Arraylias integration - Signal class -

docs/tutorials/optimizing_pulse_sequence.rst

@@ -115,9 +115,10 @@ example of one.
 .. jupyter-execute::
 
     from qiskit_dynamics import DiscreteSignal
-    from qiskit_dynamics.array import Array
     from qiskit_dynamics.signals import Convolution
 
+    import jax.numpy as jnp
+
     # define convolution filter
     def gaus(t):
         sigma = 15
@@ -128,13 +129,12 @@ example of one.
 
     # define function mapping parameters to signals
     def signal_mapping(params):
-        samples = Array(params)
 
         # map samples into [-1, 1]
-        bounded_samples = np.arctan(samples) / (np.pi / 2)
+        bounded_samples = jnp.arctan(params) / (np.pi / 2)
 
         # pad with 0 at beginning
-        padded_samples = np.append(Array([0], dtype=complex), bounded_samples)
+        padded_samples = jnp.append(jnp.array([0], dtype=complex), bounded_samples)
 
         # apply filter
         output_signal = convolution(DiscreteSignal(dt=1., samples=padded_samples))

docs/userguide/how_to_configure_simulations.rst

@@ -246,7 +246,7 @@ further highlight the benefits of the sparse representation.
 
     static_hamiltonian = 2 * np.pi * v * N + np.pi * anharm * N * (N - np.eye(dim))
     drive_hamiltonian = 2 * np.pi * r * (a + adag)
-    drive_signal = Signal(Array(1.), carrier_freq=v)
+    drive_signal = Signal(1., carrier_freq=v)
 
     y0 = np.zeros(dim, dtype=complex)
     y0[1] = 1.
@@ -266,7 +266,7 @@ amplitude, and just-in-time compile it using JAX.
     )
 
     def dense_func(amp):
-        drive_signal = Signal(Array(amp), carrier_freq=v)
+        drive_signal = Signal(amp, carrier_freq=v)
         res = solver.solve(
             t_span=[0., T],
             y0=y0,
@@ -292,7 +292,7 @@ diagonal, but we explicitly highlight the need for this.
                            evaluation_mode='sparse')
 
     def sparse_func(amp):
-        drive_signal = Signal(Array(amp), carrier_freq=v)
+        drive_signal = Signal(amp, carrier_freq=v)
         res = sparse_solver.solve(
             t_span=[0., T],
             y0=y0,

docs/userguide/how_to_use_jax.rst

@@ -138,7 +138,6 @@ before setting the signals, to ensure the simulation function remains pure.
     def sim_function(amp):
 
         # define a constant signal
-        amp = Array(amp)
         signals = [Signal(amp, carrier_freq=w)]
 
         # simulate and return results

docs/userguide/how_to_use_pulse_schedule_for_jax_jit.rst

@@ -137,6 +137,6 @@ JAX-compiled (or more generally, JAX-transformed).
         # convert from a pulse schedule to a list of signals
         converter = InstructionToSignals(dt, carriers={"d0": w})
 
-        return converter.get_signals(schedule)[0].samples.data
+        return converter.get_signals(schedule)[0].samples
 
     jax.jit(jit_func)(0.4)

docs/userguide/perturbative_solvers.rst

@@ -179,7 +179,7 @@ these functions gives a sense of the speeds attainable by these solvers.
         """For a given envelope amplitude, simulate the final unitary using the
         Dyson solver.
         """
-        drive_signal = Signal(lambda t: Array(amp) * envelope_func(t), carrier_freq=v)
+        drive_signal = Signal(lambda t: amp * envelope_func(t), carrier_freq=v)
         return dyson_solver.solve(
             signals=[drive_signal],
             y0=np.eye(dim, dtype=complex),
@@ -220,7 +220,7 @@ accuracy and simulation speed.
 
