From 52c6bcded3c9779b0034f71f64a8173716b0d67b Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 09:34:03 +0400
Subject: [PATCH 01/24] remove parser

---
 main.py                        | 260 ++++++++++++++++-----------------
 src/boostvqe/plotscripts.py    |   2 +-
 src/boostvqe/training_utils.py |  22 +--
 src/boostvqe/utils.py          |  14 +-
 4 files changed, 148 insertions(+), 150 deletions(-)

diff --git a/main.py b/main.py
index 82918a2..f7f2761 100644
--- a/main.py
+++ b/main.py
@@ -3,12 +3,12 @@
 import logging
 import pathlib
 from functools import partial
+from typing import Optional
 
 import numpy as np
 
 # qibo's
-import qibo
-from qibo import gates, hamiltonians
+from qibo import Circuit, gates, hamiltonians, set_backend
 from qibo.backends import GlobalBackend
 from qibo.models.dbi.double_bracket import (
     DoubleBracketGeneratorType,
@@ -16,7 +16,6 @@
 )
 
 # boostvqe's
-from boostvqe import ansatze
 from boostvqe.plotscripts import plot_gradients, plot_loss
 from boostvqe.training_utils import Model, vqe_loss
 from boostvqe.utils import (
@@ -38,41 +37,129 @@
     train_vqe,
 )
 
+NSHOTS = 100
 
-def main(args):
-    """VQE training."""
-    # set backend and number of classical threads
 
-    if args.platform is not None:
-        qibo.set_backend(backend=args.backend, platform=args.platform)
-    else:
-        qibo.set_backend(backend=args.backend)
-        args.platform = GlobalBackend().platform
+def main(
+    circuit: Circuit,  # in place of ansatz
+    output_folder: str,
+    backend: Optional[str] = "qibojit",
+    platform: Optional[str] = None,
+    optimizer: str = "Powell",
+    optimizer_options: dict = {},
+    tol: float = TOL,
+    decay_rate_lr: float = 1.0,
+    nboost: int = 1,
+    boost_frequency: int = 10,
+    dbi_steps: int = 1,
+    optimize_dbi_step: bool = False,
+    store_h: bool = False,
+    hamiltonian: str = "XXZ",
+    seed: int = SEED,
+    nshots: int = NSHOTS,
+    nlayers: int = 0,
+):
+    """
+    Args:
+
+       circuit (Circuit): VQE circuit.
+
+       output_folder (str): where to dump the results.
+
+       backend (str, default: "qibojit"):
+            Specifies the quantum backend to use. Options include different backend implementations
+            such as "qibojit" or "qibotf".
+
+        platform (str, default: None):
+            Specifies the platform to run on, typically used for running on GPUs (e.g., "CUDA").
+
+        optimizer (str, default: "Powell"):
+            Optimizer used for VQE training. Options include "Powell", "BFGS", and others.
+
+        optimizer_options (str, optional):
+            Custom options to fine-tune the behavior of the optimizer.
+
+        tol (float, default: TOL):
+            Absolute precision tolerance to stop VQE training once the desired convergence is achieved.
+
+        decay_rate_lr (float, default: 1.0):
+            Learning rate decay factor used when the optimizer is SGD (stochastic gradient descent).
+
+        nboost (int, default: 1):
+            Number of times DBI (Deterministic Boost Iteration) is applied in the optimization process.
+            If set to 1, no boosting is performed.
+
+        boost_frequency (int, default: 10):
+            Number of optimization steps between DBI boosting calls.
 
-    if args.optimizer_options is None:
-        opt_options = {}
+        dbi_steps (int, default: 1):
+            Number of DBI iterations performed each time DBI is called.
+
+        stepsize (float, default: 0.01):
+            Step size used during DBI updates.
+
+        optimize_dbi_step (bool, default: False):
+            If set to True, the DBI step size is hyperoptimized.
+
+        store_h (bool, default: False):
+            If this flag is set, the Hamiltonian `H` is stored at each iteration.
+
+        hamiltonian (str, default: "XXZ"):
+            Specifies the Hamiltonian to use, chosen from predefined options in the `qibo.hamiltonians` module.
+
+        seed (str, default: SEED):
+            Random seed to ensure reproducibility of results.
+
+        nshots (int, optional):
+            Number of shots (measurements) to use during the quantum computation.
+
+        nlayers (int):
+            Number of circuit layers.
+    """
+
+    output_dict = locals()
+    del output_dict["circuit"]
+    output_dict["nqubits"] = circuit.nqubits
+    output_dict["nlayers"] = nlayers  # TODO: maybe remove it
+    # set backend and number of classical threads
+
+    if platform is not None:
+        set_backend(backend=backend, platform=platform)
     else:
-        opt_options = json.loads(args.optimizer_options)
+        set_backend(backend=backend)
+        platform = GlobalBackend().platform
 
+    nqubits = circuit.nqubits
     # setup the results folder
     logging.info("Set VQE")
-    path = pathlib.Path(create_folder(generate_path(args)))
-    ham = getattr(Model, args.hamiltonian)(args.nqubits)
+    path = pathlib.Path(
+        create_folder(
+            generate_path(
+                output_folder,
+                optimizer,
+                nqubits,
+                seed,
+                decay_rate_lr,
+            )
+        )
+    )
+    ham = getattr(Model, hamiltonian)(nqubits)  # TODO : use only Model and not str
+    # ham = hamiltonian(nqubits)
     target_energy = np.real(np.min(np.asarray(ham.eigenvalues())))
 
     # construct circuit from parsed ansatz name
-    circ0 = getattr(ansatze, args.ansatz)(args.nqubits, args.nlayers)
+    circ0 = circuit
 
     logging.info(circ0.draw())
-    
+
     circ = circ0.copy(deep=True)
     backend = ham.backend
-    zero_state = backend.zero_state(args.nqubits)
+    zero_state = backend.zero_state(nqubits)
 
-    loss = partial(vqe_loss, nshots=args.nshots)
+    loss = partial(vqe_loss, nshots=nshots)
 
     # fix numpy seed to ensure replicability of the experiment
-    np.random.seed(int(args.seed))
+    np.random.seed(int(seed))
     initial_parameters = np.random.uniform(-np.pi, np.pi, len(circ.get_parameters()))
 
