Fix cases for qml.sample(..., counts=True)

doc/releases/changelog-dev.md

@@ -180,6 +180,7 @@
 
 * Samples can be grouped into counts by passing the `counts=True` flag to `qml.sample`.
   [(#2686)](https://github.com/PennyLaneAI/pennylane/pull/2686)
+  [(#2839)](https://github.com/PennyLaneAI/pennylane/pull/2839)
 
   Note that the change included creating a new `Counts` measurement type in `measurements.py`.
 
@@ -211,8 +212,7 @@
   ...   return qml.sample(qml.PauliZ(0), counts=True), qml.sample(qml.PauliZ(1), counts=True)
   >>> result = circuit()
   >>> print(result)
-  [tensor({-1: 526, 1: 474}, dtype=object, requires_grad=True)
-   tensor({-1: 526, 1: 474}, dtype=object, requires_grad=True)]
+  ({-1: 470, 1: 530}, {-1: 470, 1: 530})
   ```
 
 * The `qml.state` and `qml.density_matrix` measurements now support custom wire

pennylane/_qubit_device.py

@@ -264,6 +264,8 @@ def execute(self, circuit, **kwargs):
             self._samples = self.generate_samples()
 
         multiple_sampled_jobs = circuit.is_sampled and self._has_partitioned_shots()
+        ret_types = [m.return_type for m in circuit.measurements]
+        no_counts = all(ret is not qml.measurements.Counts for ret in ret_types)
 
         # compute the required statistics
         if not self.analytic and self._shot_vector is not None:
@@ -279,20 +281,29 @@ def execute(self, circuit, **kwargs):
 
                 if qml.math._multi_dispatch(r) == "jax":  # pylint: disable=protected-access
                     r = r[0]
-                elif not isinstance(r[0], dict):
+                elif no_counts:
                     # Measurement types except for Counts
                     r = qml.math.squeeze(r)
-                if isinstance(r, (np.ndarray, list)) and r.shape and isinstance(r[0], dict):
-                    # This happens when measurement type is Counts
-                    results.append(r)
+
+                if not no_counts:
+
+                    # This happens when at least one measurement type is Counts
+                    for lst in r:
+                        if isinstance(lst, list):
+                            # List that contains one or more dictionaries
+                            results.extend(lst)
+                        else:
+                            # Other measurement results
+                            results.append(lst.T)
+
                 elif shot_tuple.copies > 1:
                     results.extend(r.T)
                 else:
                     results.append(r.T)
 
                 s1 = s2
 
-            if not multiple_sampled_jobs:
+            if not multiple_sampled_jobs and no_counts:
                 # Can only stack single element outputs
                 results = qml.math.stack(results)
 
@@ -301,8 +312,6 @@ def execute(self, circuit, **kwargs):
 
         if not circuit.is_sampled:
 
-            ret_types = [m.return_type for m in circuit.measurements]
-
             if len(circuit.measurements) == 1:
                 if circuit.measurements[0].return_type is qml.measurements.State:
                     # State: assumed to only be allowed if it's the only measurement
@@ -317,15 +326,18 @@ def execute(self, circuit, **kwargs):
             ):
                 # Measurements with expval or var
                 results = self._asarray(results, dtype=self.R_DTYPE)
-            elif any(ret is not qml.measurements.Counts for ret in ret_types):
-                # all the other cases except all counts
+            elif no_counts:
+                # all the other cases except any counts
                 results = self._asarray(results)
 
         elif circuit.all_sampled and not self._has_partitioned_shots():
 
             results = self._asarray(results)
         else:
-            results = tuple(self._asarray(r) for r in results)
+            results = tuple(
+                qml.math.squeeze(self._asarray(r)) if not isinstance(r, dict) else r
+                for r in results
+            )
 
         # increment counter for number of executions of qubit device
         self._num_executions += 1
@@ -1011,6 +1023,7 @@ def _samples_to_counts(samples, no_observable_provided):
                 # Before converting to str, we need to extract elements from arrays
                 # to satisfy the case of jax interface, as jax arrays do not support str.
                 samples = ["".join([str(s.item()) for s in sample]) for sample in samples]
+
             states, counts = np.unique(samples, return_counts=True)
             return dict(zip(states, counts))
 
@@ -1054,14 +1067,17 @@ def _samples_to_counts(samples, no_observable_provided):
             if counts:
                 return _samples_to_counts(samples, no_observable_provided)
             return samples
+
+        num_wires = len(device_wires) if len(device_wires) > 0 else self.num_wires
         if counts:
-            shape = (-1, bin_size, 3) if no_observable_provided else (-1, bin_size)
+            shape = (-1, bin_size, num_wires) if no_observable_provided else (-1, bin_size)
             return [
                 _samples_to_counts(bin_sample, no_observable_provided)
                 for bin_sample in samples.reshape(shape)
             ]
+
         return (
-            samples.reshape((3, bin_size, -1))