     # specify tolerance as an argument to run the simulation at different tolerances
     def ode_sim(amp, tol):
-        drive_signal = Signal(lambda t: Array(amp) * envelope_func(t), carrier_freq=v)
+        drive_signal = Signal(lambda t: amp * envelope_func(t), carrier_freq=v)
         res = solver.solve(
             t_span=[0., int(T // dt) * dt],
             y0=np.eye(dim, dtype=complex),

qiskit_dynamics/__init__.py

@@ -28,6 +28,7 @@
     DYNAMICS_SCIPY_ALIAS,
     DYNAMICS_NUMPY,
     DYNAMICS_SCIPY,
+    ArrayLike,
 )
 
 from .models.rotating_frame import RotatingFrame

qiskit_dynamics/arraylias/__init__.py

@@ -22,4 +22,10 @@
 Module for Qiskit Dynamics global NumPy and SciPy aliases.
 """
 
-from .alias import DYNAMICS_NUMPY_ALIAS, DYNAMICS_SCIPY_ALIAS, DYNAMICS_NUMPY, DYNAMICS_SCIPY
+from .alias import (
+    DYNAMICS_NUMPY_ALIAS,
+    DYNAMICS_SCIPY_ALIAS,
+    DYNAMICS_NUMPY,
+    DYNAMICS_SCIPY,
+    ArrayLike,
+)

qiskit_dynamics/arraylias/alias.py

@@ -20,6 +20,8 @@
 
 from arraylias import numpy_alias, scipy_alias
 
+from qiskit import QiskitError
+
 from qiskit_dynamics.array import Array
 
 # global NumPy and SciPy aliases
@@ -34,4 +36,51 @@
 DYNAMICS_SCIPY = DYNAMICS_SCIPY_ALIAS()
 
 
-ArrayLike = Union[DYNAMICS_NUMPY_ALIAS.registered_types()]
+ArrayLike = Union[Union[DYNAMICS_NUMPY_ALIAS.registered_types()], list]
+
+
+def _preferred_lib(*args, **kwargs):
+    """Given a list of args and kwargs with potentially mixed array types, determine the appropriate
+    library to dispatch to.
+
+    For each argument, DYNAMICS_NUMPY_ALIAS.infer_libs is called to infer the library. If all are
+    "numpy", then it returns "numpy", and if any are "jax", it returns "jax".
+
+    Args:
+        *args: Positional arguments.
+        **kwargs: Keyword arguments.
+    Returns:
+        str
+    Raises:
+        QiskitError: if none of the rules apply.
+    """
+    args = list(args) + list(kwargs.values())
+    if len(args) == 1:
+        return DYNAMICS_NUMPY_ALIAS.infer_libs(args[0])
+
+    lib0 = DYNAMICS_NUMPY_ALIAS.infer_libs(args[0])[0]
+    lib1 = _preferred_lib(args[1:])[0]
+
+    if lib0 == "numpy" and lib1 == "numpy":
+        return "numpy"
+    elif lib0 == "jax" or lib1 == "jax":
+        return "jax"
+
+    raise QiskitError("_preferred_lib could not resolve preferred library.")
+
+
+def _numpy_multi_dispatch(*args, path, **kwargs):
+    """Multiple dispatching for NumPy.
+
+    Given *args and **kwargs, dispatch the function specified by path, to the array library
+    specified by _preferred_lib.
+
+    Args:
+        *args: Positional arguments to pass to function specified by path.
+        path: Path in numpy module structure.
+        **kwargs: Keyword arguments to pass to function specified by path.
+    Returns:
+        Result of evaluating the function at path on the arguments using the preferred library.
+    """
+    lib = _preferred_lib(*args, **kwargs)
+    return DYNAMICS_NUMPY_ALIAS(like=lib, path=path)(*args, **kwargs)

qiskit_dynamics/models/operator_collections.py

@@ -20,6 +20,7 @@
 
 from qiskit import QiskitError
 from qiskit.quantum_info.operators.operator import Operator
+from qiskit_dynamics.arraylias.alias import _numpy_multi_dispatch
 from qiskit_dynamics.array import Array, wrap
 from qiskit_dynamics.type_utils import to_array, to_csr, to_BCOO, vec_commutator, vec_dissipator
 
