Remove pandas dataframe from quantum volume. (#136)

* Remove pandas dataframe from quantum volume.

* Move wrapper functions to examples notebook.

CHANGELOG.md

@@ -5,9 +5,9 @@ v0.6 (June 11, 2018)
 --------------------
 Breaking Changes:
 
-- `operator_estimation.py` is entirely replaced.
+- `operator_estimation.py` is entirely replaced. All changes from (gh-135) except where stated otherwise.
 
-- `pyquil.operator_estimation` dependencies replaced with `forest.benchmarking.operator_estimation`
+- `pyquil.operator_estimation` dependencies replaced with `forest.benchmarking.operator_estimation` (gh-129,132,133,134,135)
 
 - `operator_estimation.TomographyExperiment.out_op` -> `operator_estimation.ObservablesExperiment.out_observable`
 
@@ -17,13 +17,13 @@ Breaking Changes:
 
 - `utils.all_pauli_terms` -> `utils.all_traceless_pauli_terms` 
 
-- `DFEData` and `DFEEstimate` dataclasses removed in favor of `ExperimentResult` and tuple of results respectively.
+- `DFEData` and `DFEEstimate` dataclasses removed in favor of `ExperimentResult` and tuple of results respectively (gh-134).
 
 - plotting moved out of `qubit_spectroscopy`; instead, use `fit_*_results()` to get a `lmfit.model.ModelResult` and pass this into `analysis.fitting.make_figure()`
 
-- `pandas.DataFrame` is no longer used in `randomized_benchmarking`, `qubit_spectroscopy`, and `robust_phase_estimation`. These now make use of `operator_estimation.ObservablesExperiment`, and as such the API has changed substantially. Please refer to example notebooks for new usage.
+- `pandas.DataFrame` is no longer used in `randomized_benchmarking` (gh-133), `qubit_spectroscopy` (gh-129), and `robust_phase_estimation` (gh-135). These now make use of `operator_estimation.ObservablesExperiment`, and as such the API has changed substantially. Please refer to example notebooks for new usage.
 
-- `pandas.DataFrame` methods removed from `quantum_volume`. See examples notebook for alternative usage. 
+- `pandas.DataFrame` methods removed from `quantum_volume`. See examples notebook for alternative usage (gh-136). 
 
 - `utils.determine_simultaneous_grouping()` removed in favor of similar functionality in `operator_estimation.group_settings`
 

examples/quantum_volume.ipynb

@@ -213,7 +213,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## For more fine-grained control, create and maintain a dataframe"
+    "## Run intermediate steps yourself"
    ]
   },
   {
@@ -222,19 +222,61 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from forest.benchmarking.quantum_volume import (generate_quantum_volume_experiments,\n",
-    "                                                add_programs_to_dataframe,\n",
-    "                                                acquire_quantum_volume_data,\n",
-    "                                                acquire_heavy_hitters,\n",
-    "                                                get_results_by_depth,\n",
-    "                                                extract_quantum_volume_from_results)"
+    "from forest.benchmarking.quantum_volume import generate_abstract_qv_circuit, _naive_program_generator, collect_heavy_outputs\n",
+    "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n",
+    "from pyquil.gates import RESET\n",
+    "import time\n",
+    "\n",
+    "def generate_circuits(depths):\n",
+    "    for d in depths:\n",
+    "        yield generate_abstract_qv_circuit(d)\n",
+    "\n",
+    "def convert_ckts_to_programs(qc, circuits, qubits=None):\n",
+    "    for idx, ckt in enumerate(circuits):\n",
+    "        if qubits is None:\n",
+    "            d_qubits = qc.qubits()  # by default the program can act on any qubit in the computer\n",
+    "        else:\n",
+    "            d_qubits = qubits[idx]\n",
+    "\n",
+    "        yield _naive_program_generator(qc, d_qubits, *ckt)\n",
+    "\n",
+    "\n",
+    "def acquire_quantum_volume_data(qc, programs, num_shots = 1000, use_active_reset = False):\n",
+    "    for program in programs:\n",
+    "        start = time.time()\n",
+    "\n",
+    "        if use_active_reset:\n",
+    "            reset_measure_program = Program(RESET())\n",
+    "            program = reset_measure_program + program\n",
+    "\n",
+    "        # run the program num_shots many times\n",
+    "        program.wrap_in_numshots_loop(num_shots)\n",
+    "        executable = qc.compiler.native_quil_to_executable(program)\n",
+    "\n",
+    "        results = qc.run(executable)\n",
+    "\n",
+    "        runtime = time.time() - start\n",
+    "        yield results\n",
+    "\n",
+    "\n",
+    "def acquire_heavy_hitters(abstract_circuits):\n",
+    "    for ckt in abstract_circuits:\n",
+    "        perms, gates = ckt\n",
+    "        depth = len(perms)\n",
+    "        wfn_sim = NumpyWavefunctionSimulator(depth)\n",
+    "\n",
+    "        start = time.time()\n",
+    "        heavy_outputs = collect_heavy_outputs(wfn_sim, perms, gates)\n",
+    "        runtime = time.time() - start\n",
+    "\n",
+    "        yield heavy_outputs\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Get a dataframe with (depth x n_circuits) many \"Abstract Ckt\"s that describe each model circuit for each depth."
+    "Generate (len(unique_depths) x n_circuits) many \"Abstract Ckt\"s that describe each model circuit for each depth."
    ]
   },
   {
@@ -244,16 +286,17 @@
    "outputs": [],
    "source": [
     "n_circuits = 100\n",
-    "depths = [2,3]\n",
-    "df = generate_quantum_volume_experiments(depths, n_circuits)\n",
-    "df"
+    "unique_depths = [2,3]\n",
+    "depths = [d for d in unique_depths for _ in range(n_circuits)]\n",
+    "ckts = list(generate_circuits(depths))\n",
+    "print(ckts[0])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Use the default program_generator to synthesize native pyquil programs that implement each ckt natively on the qc."
+    "Use the _naive_program_generator to synthesize native pyquil programs that implement each ckt natively on the qc."
    ]
   },
   {
@@ -262,8 +305,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = add_programs_to_dataframe(df, noisy_qc)\n",
-    "print(df[\"Program\"].values[0])"
+    "progs = list(convert_ckts_to_programs(noisy_qc, ckts))\n",
+    "print(progs[0])"
    ]
   },
   {
@@ -279,7 +322,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = acquire_quantum_volume_data(df, noisy_qc, num_shots=10)"
+    "num_shots=10\n",
+    "results = list(acquire_quantum_volume_data(noisy_qc, progs, num_shots=num_shots))"
    ]
   },
   {
@@ -295,8 +339,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = acquire_heavy_hitters(df)\n",
-    "print(df[\"Num HH Sampled\"].values[0:10])"
+    "ckt_hhs = acquire_heavy_hitters(ckts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Count the number of heavy hitters that were sampled on the qc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from forest.benchmarking.quantum_volume import count_heavy_hitters_sampled\n",
+    "num_hh_sampled = count_heavy_hitters_sampled(results, ckt_hhs)"
    ]
   },
   {
@@ -312,7 +372,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = get_results_by_depth(df)\n",
+    "from forest.benchmarking.quantum_volume import get_prob_sample_heavy_by_depth\n",
+    "\n",
+    "results = get_prob_sample_heavy_by_depth(depths, num_hh_sampled, [num_shots for _ in depths])\n",
     "results"
    ]
   },
@@ -329,6 +391,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from forest.benchmarking.quantum_volume import extract_quantum_volume_from_results\n",
     "qv = extract_quantum_volume_from_results(results)\n",
     "qv"
    ]