     # vqe lists
@@ -83,9 +170,9 @@ def main(args):
     fun_eval, hamiltonians_history = [], []
     hamiltonians_history.append(ham.matrix)
     new_hamiltonian = ham
-    args.nboost += 1
-    for b in range(args.nboost):
-        logging.info(f"Running {b+1}/{args.nboost} max optimization rounds.")
+    nboost += 1
+    for b in range(nboost):
+        logging.info(f"Running {b+1}/{nboost} max optimization rounds.")
         boost_energies[b], boost_fluctuations_dbi[b] = [], []
         params_history[b], loss_history[b], fluctuations[b] = [], [], []
         # train vqe
@@ -99,13 +186,13 @@ def main(args):
         ) = train_vqe(
             circ,
             ham,  # Fixed hamiltonian
-            args.optimizer,
+            optimizer,
             initial_parameters,
-            args.tol,
-            niterations=args.boost_frequency,
+            tol,
+            niterations=boost_frequency,
             nmessage=1,
             loss=loss,
-            training_options=opt_options,
+            training_options=optimizer_options,
         )
         # append results to global lists
         params_history[b] = np.array(partial_params_history)
@@ -113,14 +200,14 @@ def main(args):
         grads_history[b] = np.array(partial_grads_history)
         fluctuations[b] = np.array(partial_fluctuations)
         # this works with scipy.optimize.minimize only
-        if args.optimizer not in ["sgd", "cma"]:
+        if optimizer not in ["sgd", "cma"]:
             fun_eval.append(int(partial_results[2].nfev))
 
         # build new hamiltonian using trained VQE
-        if b != args.nboost - 1:
+        if b != nboost - 1:
             new_hamiltonian_matrix = rotate_h_with_vqe(hamiltonian=ham, vqe=vqe)
             new_hamiltonian = hamiltonians.Hamiltonian(
-                args.nqubits, matrix=new_hamiltonian_matrix
+                nqubits, matrix=new_hamiltonian_matrix
             )
             hamiltonians_history.extend(new_hamiltonian_matrix)
             # Initialize DBI
@@ -142,7 +229,7 @@ def main(args):
                 dbi_d_matrix,
                 dbi_operators,
             ) = apply_dbi_steps(
-                dbi=dbi, nsteps=args.dbi_steps, optimize_step=args.optimize_dbi_step
+                dbi=dbi, nsteps=dbi_steps, optimize_step=optimize_dbi_step
             )
             # Update the circuit appending the DBI generator
             # and the old circuit with non trainable circuit
@@ -158,7 +245,7 @@ def main(args):
             # is implemented as V*H*V_dagger
             circ = circ0.copy(deep=True)
             for gate in reversed([old_circ_matrix] + dbi_operators):
-                circ.add(gates.Unitary(gate, *range(circ.nqubits), trainable=False))
+                circ.add(gates.Unitary(gate, *range(nqubits), trainable=False))
             hamiltonians_history.extend(dbi_hamiltonians)
             # append dbi results
             dbi_fluctuations.insert(0, fluctuations_h0)
@@ -171,14 +258,13 @@ def main(args):
             circ.set_parameters(initial_parameters)
 
             # reduce the learning rate after DBI has been applied
-            if "learning_rate" in opt_options:
-                opt_options["learning_rate"] *= args.decay_rate_lr
+            if "learning_rate" in optimizer_options:
+                optimizer_options["learning_rate"] *= decay_rate_lr
 
     best_loss = min(np.min(array) for array in loss_history.values())
 
     opt_results = partial_results[2]
     # save final results
-    output_dict = vars(args)
     output_dict.update(
         {
             "true_ground_energy": target_energy,
@@ -189,7 +275,7 @@ def main(args):
         }
     )
     # this works only with scipy.optimize.minimize
-    if args.optimizer not in ["sgd", "cma"]:
+    if optimizer not in ["sgd", "cma"]:
         output_dict.update(
             {
                 "best_loss": float(opt_results.fun),
@@ -210,7 +296,7 @@ def main(args):
         path / FLUCTUATION_FILE,
         **{json.dumps(key): np.array(value) for key, value in fluctuations.items()},
     )
-    if args.store_h:
+    if store_h:
         np.savez(path / HAMILTONIAN_FILE, *hamiltonians_history)
     np.savez(
         path / DBI_ENERGIES,
@@ -233,105 +319,11 @@ def main(args):
     )
 
     logging.info("Dump the results")
+    print(params_history)
+    print(output_dict)
     results_dump(path, params_history, output_dict)
     plot_loss(
         path=path,
         title="Energy history",
     )
     plot_gradients(path=path, title="Grads history")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="VQE with DBI training hyper-parameters."
-    )
-    parser.add_argument("--backend", default="qibojit", type=str, help="Qibo backend")
-    parser.add_argument(
-        "--platform", default=None, type=str, help="Qibo platform (used to run on GPU)"
-    )
-    parser.add_argument(
-        "--nthreads", default=1, type=int, help="Number of threads used by the script."
-    )
-    parser.add_argument(
-        "--optimizer", default="Powell", type=str, help="Optimizer used by VQE"
-    )
-    parser.add_argument(
-        "--optimizer_options",
-        type=str,
-        help="Options to customize the optimizer training",
-    )
-    parser.add_argument(
-        "--tol", default=TOL, type=float, help="Absolute precision to stop VQE training"
-    )
-    parser.add_argument(
-        "--decay_rate_lr",
-        default=1.0,
-        type=float,
-        help="Decay factor of the learning rate if sgd is used",
-    )
-    parser.add_argument(
-        "--nqubits", default=6, type=int, help="Number of qubits for Hamiltonian / VQE"
-    )
-    parser.add_argument(
-        "--nlayers", default=1, type=int, help="Number of layers for VQE"
-    )
-    parser.add_argument(
-        "--output_folder",
-        default=None,
-        type=str,
-        help="Folder where data will be stored",
-    )
-    parser.add_argument(
-        "--nboost",
-        type=int,
-        default=1,
-        help="Number of times the DBI is used in the new optimization routine. If 1, no optimization is run.",
-    )
-    parser.add_argument(
-        "--boost_frequency",
-        type=int,
-        default=10,
-        help="Number of optimization steps which separate two DBI boosting calls.",
-    )
-    parser.add_argument(
-        "--dbi_steps",
-        type=int,
-        default=1,
-        help="Number of DBI iterations every time the DBI is called.",
-    )
-    parser.add_argument("--stepsize", type=float, default=0.01, help="DBI step size.")
-    parser.add_argument(
-        "--optimize_dbi_step",
-        type=bool,
-        default=False,
-        help="Set to True to hyperoptimize the DBI step size.",
-    )
-    parser.add_argument(
-        "--store_h",
-        action=argparse.BooleanOptionalAction,
-        help="H is stored for each iteration",
-    )
-    parser.add_argument(
-        "--hamiltonian",
-        type=str,
-        default="XXZ",
-        help="Hamiltonian available in qibo.hamiltonians.",
-    )
-    parser.add_argument(
-        "--seed",
-        type=str,
-        default=SEED,
-        help="Random seed",
-    )
-    parser.add_argument(
-        "--nshots",
-        type=int,
-        help="number of shots",
-    )
-    parser.add_argument(
-        "--ansatz",
-        type=str,
-        help="Parametric quantum circuit ansatz. It can be hw_preserving or hdw_efficient",
-    )
-    args = parser.parse_args()
-    main(args)
diff --git a/src/boostvqe/plotscripts.py b/src/boostvqe/plotscripts.py
index 792a8b1..2fc57b1 100644
--- a/src/boostvqe/plotscripts.py
+++ b/src/boostvqe/plotscripts.py
@@ -132,6 +132,7 @@ def plot_loss(
     )
     plt.xlabel("Iterations")
     plt.ylabel("Loss")
+    print("DDDDDDDDDD", config)
     plt.title(
         rf'$N_{{\rm qubits}}={config["nqubits"]}, \, N_{{\rm layers}}={config["nlayers"]}, \, \mathrm{{{config["hamiltonian"]}}}$'
     )
@@ -505,4 +506,3 @@ def plot_lr_analysis(
     plt.legend(loc=3)
     if save:
         plt.savefig(f"lr_grads_{title}.pdf", bbox_inches="tight")
-
diff --git a/src/boostvqe/training_utils.py b/src/boostvqe/training_utils.py
index 115177a..1654100 100644
--- a/src/boostvqe/training_utils.py
+++ b/src/boostvqe/training_utils.py
@@ -2,7 +2,7 @@
 import os
 from enum import Enum
 