@@ -1357,7 +1358,7 @@ def concatenate_signals(
     ) -> Array:
         """Concatenate hamiltonian and linblad signals."""
         if self._hamiltonian_operators is not None and self._dissipator_operators is not None:
-            return np.append(ham_sig_vals, dis_sig_vals, axis=-1)
+            return _numpy_multi_dispatch(ham_sig_vals, dis_sig_vals, path="append", axis=-1)
         if self._hamiltonian_operators is not None and self._dissipator_operators is None:
             return ham_sig_vals
         if self._hamiltonian_operators is None and self._dissipator_operators is not None:

qiskit_dynamics/pulse/pulse_to_signals.py

@@ -39,6 +39,9 @@
 from qiskit import QiskitError
 
 from qiskit_dynamics.array import Array
+from qiskit_dynamics import DYNAMICS_NUMPY as unp
+from qiskit_dynamics import ArrayLike
+
 from qiskit_dynamics.signals import DiscreteSignal
 
 try:
@@ -186,12 +189,10 @@ def get_signals(self, schedule: Schedule) -> List[DiscreteSignal]:
 
                 # build sample array to append to signal
                 times = self._dt * (start_sample + np.arange(len(inst_samples)))
-                samples = inst_samples * np.exp(
-                    Array(
-                        2.0j * np.pi * frequency_shifts[chan] * times
-                        + 1.0j * phases[chan]
-                        + 2.0j * np.pi * phase_accumulations[chan]
-                    )
+                samples = inst_samples * unp.exp(
+                    2.0j * np.pi * frequency_shifts[chan] * times
+                    + 1.0j * phases[chan]
+                    + 2.0j * np.pi * phase_accumulations[chan]
                 )
                 signals[chan].add_samples(start_sample, samples)
 
@@ -202,7 +203,7 @@ def get_signals(self, schedule: Schedule) -> List[DiscreteSignal]:
                 phases[chan] = inst.phase
 
             if isinstance(inst, ShiftFrequency):
-                frequency_shifts[chan] = frequency_shifts[chan] + Array(inst.frequency)
+                frequency_shifts[chan] = frequency_shifts[chan] + inst.frequency
                 phase_accumulations[chan] = (
                     phase_accumulations[chan] - inst.frequency * start_sample * self._dt
                 )
@@ -273,7 +274,7 @@ def get_awg_signals(
             new_freq = sig.carrier_freq + if_modulation
 
             samples_i = sig.samples
-            samples_q = np.imag(samples_i) - 1.0j * np.real(samples_i)
+            samples_q = unp.imag(samples_i) - 1.0j * unp.real(samples_i)
 
             sig_i = DiscreteSignal(
                 sig.dt,
@@ -325,7 +326,7 @@ def _get_channel(self, channel_name: str):
             ) from error
 
 
-def get_samples(pulse: SymbolicPulse) -> np.ndarray:
+def get_samples(pulse: SymbolicPulse) -> ArrayLike:
     """Return samples filled according to the formula that the pulse
     represents and the parameter values it contains.
 
@@ -349,8 +350,8 @@ def get_samples(pulse: SymbolicPulse) -> np.ndarray:
     args = []
     for symbol in sorted(envelope.free_symbols, key=lambda s: s.name):
         if symbol.name == "t":
-            times = Array(np.arange(0, pulse_params["duration"]) + 1 / 2)
-            args.insert(0, times.data)
+            times = unp.arange(0, pulse_params["duration"]) + 1 / 2
+            args.insert(0, times)
             continue
         try:
             args.append(pulse_params[symbol.name])
@@ -381,11 +382,11 @@ def _lru_cache_expr(expr: sym.Expr, backend) -> Callable:
     return sym.lambdify(params, expr, modules=backend)
 
 
-def _nyquist_warn(frequency_shift: Array, dt: float, channel: str):
+def _nyquist_warn(frequency_shift: ArrayLike, dt: float, channel: str):
     """Raise a warning if the frequency shift is above the Nyquist frequency given by ``dt``."""
-
     if (
-        Array(frequency_shift).backend != "jax" or not isinstance(jnp.array(0), jax.core.Tracer)
+        isinstance(frequency_shift, (int, float, list, np.ndarray))
+        or not isinstance(jnp.array(0), jax.core.Tracer)
     ) and np.abs(frequency_shift) > 0.5 / dt:
         warn(
             "Due to SetFrequency and ShiftFrequency instructions, the digital carrier frequency "