Quantum algorithms

The package implements Hamiltonian-based Binary VQE, Filtering VQE, and QAOA, plus a custom variational circuit allocator. All default to quantum simulation. Read the shared objectives and inputs before running the examples.

• Binary VQE: penalized Hamiltonian training and sampled selection.

• Filtering VQE: normalized filtered-state distance updates, with inverse or exponential filters and analytic or finite-shot execution.

• QAOA with X mixing: cost phases and unconstrained bit flips.

• QAOA with XY exchange mixing: ideal cardinality-preserving exchanges.

• CVaR training: optional lower-tail cost aggregation for Binary VQE and QAOA, using analytic Adam or finite-shot SPSA.

• Variational quantum allocator: circuit expectations mapped to simplex weights, with a classically evaluated objective.

Circuit ansatz choices, mixer topologies, initialization choices, and trained versus untrained presets are settings of these algorithms. Compatibility aliases do not identify additional implementations.