-from qibo.hamiltonians.models import HamiltonianTerm, multikron
+from qibo.hamiltonians.models import HamiltonianTerm, _multikron
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
@@ -29,7 +29,7 @@ class Model(Enum):
     XXZ = lambda nqubits: hamiltonians.XXZ(
         nqubits=nqubits, delta=DEFAULT_DELTA, dense=False
     )
-    XYZ = lambda nqubits: XYZ(nqubits=nqubits, delta=[0.5, 0.5], dense=False)
+    XYZ = lambda nqubits: XYZ(nqubits=nqubits, deltas=[0.5, 0.5], dense=False)
     TFIM = lambda nqubits: hamiltonians.TFIM(nqubits=nqubits, h=nqubits, dense=False)
     TLFIM = lambda nqubits: TLFIM(nqubits=nqubits, h=TLFIM_h, dense=False)
     J1J2 = lambda nqubits: J1J2(nqubits=nqubits, h=J1J2_h, dense=False)
@@ -61,9 +61,9 @@ def TLFIM(nqubits, h=TLFIM_h, dense=True, backend=None):
         return Hamiltonian(nqubits, ham, backend=backend)
 
     matrix = -(
-        multikron([matrices.Z, matrices.Z])
-        + h[0] * multikron([matrices.X, matrices.I])
-        + h[1] * multikron([matrices.Z, matrices.I])
+        _multikron([matrices.Z, matrices.Z])
+        + h[0] * _multikron([matrices.X, matrices.I])
+        + h[1] * _multikron([matrices.Z, matrices.I])
     )
     terms = [HamiltonianTerm(matrix, i, i + 1) for i in range(nqubits - 1)]
     terms.append(HamiltonianTerm(matrix, nqubits - 1, 0))
@@ -89,9 +89,9 @@ def J1J2(nqubits, h=J1J2_h, dense=True, backend=None):
         matrix = h[0] * (hx + hy + hz) + h[1] * (hx2 + hy2 + hz2)
         return Hamiltonian(nqubits, matrix, backend=backend)
 
-    hx = multikron([matrices.X, matrices.X])
-    hy = multikron([matrices.Y, matrices.Y])
-    hz = multikron([matrices.Z, matrices.Z])
+    hx = _multikron([matrices.X, matrices.X])
+    hy = _multikron([matrices.Y, matrices.Y])
+    hz = _multikron([matrices.Z, matrices.Z])
     matrix_1 = h[0] * (hx + hy + hz)
     matrix_2 = h[1] * (hx + hy + hz)
     terms = [HamiltonianTerm(matrix_1, i, i + 1) for i in range(nqubits - 1)]
@@ -128,9 +128,9 @@ def XYZ(nqubits, deltas=[0.5, 0.5], dense=True, backend=None):
         matrix = hx + deltas[0] * hy + deltas[1] * hz
         return Hamiltonian(nqubits, matrix, backend=backend)
 
-    hx = multikron([matrices.X, matrices.X])
-    hy = multikron([matrices.Y, matrices.Y])
-    hz = multikron([matrices.Z, matrices.Z])
+    hx = _multikron([matrices.X, matrices.X])
+    hy = _multikron([matrices.Y, matrices.Y])
+    hz = _multikron([matrices.Z, matrices.Z])
     matrix = (hx + deltas[0] * hy) + deltas[1] * hz
     terms = [HamiltonianTerm(matrix, i, i + 1) for i in range(nqubits - 1)]
     terms.append(HamiltonianTerm(matrix, nqubits - 1, 0))
diff --git a/src/boostvqe/utils.py b/src/boostvqe/utils.py
index 673bb0a..1966b85 100644
--- a/src/boostvqe/utils.py
+++ b/src/boostvqe/utils.py
@@ -36,13 +36,19 @@
 logging.basicConfig(level=logging.INFO)
 
 
-def generate_path(args) -> str:
+def generate_path(
+    output_folder,
+    optimizer,
+    nqubits,
+    seed,
+    decay_rate_lr,
+) -> str:
     """Generate path according to job parameters"""
-    if args.output_folder is None:
+    if output_folder is None:
         output_folder = "results"
     else:
-        output_folder = args.output_folder
-    return f"./{output_folder}/{args.optimizer}_{args.nqubits}q_{args.nlayers}l_{args.seed}s_{args.decay_rate_lr}decay"
+        output_folder = output_folder
+    return f"./{output_folder}/{optimizer}_{nqubits}q_{seed}s_{decay_rate_lr}decay"
 
 
 def create_folder(path: str) -> Path:

From 401d1aae6e5ef443ddb7cf2fbef75e3a03774cd0 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 10:05:22 +0400
Subject: [PATCH 02/24] expose DBI type

---
 main.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/main.py b/main.py
index f7f2761..7dc5f9b 100644
--- a/main.py
+++ b/main.py
@@ -58,6 +58,7 @@ def main(
     seed: int = SEED,
     nshots: int = NSHOTS,
     nlayers: int = 0,
+    mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.single_commutator,
 ):
     """
     Args:
@@ -120,7 +121,8 @@ def main(
     output_dict = locals()
     del output_dict["circuit"]
     output_dict["nqubits"] = circuit.nqubits
-    output_dict["nlayers"] = nlayers  # TODO: maybe remove it
+    # output_dict["nlayers"] = nlayers  # TODO: maybe remove it
+    output_dict["mode"] = str(mode)
     # set backend and number of classical threads
 
     if platform is not None:
@@ -213,7 +215,7 @@ def main(
             # Initialize DBI
             dbi = DoubleBracketIteration(
                 hamiltonian=new_hamiltonian,
-                mode=DoubleBracketGeneratorType.single_commutator,
+                mode=mode,
             )
 
             zero_state_t = np.transpose([zero_state])

