File size: 7,348 Bytes
9425aed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | //! # Trajectory Export
//!
//! Converts stochastic solver output (density matrix trajectories) into
//! flat Float32 binary files for direct WebGL consumption.
//!
//! ## Binary Format
//! Layout: `[traj₀_step₀(x,y,z), traj₀_step₁(x,y,z), ..., traj₁_step₀(x,y,z), ...]`
//! - Little-endian Float32 (matches JavaScript Float32Array and WebGL)
//! - 3 floats per vertex (x, y, z coordinates on Bloch sphere)
//! - Trajectories grouped contiguously
//!
//! ## Coordinate Mapping
//! Density matrix ρ (2×2 qubit) → Bloch sphere coordinates:
//! - x = 2·Re(ρ₀₁)
//! - y = 2·Im(ρ₀₁)
//! - z = ρ₀₀ - ρ₁₁
//!
//! For higher-dimensional states, projects onto first 3 principal components.
use ndarray::{Array2, Array3};
use std::fs::File;
use std::io::{self, Write};
/// Flattens an ndarray trajectory tensor and writes to raw Float32 binary.
///
/// # Arguments
/// * `trajectory_tensor` - Shape [time_steps, batch_size, 3] of f32 coordinates
/// * `output_path` - Path to write the binary file
///
/// # Binary Layout
/// Contiguous Float32 values, little-endian:
/// `[batch₀_t₀_x, batch₀_t₀_y, batch₀_t₀_z, batch₀_t₁_x, ...]`
///
/// Note: For WebGL consumption, data is re-ordered to group by trajectory
/// (all steps of traj 0, then all steps of traj 1, etc.)
pub fn export_trajectory_to_bin(
trajectory_tensor: &Array3<f32>,
output_path: &str,
) -> io::Result<()> {
let (time_steps, batch_size, coords) = trajectory_tensor.dim();
assert_eq!(coords, 3, "Expected 3 coordinates per vertex, got {coords}");
// Re-order from [time, batch, 3] to [batch, time, 3] for WebGL
// (WebGL needs all steps of one trajectory contiguous)
let total_floats = batch_size * time_steps * 3;
let mut flat = Vec::with_capacity(total_floats);
for b in 0..batch_size {
for t in 0..time_steps {
flat.push(trajectory_tensor[[t, b, 0]]);
flat.push(trajectory_tensor[[t, b, 1]]);
flat.push(trajectory_tensor[[t, b, 2]]);
}
}
// Zero-copy byte cast and write
let byte_slice: &[u8] = bytemuck::cast_slice(&flat);
let mut file = File::create(output_path)?;
file.write_all(byte_slice)?;
file.flush()?;
eprintln!(
"Exported {} trajectories × {} steps = {} vertices to {}",
batch_size, time_steps, batch_size * time_steps, output_path
);
Ok(())
}
/// Converts a 2×2 density matrix to Bloch sphere coordinates.
///
/// For qubit state ρ:
/// - x = 2·Re(ρ₀₁) = Tr[σₓ ρ]
/// - y = 2·Im(ρ₀₁) = Tr[σᵧ ρ]
/// - z = ρ₀₀ - ρ₁₁ = Tr[σ_z ρ]
///
/// Returns (x, y, z) as f32 tuple.
pub fn density_matrix_to_bloch(rho: &Array2<f64>) -> (f32, f32, f32) {
assert_eq!(rho.dim(), (2, 2), "Bloch conversion requires 2×2 density matrix");
let x = 2.0 * rho[[0, 1]]; // Re(ρ₀₁) — for real density matrices
let y = 0.0f64; // Im(ρ₀₁) — zero for real matrices; complex case needs separate handling
let z = rho[[0, 0]] - rho[[1, 1]];
(x as f32, y as f32, z as f32)
}
/// Generates a synthetic demo trajectory dataset for testing the frontend
/// without running the full stochastic solver.
///
/// Simulates φ⁻¹ contraction toward origin (entropy maximum) with Brownian noise.
