feat(lattice): Make lattice geometries differentiable and backend-agn…

examples/lattice_neighbor_time_compare.py

@@ -0,0 +1,111 @@
+"""
+Benchmark: Compare neighbor-building time between KDTree and distance-matrix
+methods in CustomizeLattice for varying lattice sizes.
+"""
+
+import argparse
+import csv
+import time
+from typing import Iterable, List, Tuple, Optional
+import logging
+
+import numpy as np
+
+# Silence verbose infos from the library during benchmarks
+
+logging.basicConfig(level=logging.WARNING)
+
+from tensorcircuit.templates.lattice import CustomizeLattice
+
+
+def run_once(
+    n: int, d: int, max_k: int, repeats: int, seed: int
+) -> Tuple[float, float]:
+    """Run one size point and return (time_kdtree, time_matrix)."""
+    rng = np.random.default_rng(seed)
+    ids = list(range(n))
+
+    # Collect times for each repeat with different random coordinates
+    kdtree_times: List[float] = []
+    matrix_times: List[float] = []
+
+    for _ in range(repeats):
+        # Generate different coordinates for each repeat
+        coords = rng.random((n, d), dtype=float)
+        lat = CustomizeLattice(dimensionality=d, identifiers=ids, coordinates=coords)
+
+        # KDTree path - single measurement
+        t0 = time.perf_counter()
+        lat._build_neighbors(max_k=max_k, use_kdtree=True)
+        kdtree_times.append(time.perf_counter() - t0)
+
+        # Distance-matrix path - single measurement
+        t0 = time.perf_counter()
+        lat._build_neighbors(max_k=max_k, use_kdtree=False)
+        matrix_times.append(time.perf_counter() - t0)
+
+    return float(np.mean(kdtree_times)), float(np.mean(matrix_times))
+
+
+def parse_sizes(s: str) -> List[int]:
+    return [int(x) for x in s.split(",") if x.strip()]
+
+
+def format_row(n: int, t_kdtree: float, t_matrix: float) -> str:
+    speedup = (t_matrix / t_kdtree) if t_kdtree > 0 else float("inf")
+    return f"{n:>8} | {t_kdtree:>12.6f} | {t_matrix:>14.6f} | {speedup:>7.2f}x"
+
+
+def main(argv: Optional[Iterable[str]] = None) -> int:
+    p = argparse.ArgumentParser(description="Neighbor-building time comparison")
+    p.add_argument(
+        "--sizes",
+        type=parse_sizes,
+        default=[128, 256, 512, 1024, 2048],
+        help="Comma-separated site counts to benchmark (default: 128,256,512,1024,2048)",
+    )
+    p.add_argument(
+        "--dims", type=int, default=2, help="Lattice dimensionality (default: 2)"
+    )
+    p.add_argument(
+        "--max-k", type=int, default=6, help="Max neighbor shells k (default: 6)"
+    )
+    p.add_argument(
+        "--repeats", type=int, default=5, help="Repeats per measurement (default: 5)"
+    )
+    p.add_argument("--seed", type=int, default=42, help="PRNG seed (default: 42)")
+    p.add_argument("--csv", type=str, default="", help="Optional CSV output path")
+    args = p.parse_args(list(argv) if argv is not None else None)
+
+    print("=" * 74)
+    print(
+        f"Benchmark CustomizeLattice neighbor-building | dims={args.dims} max_k={args.max_k} repeats={args.repeats}"
+    )
+    print("=" * 74)
+    print(f"{'N':>8} | {'KDTree(s)':>12} | {'DistMatrix(s)':>14} | {'Speedup':>7}")
+    print("-" * 74)
+
+    rows: List[Tuple[int, float, float]] = []
+    for n in args.sizes:
+        t_kdtree, t_matrix = run_once(n, args.dims, args.max_k, args.repeats, args.seed)
+        rows.append((n, t_kdtree, t_matrix))
+        print(format_row(n, t_kdtree, t_matrix))
+
+    if args.csv:
+        with open(args.csv, "w", newline="", encoding="utf-8") as f:
+            w = csv.writer(f)
+            w.writerow(["N", "time_kdtree_s", "time_distance_matrix_s", "speedup"])
+            for n, t_kdtree, t_matrix in rows:
+                speedup = (t_matrix / t_kdtree) if t_kdtree > 0 else float("inf")
+                w.writerow([n, f"{t_kdtree:.6f}", f"{t_matrix:.6f}", f"{speedup:.2f}"])
+
+        print("-" * 74)
+        print(f"Saved CSV to: {args.csv}")
+
+    print("-" * 74)
+    print("Done.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