From 3290e090ea4931be1e88d30df459909e2659f149 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 10:32:42 +0400
Subject: [PATCH 03/24] use nlayers

---
 main.py               | 4 ++++
 src/boostvqe/utils.py | 3 ++-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/main.py b/main.py
index 7dc5f9b..5865b56 100644
--- a/main.py
+++ b/main.py
@@ -116,6 +116,9 @@ def main(
 
         nlayers (int):
             Number of circuit layers.
+
+        mode:
+            Define the DBI Generator.
     """
 
     output_dict = locals()
@@ -142,6 +145,7 @@ def main(
                 nqubits,
                 seed,
                 decay_rate_lr,
+                nlayers,
             )
         )
     )
diff --git a/src/boostvqe/utils.py b/src/boostvqe/utils.py
index 1966b85..497edb8 100644
--- a/src/boostvqe/utils.py
+++ b/src/boostvqe/utils.py
@@ -42,13 +42,14 @@ def generate_path(
     nqubits,
     seed,
     decay_rate_lr,
+    nlayers,
 ) -> str:
     """Generate path according to job parameters"""
     if output_folder is None:
         output_folder = "results"
     else:
         output_folder = output_folder
-    return f"./{output_folder}/{optimizer}_{nqubits}q_{seed}s_{decay_rate_lr}decay"
+    return f"./{output_folder}/{optimizer}_{nqubits}q_{nlayers}l_{seed}s_{decay_rate_lr}decay"
 
 
 def create_folder(path: str) -> Path:

From 94c14206127a4c978d93a3b9096641c0ac7f0ab6 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 10:46:06 +0400
Subject: [PATCH 04/24] move main.py in src/boostvqe/boost.py and update the
 README

---
 README.md                        | 18 ++++++++++++++----
 main.py => src/boostvqe/boost.py |  0
 2 files changed, 14 insertions(+), 4 deletions(-)
 rename main.py => src/boostvqe/boost.py (100%)

diff --git a/README.md b/README.md
index c0c4827..1a90ce8 100644
--- a/README.md
+++ b/README.md
@@ -24,12 +24,22 @@ The source code is located in `./src/boostvqe/.` and its composed of:
 * `plotscripts.py`: plotting functions.
 * `compiling_XXZ.py`: compilation for XXZ model.
 
-## How to run the code
+## Example
 
-For further information about the inputs:
+It follows a python snippets explaining how to run the boosting
 
-```sh
-python main.py --help
+```py
+
+from boostvqe.boost import main
+from boostvqe.ansatze import hdw_efficient
+
+from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType
+
+help(main) #If you need some info
+
+circuit = hdw_efficient(nqubits=2, nlayers=2)
+
+main(circuit, "test_output", mode = DoubleBracketGeneratorType.group_commutator)
 ```
 
 # Tutorials
diff --git a/main.py b/src/boostvqe/boost.py
similarity index 100%
rename from main.py
rename to src/boostvqe/boost.py

From 8bd5c9179d88209a1e237667edd52c034c0adbae Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 10:47:04 +0400
Subject: [PATCH 05/24] typo in README

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1a90ce8..ff61ba0 100644
--- a/README.md
+++ b/README.md
@@ -26,7 +26,7 @@ The source code is located in `./src/boostvqe/.` and its composed of:
 
 ## Example
 
-It follows a python snippets explaining how to run the boosting
+It follows a python snippet explaining how to run the boosting
 
 ```py
 

From 8ea85f02b5375770c9cc821c804d7f795f686f3a Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 10:50:25 +0400
Subject: [PATCH 06/24] clean code

---
 src/boostvqe/boost.py       | 2 --
 src/boostvqe/plotscripts.py | 1 -
 2 files changed, 3 deletions(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index 5865b56..a65d91f 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -325,8 +325,6 @@ def main(
     )
 
     logging.info("Dump the results")
-    print(params_history)
-    print(output_dict)
     results_dump(path, params_history, output_dict)
     plot_loss(
         path=path,
diff --git a/src/boostvqe/plotscripts.py b/src/boostvqe/plotscripts.py
index 2fc57b1..af9b953 100644
--- a/src/boostvqe/plotscripts.py
+++ b/src/boostvqe/plotscripts.py
@@ -132,7 +132,6 @@ def plot_loss(
     )
     plt.xlabel("Iterations")
     plt.ylabel("Loss")
-    print("DDDDDDDDDD", config)
     plt.title(
         rf'$N_{{\rm qubits}}={config["nqubits"]}, \, N_{{\rm layers}}={config["nlayers"]}, \, \mathrm{{{config["hamiltonian"]}}}$'
     )

From 4ab779c6bc3eb8fb6e792407ee71435c6ce80e4e Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 11:13:33 +0400
Subject: [PATCH 07/24] README installation instructions

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index ff61ba0..418cf84 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@ Boosting variational eigenstate preparation algorithms limited by training and n
 The package can be installed by source after cloning the repository:
 
 ```sh
+cd boostvqe
 pip install -e .
 ```
 

From ed50b141a45d55925db1349ed896e693f34818e9 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 11:14:12 +0400
Subject: [PATCH 08/24] README installation instructions