+            samples.reshape((num_wires, bin_size, -1))
             if no_observable_provided
             else samples.reshape((bin_size, -1))
         )

pennylane/interfaces/autograd.py

@@ -111,15 +111,12 @@ def _execute(
 
     for i, r in enumerate(res):
 
+        if any(m.return_type is qml.measurements.Counts for m in tapes[i].measurements):
+            continue
+
         if isinstance(r, np.ndarray):
             # For backwards compatibility, we flatten ragged tape outputs
             # when there is no sampling
-            try:
-                if isinstance(r[0][0], dict):
-                    # This happens when measurement type is Counts and shot vector is passed
-                    continue
-            except (IndexError, KeyError):
-                pass
             r = np.hstack(r) if r.dtype == np.dtype("object") else r
             res[i] = np.tensor(r)
 

pennylane/interfaces/jax.py

@@ -169,16 +169,26 @@ def cp_tape(t, a):
         tc.set_parameters(a)
         return tc
 
+    def array_if_not_counts(tape, r):
+        """Auxiliary function to convert the result of a tape to an array,
+        unless the tape had Counts measurements that are represented with
+        dictionaries. JAX NumPy arrays don't support dictionaries."""
+        return (
+            jnp.array(r)
+            if not any(m.return_type is qml.measurements.Counts for m in tape.measurements)
+            else r
+        )
+
     @jax.custom_vjp
     def wrapped_exec(params):
         new_tapes = [cp_tape(t, a) for t, a in zip(tapes, params)]
         with qml.tape.Unwrap(*new_tapes):
             res, _ = execute_fn(new_tapes, **gradient_kwargs)
 
         if len(tapes) > 1:
-            res = [jnp.array(r) for r in res]
+            res = [array_if_not_counts(tape, r) for tape, r in zip(tapes, res)]
         else:
-            res = jnp.array(res)
+            res = array_if_not_counts(tapes[0], res)
 
         return res
 

pennylane/interfaces/tensorflow.py

@@ -91,6 +91,9 @@ def execute(tapes, device, execute_fn, gradient_fn, gradient_kwargs, _n=1, max_d
     for i, tape in enumerate(tapes):
         # convert output to TensorFlow tensors
 
+        if any(m.return_type is qml.measurements.Counts for m in tape.measurements):
+            continue
+
         if isinstance(res[i], np.ndarray):
             # For backwards compatibility, we flatten ragged tape outputs
             # when there is no sampling

pennylane/interfaces/torch.py

@@ -97,6 +97,9 @@ def forward(ctx, kwargs, *parameters):  # pylint: disable=arguments-differ
                 # For backwards compatibility, we flatten ragged tape outputs
                 r = np.hstack(r)
 
+            if any(m.return_type is qml.measurements.Counts for m in ctx.tapes[i].measurements):
+                continue
+
             if isinstance(r, (list, tuple)):
                 res[i] = [torch.as_tensor(t) for t in r]
 

pennylane/measurements.py

@@ -654,21 +654,20 @@ def circuit(x):
 
     .. code-block:: python3
 
-        dev = qml.device('default.qubit', wires=3, shots=10)
+        dev = qml.device("default.qubit", wires=3, shots=10)
+
 
         @qml.qnode(dev)
         def my_circ():
             qml.Hadamard(wires=0)
-            qml.CNOT(wires=[0,1])
+            qml.CNOT(wires=[0, 1])
             qml.PauliX(wires=2)
-            return qml.sample(qml.PauliZ(0), counts = True), qml.sample(counts=True)
+            return qml.sample(qml.PauliZ(0), counts=True), qml.sample(counts=True)
 
     Executing this QNode:
 
     >>> my_circ()
-    tensor([tensor({-1: 5, 1: 5}, dtype=object, requires_grad=True),
-        tensor({'001': 5, '111': 5}, dtype=object, requires_grad=True)],
-       dtype=object, requires_grad=True)
+    ({-1: 3, 1: 7}, {'001': 7, '111': 3})
 
     .. note::
 

pennylane/qnode.py

@@ -640,6 +640,25 @@ def __call__(self, *args, **kwargs):
 
             res = res[0]
 
+        if (
+            not isinstance(self._qfunc_output, Sequence)
+            and self._qfunc_output.return_type is qml.measurements.Counts
+        ):
+
+            if not self.device._has_partitioned_shots():
+                # return a dictionary with counts not as a single-element array
+                return res[0]
+
+            return tuple(res)
+
+        if isinstance(self._qfunc_output, Sequence) and qml.measurements.Counts in set(
+            out.return_type for out in self._qfunc_output
+        ):
+            # If Counts was returned with other measurements, then apply the
+            # data structure used in the qfunc
+            qfunc_output_type = type(self._qfunc_output)
+            return qfunc_output_type(res)
+
         if override_shots is not False:
             # restore the initialization gradient function
             self.gradient_fn, self.gradient_kwargs, self.device = original_grad_fn
@@ -650,9 +669,6 @@ def __call__(self, *args, **kwargs):
             self.tape.is_sampled and self.device._has_partitioned_shots()
         ):
             return res
-        if self._qfunc_output.return_type is qml.measurements.Counts:
-            # return a dictionary with counts not as a single-element array
-            return res[0]
 
         return qml.math.squeeze(res)