add gate by gate sampling and MPS local expectation methods

jcmgray · Oct 18, 2024 · d57eb53 · d57eb53
1 parent ab9f014
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diff --git a/docs/_static/amplitude.png b/docs/_static/amplitude.png
diff --git a/docs/_static/branching.png b/docs/_static/branching.png
diff --git a/docs/_static/compute_marginal.png b/docs/_static/compute_marginal.png
diff --git a/docs/_static/local_expectation.png b/docs/_static/local_expectation.png
diff --git a/docs/_static/partial_trace.png b/docs/_static/partial_trace.png
diff --git a/docs/_static/sample.png b/docs/_static/sample.png
diff --git a/docs/_static/sample_chaotic.png b/docs/_static/sample_chaotic.png
diff --git a/docs/_static/sample_gate_by_gate.png b/docs/_static/sample_gate_by_gate.png
diff --git a/docs/_static/to_dense.png b/docs/_static/to_dense.png
diff --git a/docs/changelog.md b/docs/changelog.md
@@ -2,14 +2,26 @@
 
 Release notes for `quimb`.
 
-(whats-new-1-8-5)=
-## v1.8.5 (unreleased)
+(whats-new-1-9-0)=
+## v1.9.0 (unreleased)
+
+**Breaking Changes**
+
+- renamed `MatrixProductState.partial_trace` and `MatrixProductState.ptr` to [MatrixProductState.partial_trace_to_mpo](quimb.tensor.tensor_1d.MatrixProductState.partial_trace_to_mpo) to avoid confusion with other `partial_trace` methods that usually produce a dense matrix.
 
 **Enhancements:**
 
+- add [`Circuit.sample_gate_by_gate`](quimb.tensor.circuit.Circuit.sample_gate_by_gate) and related methods [`CircuitMPS.reordered_gates_dfs_clustered`](quimb.tensor.circuit.Circuit.reordered_gates_dfs_clustered) and [`CircuitMPS.get_qubit_distances`](quimb.tensor.circuit.CircuitMPS.get_qubit_distances) for sampling a circuit using the 'gate by gate' method introduced in https://arxiv.org/abs/2112.08499.
+- add [`Circuit.draw`](quimb.tensor.circuit.Circuit.draw) for drawing a very simple circuit schematic.
+- [`Circuit`](quimb.tensor.circuit.Circuit): by default turn on `simplify_equalize_norms` and use a `group_size=10` for sampling. This should result in faster and more stable sampling.
+- [`Circuit`](quimb.tensor.circuit.Circuit): use `numpy.random.default_rng` for random number generation.
+- add [`qtn.circ_a2a_rand`](quimb.tensor.circuit_gen.circ_a2a_rand) for generating random all-to-all circuits.
 - expose [`qtn.edge_coloring`](quimb.tensor.tensor_arbgeom_tebd.edge_coloring) as top level function and allow layers to be returned grouped.
-- add docstring for [`tn.contract_compressed`](quimb.tensor.tensor_core.TensorNetwork.contract_compressed)
+- add docstring for [`tn.contract_compressed`](quimb.tensor.tensor_core.TensorNetwork.contract_compressed) and by default pick up important settings from the supplied contraction path optimizer (`max_bond` and `compress_late`)
 - add [`Tensor.rand_reduce`](quimb.tensor.tensor_core.Tensor.rand_reduce) for randomly removing a tensor index by contracting a random vector into it. One can also supply the value `"r"` to `isel` selectors to use this.
+- add `fit-zipup` and `fit-projector` shorthand methods to the general 1d tensor network compression function
+- add [`MatrixProductState.compute_local_expectation`](quimb.tensor.tensor_1d.MatrixProductState.compute_local_expectation) for computing many local expectations for a MPS at once, to match the interface for this method elsewhere. These can either be computed via canonicalization (`method="canonical"`), or via explicit left and right environment contraction (`method="envs"`)
+- specialize [`CircuitMPS.local_expectation`](quimb.tensor.circuit.CircuitMPS.local_expectation) to make use of the MPS form.
 
 ---
 

diff --git a/docs/tensor-circuit.ipynb b/docs/tensor-circuit.ipynb
diff --git a/quimb/calc.py b/quimb/calc.py
@@ -357,8 +357,7 @@ def simulate_counts(p, C, phys_dim=2, seed=None):
         {'000': 514, '111': 510}
 
     """
-    if seed is not None:
-        np.random.seed(seed)
+    rng = np.random.default_rng(seed)
 
     n = infer_size(p, phys_dim)
     d = phys_dim**n
@@ -372,7 +371,7 @@ def simulate_counts(p, C, phys_dim=2, seed=None):
     pi = pi.reshape(-1)
 
     # raw counts in terms of integers
-    raw_counts = np.random.choice(np.arange(d), size=C, p=pi)
+    raw_counts = rng.choice(d, size=C, p=pi)
 
     # convert to frequencies of binary
     bin_str = "{:0>" + str(n) + "b}"

diff --git a/quimb/schematic.py b/quimb/schematic.py
@@ -430,6 +430,9 @@ def marker(self, coo, preset=None, **kwargs):
             coo, preset=preset, n=n, orientation=orientation, **style
         )
 
+    def square(self, coo, preset=None, **kwargs):
+        return self.marker(coo, preset=preset, marker="s", **kwargs)
+
     def cube(self, coo, preset=None, **kwargs):
         """Draw a cube at the specified coordinate, which must be 3D.
 
@@ -825,6 +828,24 @@ def shape(self, coos, preset=None, **kwargs):
         for coo in coos:
             self._adjust_lims(*coo)
 
+    def rectangle(self, cooa, coob, preset=None, **kwargs):
+        style = parse_style_preset(self.presets, preset, **kwargs)
+        radius = style.pop("radius", 0.25)
+
+        forward, inverse = get_rotator_and_inverse(cooa, coob)
+
+        # rotate both onto y=0
+        xa, _ = forward(*cooa)
+        xb, _ = forward(*coob)
+        points = [
+            (xa - radius, -radius),
+            (xa - radius, +radius),
+            (xb + radius, +radius),
+            (xb + radius, -radius),
+        ]
+        points = [inverse(*coo) for coo in points]
+        self.shape(points, **style)
+
     def patch(self, coos, preset=None, **kwargs):
         """Draw a closed smooth patch through given coordinates.
 

diff --git a/quimb/tensor/__init__.py b/quimb/tensor/__init__.py
@@ -8,6 +8,7 @@
     Gate,
 )
 from .circuit_gen import (
+    circ_a2a_rand,
     circ_ansatz_1D_brickwork,
     circ_ansatz_1D_rand,
     circ_ansatz_1D_zigzag,
@@ -222,6 +223,7 @@
     "array_contract",
     "bonds_size",
     "bonds",
+    "circ_a2a_rand",
     "circ_ansatz_1D_brickwork",
     "circ_ansatz_1D_rand",
     "circ_ansatz_1D_zigzag",