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 418cf84..0e49c2f 100644
--- a/README.md
+++ b/README.md
@@ -12,8 +12,6 @@ cd boostvqe
 pip install -e .
 ```
 
-will install `boostvqe 0.0.1` and activate a dedicated working shell.
-
 ## Code structure
 
 The file `main.py` performs boosted VQE training.

From 5744c7ad05884cc64902a2ca299a2afb4af883a1 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 11:31:04 +0400
Subject: [PATCH 09/24] update pyproject

---
 poetry.lock    | 2264 +++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |    8 +-
 2 files changed, 2268 insertions(+), 4 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index c6a9cc2..64bb7cb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,45 @@
-# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "absl-py"
+version = "2.1.0"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
+    {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
+]
+
+[[package]]
+name = "alembic"
+version = "1.13.3"
+description = "A database migration tool for SQLAlchemy."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "alembic-1.13.3-py3-none-any.whl", hash = "sha256:908e905976d15235fae59c9ac42c4c5b75cfcefe3d27c0fbf7ae15a37715d80e"},
+    {file = "alembic-1.13.3.tar.gz", hash = "sha256:203503117415561e203aa14541740643a611f641517f0209fcae63e9fa09f1a2"},
+]
+
+[package.dependencies]
+Mako = "*"
+SQLAlchemy = ">=1.3.0"
+typing-extensions = ">=4"
+
+[package.extras]
+tz = ["backports.zoneinfo"]
+
+[[package]]
+name = "antlr4-python3-runtime"
+version = "4.13.2"
+description = "ANTLR 4.13.2 runtime for Python 3"
+optional = false
+python-versions = "*"
+files = [
+    {file = "antlr4_python3_runtime-4.13.2-py3-none-any.whl", hash = "sha256:fe3835eb8d33daece0e799090eda89719dbccee7aa39ef94eed3818cafa5a7e8"},
+    {file = "antlr4_python3_runtime-4.13.2.tar.gz", hash = "sha256:909b647e1d2fc2b70180ac586df3933e38919c85f98ccc656a96cd3f25ef3916"},
+]
 
 [[package]]
 name = "appnope"
@@ -11,6 +52,21 @@ files = [
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+description = "An AST unparser for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
+    {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
+]
+
+[package.dependencies]
+six = ">=1.6.1,<2.0"
+wheel = ">=0.23.0,<1.0"
+
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -41,6 +97,160 @@ files = [
     {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
 ]
 
+[[package]]
+name = "certifi"
+version = "2024.8.30"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.0"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "charset_normalizer-3.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4f9fc98dad6c2eaa32fc3af1417d95b5e3d08aff968df0cd320066def971f9a6"},
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+
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 version = "0.7.0"
@@ -422,6 +1976,17 @@ files = [
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+python-versions = "*"
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 version = "3.0.4"
@@ -468,6 +2033,20 @@ files = [
 plugins = ["importlib-metadata"]
 windows-terminal = ["colorama (>=0.4.6)"]
 
+[[package]]
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+
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 version = "2.1"
@@ -532,6 +2111,244 @@ aspect = ["aspectlib"]
 elasticsearch = ["elasticsearch"]
 histogram = ["pygal", "pygaljs"]
 
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@@ -559,6 +2648,26 @@ files = [
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@@ -574,6 +2683,45 @@ files = [
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@@ -585,6 +2733,37 @@ files = [
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 [[package]]
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@@ -602,6 +2781,85 @@ attrs = "*"
 [package.extras]
 test = ["pytest"]
 
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+    {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"},
+]
+
 [[package]]
 name = "zipp"
 version = "3.17.0"
@@ -619,5 +2877,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.9"
-content-hash = "56730428c706162904c9068f90c9b53c44a6a931c33cae18b58a9cb3e2120b71"
+python-versions = ">=3.9,<3.13"
+content-hash = "0386d4993a9189caecdb427f313a9bdf529f03ab375f3b14461ce74f960742fd"
diff --git a/pyproject.toml b/pyproject.toml
index 69b843b..e2064b4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,9 +11,15 @@ readme = "README.md"
 packages = [{ include = "boostvqe", from = "src" }]
 
 [tool.poetry.dependencies]
-python = "^3.9"
+python = ">=3.9,<3.13"
 numpy = "^1.26.4"
 numba = "^0.59.0"
+qibo = "^0.2.12"
+tensorflow = "^2.17.0"
+matplotlib = "^3.9.2"
+seaborn = "^0.13.2"
+hyperopt = "^0.2.7"
+qibojit = "^0.1.6"
 
 [tool.poetry.group.dev.dependencies]
 ipython = "^7.34"

From 9c54a486d95d1ace7b4ed0985e12cc87986818de Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 12:09:51 +0400
Subject: [PATCH 10/24] remove help from the README script

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 0e49c2f..977494b 100644
--- a/README.md
+++ b/README.md
@@ -34,13 +34,13 @@ from boostvqe.ansatze import hdw_efficient
 
 from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType
 
-help(main) #If you need some info
-
 circuit = hdw_efficient(nqubits=2, nlayers=2)
 
 main(circuit, "test_output", mode = DoubleBracketGeneratorType.group_commutator)
 ```
 
+All the info regarding `main`, can be generated with `help(main)`.
+
 # Tutorials
 
 Some useful notebooks to understand how the library works, are collected [here](notebooks/notebooks_links.md).

From e9b12b19ff05f68ab78f4838dd2878d495b65a14 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 12:36:23 +0400
Subject: [PATCH 11/24] rename main function dbi_vqe

---
 README.md             | 8 ++++----
 src/boostvqe/boost.py | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 977494b..e685d4a 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ pip install -e .
 
 ## Code structure
 
-The file `main.py` performs boosted VQE training.
+The file `src/boostvqe/boost.py` performs boosted VQE training.
 
 The source code is located in `./src/boostvqe/.` and its composed of:
 
@@ -29,17 +29,17 @@ It follows a python snippet explaining how to run the boosting
 
 ```py
 
-from boostvqe.boost import main
+from boostvqe.boost import dbqa_vqe
 from boostvqe.ansatze import hdw_efficient
 
 from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType
 
 circuit = hdw_efficient(nqubits=2, nlayers=2)
 
-main(circuit, "test_output", mode = DoubleBracketGeneratorType.group_commutator)
+dbqa_vqe(circuit, "test_output", mode = DoubleBracketGeneratorType.group_commutator)
 ```
 
-All the info regarding `main`, can be generated with `help(main)`.
+All the info regarding `dbqa_vqe` can be generated with `help(dbqa_vqe)`.
 
 # Tutorials
 
diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index a65d91f..f5f070a 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -40,7 +40,7 @@
 NSHOTS = 100
 
 
-def main(
+def dbqa_vqe(
     circuit: Circuit,  # in place of ansatz
     output_folder: str,
     backend: Optional[str] = "qibojit",

From f70b447ff5115d6ea88d6755b998dfce5b986e87 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 12:37:00 +0400
Subject: [PATCH 12/24] remove -e option in README

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e685d4a..aa031a2 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ The package can be installed by source after cloning the repository:
 
 ```sh
 cd boostvqe
-pip install -e .
+pip install .
 ```
 
 ## Code structure

From 18f97dd22da15648e2cb2a51821d7e1e16576903 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 12:38:27 +0400
Subject: [PATCH 13/24] increase example readibility

