Module quantum 

High-performance quantum annealing primitives.

Accelerates the hot paths in the Python quantum_annealing bridge:

• Simulated annealing: Metropolis-Hastings with exponential schedule
• Ising energy: Vectorized energy evaluation
• Gauge transform: Batch gauge generation and application
• Problem decomposition: Greedy graph partitioning

Modules

bindings 🔒

Python bindings for the quantum annealing acceleration primitives.

Structs

Xoshiro256pp 🔒

Functions

batch_ising_energy

Batch evaluate energies for many configurations (rayon-parallelized).

build_j_index 🔒

Build adjacency index for fast delta-energy lookup.

delta_energy 🔒

Delta energy ΔE = E_after - E_before for flipping spin qubit from s_q to -s_q in the Ising model H = Σ h_i s_i + Σ J_ij s_i s_j.

gauge_transform

Apply a gauge transform to Ising biases and couplings.

generate_gauges

Generate n_gauges random gauge vectors.

greedy_partition

Greedy graph partitioning for problem decomposition.

ising_energy

Compute Ising energy for a spin configuration.

simulated_annealing

Run simulated annealing on an Ising model.