQ) Sampling and quantum advantage tests
Photons sent through a large network of beam splitters produce output patterns whose probabilities are very hard to compute classically. It was proposed to show a quantum computational advantage with linear optics.
Platforms: Photonic qubits
0 sourced resource estimates · 1 documented demonstration
Details, requirements and sources Boson sampling and Gaussian boson sampling
Algorithms by hardware platform
A dash means that no source for that platform was verified for this release, not that it cannot work. Any marks families that are platform-independent.
Further algorithms (Zoo index)
Further notable quantum algorithms, found through the Quantum Algorithm Zoo and each checked against its original paper. They are mostly theoretical and need fault-tolerant machines; no resource estimate is given unless cited. Summaries and speedups are in the portal’s own words from the original papers; the Zoo category and speedup are given as the Zoo states them.
Index: the Quantum Algorithm Zoo by Stephen P. Jordan, last updated 2026-09-10, checked 2026-10-23. The portal links to it and copies no description from it. Browse the full Quantum Algorithm Zoo (external site)
These algorithms name no hardware platform, so the platform filter hides them.
Further reading
Olivier Ezratty, Understanding Quantum Technologies (CC BY-NC-ND 4.0). Links only; no text of the book is reproduced on this portal.