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index aa031a2..e23485f 100644
--- a/README.md
+++ b/README.md
@@ -35,8 +35,8 @@ from boostvqe.ansatze import hdw_efficient
 from qibo.models.dbi.double_bracket import DoubleBracketGeneratorType
 
 circuit = hdw_efficient(nqubits=2, nlayers=2)
-
-dbqa_vqe(circuit, "test_output", mode = DoubleBracketGeneratorType.group_commutator)
+output_folder = "output"
+dbqa_vqe(circuit, output_folder, mode = DoubleBracketGeneratorType.group_commutator)
 ```
 
 All the info regarding `dbqa_vqe` can be generated with `help(dbqa_vqe)`.

From 79cf18be12b0dab734e247973c5f5da9a89bd2fd Mon Sep 17 00:00:00 2001
From: Edoardo Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 16 Oct 2024 12:39:08 +0400
Subject: [PATCH 14/24] Update src/boostvqe/boost.py

Co-authored-by: Andrea Pasquale <andreapasquale97@gmail.com>
---
 src/boostvqe/boost.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index f5f070a..41533c7 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -150,7 +150,6 @@ def dbqa_vqe(
         )
     )
     ham = getattr(Model, hamiltonian)(nqubits)  # TODO : use only Model and not str
-    # ham = hamiltonian(nqubits)
     target_energy = np.real(np.min(np.asarray(ham.eigenvalues())))
 
     # construct circuit from parsed ansatz name

From c5815836d58ec60c49faf09314ee3cb15542140b Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Tue, 22 Oct 2024 17:18:34 +0400
Subject: [PATCH 15/24] fix: remove optimize_dbi_step

---
 src/boostvqe/boost.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index f5f070a..731225f 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -52,7 +52,6 @@ def dbqa_vqe(
     nboost: int = 1,
     boost_frequency: int = 10,
     dbi_steps: int = 1,
-    optimize_dbi_step: bool = False,
     store_h: bool = False,
     hamiltonian: str = "XXZ",
     seed: int = SEED,
@@ -99,9 +98,6 @@ def dbqa_vqe(
         stepsize (float, default: 0.01):
             Step size used during DBI updates.
 
-        optimize_dbi_step (bool, default: False):
-            If set to True, the DBI step size is hyperoptimized.
-
         store_h (bool, default: False):
             If this flag is set, the Hamiltonian `H` is stored at each iteration.
 
@@ -234,9 +230,7 @@ def dbqa_vqe(
                 dbi_steps,
                 dbi_d_matrix,
                 dbi_operators,
-            ) = apply_dbi_steps(
-                dbi=dbi, nsteps=dbi_steps, optimize_step=optimize_dbi_step
-            )
+            ) = apply_dbi_steps(dbi=dbi, nsteps=dbi_steps)
             # Update the circuit appending the DBI generator
             # and the old circuit with non trainable circuit
             dbi_operators = [

From 8c0db957e78c0be21019a1be4c17284529a05231 Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Tue, 5 Nov 2024 12:28:58 +0100
Subject: [PATCH 16/24] feat: some fixes to Edo's improvments

---
 src/boostvqe/ansatze.py        | 36 ++++++++++++++++++----------------
 src/boostvqe/boost.py          |  2 --
 src/boostvqe/plotscripts.py    |  2 +-
 src/boostvqe/training_utils.py |  3 ++-
 4 files changed, 22 insertions(+), 21 deletions(-)

diff --git a/src/boostvqe/ansatze.py b/src/boostvqe/ansatze.py
index 20b2b4c..2867ad0 100644
--- a/src/boostvqe/ansatze.py
+++ b/src/boostvqe/ansatze.py
@@ -5,14 +5,16 @@
 
 from boostvqe.training_utils import vqe_loss
 
+
 def connect_qubits(circuit, jumpsize=1, start_from=0):
     def get_circular_index(n, index):
         circular_index = index % n
         return circular_index
-    for q in range(start_from, circuit.nqubits, jumpsize+1):
+
+    for q in range(start_from, circuit.nqubits, jumpsize + 1):
         ctrl_index = q
-        targ_index = get_circular_index(circuit.nqubits, q+jumpsize)
-        circuit.add(gates.RBS(q0=ctrl_index, q1=targ_index, theta=0.))
+        targ_index = get_circular_index(circuit.nqubits, q + jumpsize)
+        circuit.add(gates.RBS(q0=ctrl_index, q1=targ_index, theta=0.0))
     return circuit
 
 
@@ -31,18 +33,16 @@ def hdw_efficient(nqubits, nlayers):
     circuit.add(gates.RY(q, theta=0) for q in range(nqubits))
 
     return circuit
-            
 
-def hw_preserving(nqubits, nlayers=1):
 
-    if nqubits%2 != 0:
+def hw_preserving(nqubits, nlayers=1):
+    if nqubits % 2 != 0:
         raise_error(
-            ValueError,
-            "To use this ansatz please be sure number of qubits is even."
-    )
+            ValueError, "To use this ansatz please be sure number of qubits is even."
+        )
     c = Circuit(nqubits)
 
-    for q in range(int(nqubits/2)):
+    for q in range(int(nqubits / 2)):
         c.add(gates.X(q))
 
     for _ in range(int(nlayers)):
@@ -54,20 +54,22 @@ def hw_preserving(nqubits, nlayers=1):
 
     return c
 
+
 def su2_preserving(nqubits, nlayers):
     """SU2 invariant circuit."""
     c = Circuit(nqubits)
     for _ in range(nlayers):
         for q in range(1, nqubits, 2):
-            c.add(gates.RXX(q0=q, q1=(q+1)%nqubits, theta=0.))
-            c.add(gates.RYY(q0=q, q1=(q+1)%nqubits, theta=0.))
-            c.add(gates.RZZ(q0=q, q1=(q+1)%nqubits, theta=0.))
+            c.add(gates.RXX(q0=q, q1=(q + 1) % nqubits, theta=0.0))
+            c.add(gates.RYY(q0=q, q1=(q + 1) % nqubits, theta=0.0))
+            c.add(gates.RZZ(q0=q, q1=(q + 1) % nqubits, theta=0.0))
         for q in range(0, nqubits, 2):
-            c.add(gates.RXX(q0=q, q1=(q+1)%nqubits, theta=0.))
-            c.add(gates.RYY(q0=q, q1=(q+1)%nqubits, theta=0.))
-            c.add(gates.RZZ(q0=q, q1=(q+1)%nqubits, theta=0.))
+            c.add(gates.RXX(q0=q, q1=(q + 1) % nqubits, theta=0.0))
+            c.add(gates.RYY(q0=q, q1=(q + 1) % nqubits, theta=0.0))
+            c.add(gates.RZZ(q0=q, q1=(q + 1) % nqubits, theta=0.0))
     return c
 
+
 def compute_gradients(parameters, circuit, hamiltonian):
     """
     Compute gradients of circuit's parameters to check the problem trainability.
@@ -75,7 +77,7 @@ def compute_gradients(parameters, circuit, hamiltonian):
     over the final state get running `circuit.execute` w.r.t. rotational angles.
 
     """
-    tf_backend = construct_backend("tensorflow")