examples/lennard_jones_optimization.py

@@ -0,0 +1,119 @@
+"""
+Lennard-Jones Potential Optimization Example
+
+This script demonstrates how to use TensorCircuit's differentiable lattice geometries
+to optimize crystal structure. It finds the equilibrium lattice constant that minimizes
+the total Lennard-Jones potential energy of a 2D square lattice.
+
+The optimization showcases the key Task 3 capability: making lattice parameters
+differentiable for variational material design.
+"""
+
+import optax
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorcircuit as tc
+
+
+tc.set_dtype("float64")  # Use tc for universal control
+K = tc.set_backend("jax")
+
+
+def calculate_potential(log_a, epsilon=0.5, sigma=1.0):
+    """
+    Calculate the total Lennard-Jones potential energy for a given logarithm of the lattice constant (log_a).
+    This version creates the lattice inside the function to demonstrate truly differentiable geometry.
+    """
+    lattice_constant = K.exp(log_a)
+
+    # Create lattice with the differentiable parameter
+    size = (4, 4)  # Smaller size for demonstration
+    lattice = tc.templates.lattice.SquareLattice(
+        size, lattice_constant=lattice_constant, pbc=True
+    )
+    d = lattice.distance_matrix
+
+    d_safe = K.where(d > 1e-9, d, K.convert_to_tensor(1e-9))
+
+    term12 = K.power(sigma / d_safe, 12)
+    term6 = K.power(sigma / d_safe, 6)
+    potential_matrix = 4 * epsilon * (term12 - term6)
+
+    num_sites = lattice.num_sites
+    # Zero out self-interactions (diagonal elements)
+    eye_mask = K.eye(num_sites, dtype=potential_matrix.dtype)
+    potential_matrix = potential_matrix * (1 - eye_mask)
+
+    potential_energy = K.sum(potential_matrix) / 2.0
+
+    return potential_energy
+
+
+# Create value and grad function for optimization
+value_and_grad_fun = K.jit(K.value_and_grad(calculate_potential))
+
+optimizer = optax.adam(learning_rate=0.01)
+
+log_a = K.convert_to_tensor(K.log(K.convert_to_tensor(1.1)))
+
+opt_state = optimizer.init(log_a)
+
+history = {"a": [], "energy": []}
+
+print("Starting optimization of lattice constant...")
+for i in range(200):
+    energy, grad = value_and_grad_fun(log_a)
+
+    history["a"].append(K.exp(log_a))
+    history["energy"].append(energy)
+
+    updates, opt_state = optimizer.update(grad, opt_state)
+    log_a = optax.apply_updates(log_a, updates)
+
+    if (i + 1) % 20 == 0:
+        current_a = K.exp(log_a)
+        print(
+            f"Iteration {i+1}/200: Total Energy = {energy:.4f}, Lattice Constant = {current_a:.4f}"
+        )
+
+final_a = K.exp(log_a)
+final_energy = calculate_potential(log_a)
+
+print("\nOptimization finished!")
+print(f"Final optimized lattice constant: {final_a:.6f}")
+print(f"Corresponding minimum total energy: {final_energy:.6f}")
+
+# Vectorized calculation for the potential curve
+a_vals = np.linspace(0.8, 1.5, 200)
+log_a_vals = K.log(K.convert_to_tensor(a_vals))
+
+# Use vmap to create a vectorized version of the potential function
+vmap_potential = K.vmap(lambda la: calculate_potential(la))
+potential_curve = vmap_potential(log_a_vals)
+
+plt.figure(figsize=(10, 6))
+plt.plot(a_vals, potential_curve, label="Lennard-Jones Potential", color="blue")
+plt.scatter(
+    history["a"],
+    history["energy"],
+    color="red",
+    s=20,
+    zorder=5,
+    label="Optimization Steps",
+)
+plt.scatter(
+    final_a,
+    final_energy,
+    color="green",
+    s=100,
+    zorder=6,
+    marker="*",
+    label="Final Optimized Point",
+)
+
+plt.title("Lennard-Jones Potential Optimization")
+plt.xlabel("Lattice Constant (a)")
+plt.ylabel("Total Potential Energy")
+plt.legend()
+plt.grid(True)
+plt.show()

tensorcircuit/backends/abstract_backend.py

@@ -596,6 +596,82 @@ def argsort(self: Any, a: Tensor, axis: int = -1) -> Tensor:
             "Backend '{}' has not implemented `argsort`.".format(self.name)
         )
 
