Optimization
Quantum optimization is an emerging field that explores how quantum computing can be used to solve optimization problems, where the goal is to find the best solution among many possible alternatives. Applications include logistics and scheduling, portfolio optimization, resource allocation, and materials science.
Quantum optimization approaches include quantum approximate optimization algorithms (QAOA), quantum annealing, and hybrid quantum-classical methods that combine quantum processors with classical optimization techniques. Although quantum computing can offer advantages for certain optimization problems, identifying practical performance benefits over classical methods remains an active area of research.
Map domain problems
These capabilities specialize in mapping domain problems into quantum operators and circuits for execution on a quantum computer.
Qiskit Optimization mapper (opens in a new tab)
Model optimization problems and map them into representations that can be understood by a quantum computer.
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Optimization best practices (opens in a new tab)
A collection of guidelines to run quantum optimization algorithms on superconducting qubits with Qiskit.
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QAOA training pipeline (opens in a new tab)
A pipeline and methods to generate good parameters for QAOA Ansatz circuits.
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Postprocessing
Some explanatory text
Application functions
IBM Quantum Functions are an experimental feature available only to IBM Quantum® Premium Plan, Flex Plan, and On-Prem (via IBM Quantum Platform API) Plan users. Refer to Set up IBM Quantum Functions for instructions on getting access.