+    tf_backend = construct_backend(backend="qiboml", platform="tensorflow")
     parameters = tf_backend.tf.Variable(parameters, dtype=tf_backend.tf.float64)
 
     with tf_backend.tf.GradientTape() as tape:
diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index 9185ca8..6f05b58 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -9,7 +9,6 @@
 
 # qibo's
 from qibo import Circuit, gates, hamiltonians, set_backend
-from qibo.backends import GlobalBackend
 from qibo.models.dbi.double_bracket import (
     DoubleBracketGeneratorType,
     DoubleBracketIteration,
@@ -128,7 +127,6 @@ def dbqa_vqe(
         set_backend(backend=backend, platform=platform)
     else:
         set_backend(backend=backend)
-        platform = GlobalBackend().platform
 
     nqubits = circuit.nqubits
     # setup the results folder
diff --git a/src/boostvqe/plotscripts.py b/src/boostvqe/plotscripts.py
index af9b953..9057038 100644
--- a/src/boostvqe/plotscripts.py
+++ b/src/boostvqe/plotscripts.py
@@ -65,7 +65,7 @@ def plot_loss(
     plt.figure(figsize=(10 * width, 10 * width * 6 / 8))
     plt.title(title)
 
-    for i in range(config["nboost"]):
+    for i in range(config["nboost"] + 1):
         start = (
             0
             if str(i - 1) not in loss_vqe
diff --git a/src/boostvqe/training_utils.py b/src/boostvqe/training_utils.py
index 1654100..574ac32 100644
--- a/src/boostvqe/training_utils.py
+++ b/src/boostvqe/training_utils.py
@@ -12,11 +12,12 @@
 tf.get_logger().setLevel("ERROR")
 
 from qibo import gates, hamiltonians
-from qibo.backends import GlobalBackend, NumpyBackend, TensorflowBackend, matrices
+from qibo.backends import NumpyBackend, matrices
 from qibo.config import raise_error
 from qibo.hamiltonians import AbstractHamiltonian, Hamiltonian, SymbolicHamiltonian
 from qibo.hamiltonians.models import _build_spin_model
 from qibo.symbols import Z
+from qiboml.backends import TensorflowBackend
 
 DEFAULT_DELTA = 0.5
 """Default `delta` value of XXZ Hamiltonian"""

From c1685581b39642fdd86fb10cf86a6c34dddc1fef Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Tue, 5 Nov 2024 12:29:33 +0100
Subject: [PATCH 17/24] feat: add script to run vqe training

---
 train_vqes.py | 113 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 113 insertions(+)
 create mode 100644 train_vqes.py

diff --git a/train_vqes.py b/train_vqes.py
new file mode 100644
index 0000000..6e0c9c1
--- /dev/null
+++ b/train_vqes.py
@@ -0,0 +1,113 @@
+import argparse
+import json
+import logging
+
+from boostvqe import ansatze
+from boostvqe.boost import dbqa_vqe
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run DBQA VQE optimization.")
+
+    # Required arguments
+    parser.add_argument("--output_folder", type=str, help="Output folder for results.")
+    parser.add_argument("--circuit_ansatz", type=str, help="Circuit ansatz.")
+    parser.add_argument(
+        "--nqubits",
+        type=int,
+        help="Number of particles of the problem. It will fix the number of qubits.",
+    )
+    parser.add_argument(
+        "--nlayers", type=int, help="Number of layers of the VQE ansatz."
+    )
+
+    # Optional arguments with defaults matching the function signature
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="numpy",
+        help="Quantum backend to use (default: numpy).",
+    )
+    parser.add_argument(
+        "--platform",
+        type=str,
+        default=None,
+        help="Platform for backend (e.g., 'CUDA').",
+    )
+    parser.add_argument(
+        "--optimizer",
+        type=str,
+        default="Powell",
+        help="Optimizer for VQE (default: Powell).",
+    )
+    parser.add_argument(
+        "--optimizer_options",
+        type=json.loads,
+        default={},
+        help="Optimizer options as a JSON string.",
+    )
+    parser.add_argument(
+        "--tol",
+        type=float,
+        default=1e-7,
+        help="Tolerance for optimization convergence.",
+    )
+    parser.add_argument(
+        "--decay_rate_lr", type=float, default=1.0, help="Decay rate for learning rate."
+    )
+    parser.add_argument(
+        "--nboost", type=int, default=0, help="Number of boost iterations."
+    )
+    parser.add_argument(
+        "--boost_frequency", type=int, default=10, help="Frequency of boosting calls."
+    )
+    parser.add_argument(
+        "--dbi_steps", type=int, default=1, help="Number of DBI iterations."
+    )
+    parser.add_argument(
+        "--store_h", action="store_true", help="Store Hamiltonian at each iteration."
+    )
+    parser.add_argument(
+        "--hamiltonian",
+        type=str,
+        default="XXZ",
+        help="Type of Hamiltonian (default: XXZ).",
+    )
+    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
+    parser.add_argument("--nshots", type=int, default=None, help="Number of shots.")
+    parser.add_argument(
+        "--mode", type=str, default="single_commutator", help="DBI generator mode."
+    )
+
+    args = parser.parse_args()
+
+    # Set up logging
+    logging.basicConfig(level=logging.INFO)
+
+    # Load or construct the circuit from the given file path
+    circuit = getattr(ansatze, args.circuit_ansatz)(args.nqubits, args.nlayers)
+
+    # Run the DBQA VQE function
+    dbqa_vqe(
+        circuit=circuit,
+        output_folder=args.output_folder,
+        backend=args.backend,
+        platform=args.platform,
+        optimizer=args.optimizer,
+        optimizer_options=args.optimizer_options,
+        tol=args.tol,
+        decay_rate_lr=args.decay_rate_lr,
+        nboost=args.nboost,
+        boost_frequency=args.boost_frequency,
+        dbi_steps=args.dbi_steps,
+        store_h=args.store_h,
+        hamiltonian=args.hamiltonian,
+        seed=args.seed,
+        nshots=args.nshots,
+        nlayers=args.nlayers,
+        mode=args.mode,
+    )
+
+
+if __name__ == "__main__":
+    main()

From ad3f3980623b3a606aa44331e98e99989b08bd14 Mon Sep 17 00:00:00 2001
From: MatteoRobbiati <robbiati.matteo@gmail.com>
Date: Wed, 6 Nov 2024 14:20:24 +0100
Subject: [PATCH 18/24] fix: RBS derivative

---
 src/boostvqe/training_utils.py | 40 ++++++++++++++++++++++++++++++++--
 train_vqes.py                  | 14 ++++++++++++