+    def sort(self: Any, a: Tensor, axis: int = -1) -> Tensor:
+        """
+        Sort a tensor along the given axis.
+
+        :param a: [description]
+        :type a: Tensor
+        :param axis: [description], defaults to -1
+        :type axis: int, optional
+        :return: [description]
+        :rtype: Tensor
+        """
+        raise NotImplementedError(
+            "Backend '{}' has not implemented `sort`.".format(self.name)
+        )
+
+    def all(self: Any, a: Tensor, axis: Optional[Sequence[int]] = None) -> Tensor:
+        """
+        Test whether all array elements along a given axis evaluate to True.
+
+        :param a: Input tensor
+        :type a: Tensor
+        :param axis: Axis or axes along which a logical AND reduction is performed,
+            defaults to None
+        :type axis: Optional[Sequence[int]], optional
+        :return: A new boolean or tensor resulting from the AND reduction
+        :rtype: Tensor
+        """
+        raise NotImplementedError(
+            "Backend '{}' has not implemented `all`.".format(self.name)
+        )
+
+    def meshgrid(self: Any, *args: Any, **kwargs: Any) -> Any:
+        """
+         Return coordinate matrices from coordinate vectors.
+
+        :param args: coordinate vectors
+        :type args: Any
+        :param kwargs: keyword arguments for meshgrid, typically includes 'indexing'
+           which can be 'ij' (matrix indexing) or 'xy' (Cartesian indexing).
+           - 'ij': matrix indexing, first dimension corresponds to rows (default)
+           - 'xy': Cartesian indexing, first dimension corresponds to columns
+           Example:
+              >>> x, y = backend.meshgrid([0, 1], [0, 2], indexing='xy')
+              Shapes:
+              - x.shape == (2, 2)  # rows correspond to y vector length
+              - y.shape == (2, 2)
+              Values:
+              x = [[0, 1],
+                  [0, 1]]
+              y = [[0, 0],
+                  [2, 2]]
+        :type kwargs: Any
+        :return: list of coordinate matrices
+        :rtype: Any
+        """
+        raise NotImplementedError(
+            "Backend '{}' has not implemented `meshgrid`.".format(self.name)
+        )
+
+    def expand_dims(self: Any, a: Tensor, axis: int) -> Tensor:
+        """
+        Expand the shape of a tensor.
+        Insert a new axis that will appear at the `axis` position in the expanded
+        tensor shape.
+
+        :param a: Input tensor
+        :type a: Tensor
+        :param axis: Position in the expanded axes where the new axis is placed
+        :type axis: int
+        :return: Output tensor with the number of dimensions increased by one.
+        :rtype: Tensor
+        """
+        raise NotImplementedError(
+            "Backend '{}' has not implemented `expand_dims`.".format(self.name)
+        )
+
     def unique_with_counts(self: Any, a: Tensor, **kws: Any) -> Tuple[Tensor, Tensor]:
         """
         Find the unique elements and their corresponding counts of the given tensor ``a``.
@@ -733,6 +809,21 @@ def cast(self: Any, a: Tensor, dtype: str) -> Tensor:
             "Backend '{}' has not implemented `cast`.".format(self.name)
         )
 
+    def convert_to_tensor(self: Any, a: Tensor, dtype: Optional[str] = None) -> Tensor:
+        """
+        Convert input to tensor.
+
+        :param a: input data to be converted
+        :type a: Tensor
+        :param dtype: target dtype, optional
+        :type dtype: Optional[str]
+        :return: converted tensor
+        :rtype: Tensor
+        """
+        raise NotImplementedError(
+            "Backend '{}' has not implemented `convert_to_tensor`.".format(self.name)
+        )
+
     def mod(self: Any, x: Tensor, y: Tensor) -> Tensor:
         """
         Compute y-mod of x (negative number behavior is not guaranteed to be consistent)
@@ -1404,6 +1495,28 @@ def cond(
             "Backend '{}' has not implemented `cond`.".format(self.name)
         )
 
+    def where(
+        self: Any,
+        condition: Tensor,
+        x: Optional[Tensor] = None,
+        y: Optional[Tensor] = None,
+    ) -> Tensor:
+        """
+        Return a tensor of elements selected from either x or y, depending on condition.
+
+        :param condition: Where True, yield x, otherwise yield y.
+        :type condition: Tensor (bool)
+        :param x: Values from which to choose when condition is True.
+        :type x: Tensor
+        :param y: Values from which to choose when condition is False.
+        :type y: Tensor
+        :return: A tensor with elements from x where condition is True, and y otherwise.
+        :rtype: Tensor
+        """
+        raise NotImplementedError(
+            "Backend '{}' has not implemented `where`.".format(self.name)
+        )
+
     def switch(
         self: Any, index: Tensor, branches: Sequence[Callable[[], Tensor]]
     ) -> Tensor:

tensorcircuit/backends/cupy_backend.py

@@ -56,10 +56,12 @@ def __init__(self) -> None:
         cpx = cupyx
         self.name = "cupy"
 
-    def convert_to_tensor(self, a: Tensor) -> Tensor:
+    def convert_to_tensor(self, a: Tensor, dtype: Optional[str] = None) -> Tensor:
         if not isinstance(a, cp.ndarray) and not cp.isscalar(a):
             a = cp.array(a)
         a = cp.asarray(a)
+        if dtype is not None:
+            a = self.cast(a, dtype)
         return a
 
     def sum(