///
/// # Returns
/// Array3<f32> of shape [time_steps, batch_size, 3]
pub fn generate_demo_data(time_steps: usize, batch_size: usize, dt: f32, diffusion: f32) -> Array3<f32> {
use std::f32::consts::PI;
let phi: f32 = (1.0 + 5.0f32.sqrt()) / 2.0;
let contraction = 1.0 / phi;
let mut data = Array3::zeros((time_steps, batch_size, 3));
// Simple LCG for reproducibility without external deps
let mut seed: u64 = 42;
let mut rng = || -> f32 {
seed = seed.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
let bits = ((seed >> 33) as u32) as f32 / (u32::MAX as f32);
bits * 2.0 - 1.0
};
for b in 0..batch_size {
// Random starting point on unit sphere
let theta = (rng() + 1.0) * 0.5 * PI;
let phi0 = (rng() + 1.0) * PI;
let mut x = theta.sin() * phi0.cos();
let mut y = theta.sin() * phi0.sin();
let mut z = theta.cos();
for t in 0..time_steps {
data[[t, b, 0]] = x;
data[[t, b, 1]] = y;
data[[t, b, 2]] = z;
// φ⁻¹ drift toward origin
let drift = contraction * dt;
x -= x * drift;
y -= y * drift;
z -= z * drift;
// Tangent-space noise
let noise_scale = (diffusion * dt).sqrt();
let nx = rng() * noise_scale;
let ny = rng() * noise_scale;
let nz = rng() * noise_scale;
// Project to tangent plane
let dot = nx * x + ny * y + nz * z;
let r2 = x * x + y * y + z * z;
if r2 > 1e-8 {
x += nx - dot * x / r2;
y += ny - dot * y / r2;
z += nz - dot * z / r2;
}
// Retract to decaying radius
let r = (x * x + y * y + z * z).sqrt();
if r > 1e-8 {
let target_r = (1.0 - (t as f32) * contraction * dt * 0.5).max(0.01);
x = x / r * target_r;
y = y / r * target_r;
z = z / r * target_r;
}
}
}
data
}
#[cfg(test)]
mod tests {
use super::*;
use std::path::Path;
#[test]
fn test_demo_data_shape() {
let data = generate_demo_data(100, 10, 0.01, 0.3);
assert_eq!(data.dim(), (100, 10, 3));
}
#[test]
fn test_demo_data_bounded() {
let data = generate_demo_data(200, 50, 0.01, 0.2);
for val in data.iter() {
assert!(val.abs() <= 1.5, "Coordinate out of bounds: {val}");
}
}
#[test]
fn test_export_creates_file() {
let data = generate_demo_data(10, 5, 0.01, 0.1);
let path = "test_trajectory_output.bin";
export_trajectory_to_bin(&data, path).expect("Export failed");
let metadata = std::fs::metadata(path).expect("File not found");
// 5 trajectories × 10 steps × 3 floats × 4 bytes = 600 bytes
assert_eq!(metadata.len(), 600);
std::fs::remove_file(path).ok();
}
#[test]
fn test_bloch_conversion_pure_state() {
// |0⟩⟨0| = [[1,0],[0,0]] → Bloch: (0, 0, 1) (north pole)
let rho = Array2::from_shape_vec((2, 2), vec![1.0, 0.0, 0.0, 0.0]).unwrap();
let (x, y, z) = density_matrix_to_bloch(&rho);
assert!((x - 0.0).abs() < 1e-6);
assert!((y - 0.0).abs() < 1e-6);
assert!((z - 1.0).abs() < 1e-6);
}
#[test]
fn test_bloch_conversion_mixed_state() {
// I/2 = [[0.5,0],[0,0.5]] → Bloch: (0, 0, 0) (origin)
let rho = Array2::from_shape_vec((2, 2), vec![0.5, 0.0, 0.0, 0.5]).unwrap();
let (x, y, z) = density_matrix_to_bloch(&rho);
assert!((x - 0.0).abs() < 1e-6);
assert!((y - 0.0).abs() < 1e-6);
assert!((z - 0.0).abs() < 1e-6);
}
}
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