 2 files changed, 52 insertions(+), 2 deletions(-)

diff --git a/src/boostvqe/training_utils.py b/src/boostvqe/training_utils.py
index 574ac32..cf6ec04 100644
--- a/src/boostvqe/training_utils.py
+++ b/src/boostvqe/training_utils.py
@@ -11,7 +11,7 @@
 
 tf.get_logger().setLevel("ERROR")
 
-from qibo import gates, hamiltonians
+from qibo import Circuit, gates, hamiltonians
 from qibo.backends import NumpyBackend, matrices
 from qibo.config import raise_error
 from qibo.hamiltonians import AbstractHamiltonian, Hamiltonian, SymbolicHamiltonian
@@ -256,7 +256,7 @@ def grad(upstream):
             for p in range(nparams):
                 gradients.append(
                     upstream
-                    * parameter_shift(
+                    * quantum_derivative_via_shifts(
                         circuit=circuit,
                         hamiltonian=hamiltonian,
                         parameter_index=p,
@@ -296,6 +296,7 @@ def parameter_shift(
         )
 
     gate = circuit.associate_gates_with_parameters()[parameter_index]
+
     generator_eigenval = gate.generator_eigenvalue()
 
     s = np.pi / (4 * generator_eigenval)
@@ -320,3 +321,38 @@ def parameter_shift(
 
     circuit.set_parameters(original)
     return float(generator_eigenval * (forward - backward))
+
+
+def quantum_derivative_via_shifts(
+    hamiltonian, circuit, parameter_index, exec_backend, nshots=None
+):
+    target_gate = circuit.associate_gates_with_parameters()[parameter_index]
+    target_qubits = target_gate.qubits
+    parameter_value = target_gate.parameters[0]
+
+    if target_gate.name in ["rx", "ry", "rz"]:
+        return parameter_shift(
+            hamiltonian, circuit, parameter_index, exec_backend, nshots
+        )
+    elif target_gate.name == "rbs":
+        unrolled_rbs_circuit = Circuit(circuit.nqubits)
+        for g in circuit.queue:
+            if g != target_gate:
+                unrolled_rbs_circuit.add(g)
+            else:
+                unrolled_rbs_circuit.add(gates.H(target_qubits[0]))
+                unrolled_rbs_circuit.add(gates.CNOT(target_qubits[0], target_qubits[1]))
+                unrolled_rbs_circuit.add(gates.H(target_qubits[1]))
+                unrolled_rbs_circuit.add(gates.RY(target_qubits[0], parameter_value))
+                unrolled_rbs_circuit.add(gates.RY(target_qubits[1], -parameter_value))
+                unrolled_rbs_circuit.add(gates.H(target_qubits[1]))
+                unrolled_rbs_circuit.add(gates.CNOT(target_qubits[0], target_qubits[1]))
+                unrolled_rbs_circuit.add(gates.H(target_qubits[0]))
+
+    derivative = parameter_shift(
+        hamiltonian, unrolled_rbs_circuit, parameter_index, exec_backend, nshots
+    ) - parameter_shift(
+        hamiltonian, unrolled_rbs_circuit, parameter_index + 1, exec_backend, nshots
+    )
+
+    return derivative
diff --git a/train_vqes.py b/train_vqes.py
index 6e0c9c1..a19032f 100644
--- a/train_vqes.py
+++ b/train_vqes.py
@@ -2,10 +2,24 @@
 import json
 import logging
 
+import qibo
+
 from boostvqe import ansatze
 from boostvqe.boost import dbqa_vqe
 
 
+class SpecificWarningFilter(logging.Filter):
+    def filter(self, record):
+        return (
+            "Calculating the dense form of a symbolic Hamiltonian"
+            not in record.getMessage()
+        )
+
+
+qibo_logger = qibo.config.log
+qibo_logger.addFilter(SpecificWarningFilter())
+
+
 def main():
     parser = argparse.ArgumentParser(description="Run DBQA VQE optimization.")
 

From 981f103b6b7b574e3ea774d1eb9598a6be478665 Mon Sep 17 00:00:00 2001
From: Edoardo Pedicillo <edoardo.pedicillo@gmail.com>
Date: Tue, 12 Nov 2024 21:33:47 +0400
Subject: [PATCH 19/24] Update src/boostvqe/boost.py

Co-authored-by: Matteo Robbiati <62071516+MatteoRobbiati@users.noreply.github.com>
---
 src/boostvqe/boost.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index 6f05b58..2c31aab 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -36,7 +36,7 @@
     train_vqe,
 )
 
-NSHOTS = 100
+NSHOTS = 1000
 
 
 def dbqa_vqe(

From e35f3362a36d698a5662b2d174d15a10c6eeeae6 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 13 Nov 2024 11:43:13 +0400
Subject: [PATCH 20/24] refactor: use time_steps

---
 src/boostvqe/boost.py | 9 +++++----
 src/boostvqe/utils.py | 4 ++--
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index 2c31aab..c3ab48e 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -56,6 +56,7 @@ def dbqa_vqe(
     seed: int = SEED,
     nshots: int = NSHOTS,
     nlayers: int = 0,
+    time_step: float = 0.1,
     mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.single_commutator,
 ):
     """
@@ -85,7 +86,7 @@ def dbqa_vqe(
             Learning rate decay factor used when the optimizer is SGD (stochastic gradient descent).
 
         nboost (int, default: 1):
-            Number of times DBI (Deterministic Boost Iteration) is applied in the optimization process.
+            Number of times DBI is applied in the optimization process.
             If set to 1, no boosting is performed.
 
         boost_frequency (int, default: 10):
@@ -94,8 +95,8 @@ def dbqa_vqe(
         dbi_steps (int, default: 1):
             Number of DBI iterations performed each time DBI is called.
 
-        stepsize (float, default: 0.01):
-            Step size used during DBI updates.
+        time_step (float, default: 0.01):
+            Time step used during DBI updates.
 
         store_h (bool, default: False):
             If this flag is set, the Hamiltonian `H` is stored at each iteration.
@@ -227,7 +228,7 @@ def dbqa_vqe(
                 dbi_steps,
                 dbi_d_matrix,
                 dbi_operators,
-            ) = apply_dbi_steps(dbi=dbi, nsteps=dbi_steps)
+            ) = apply_dbi_steps(dbi=dbi, nsteps=dbi_steps, time_step=time_step)
             # Update the circuit appending the DBI generator
             # and the old circuit with non trainable circuit
             dbi_operators = [
diff --git a/src/boostvqe/utils.py b/src/boostvqe/utils.py
index 497edb8..430daee 100644
--- a/src/boostvqe/utils.py
+++ b/src/boostvqe/utils.py
@@ -181,11 +181,11 @@ def rotate_h_with_vqe(hamiltonian, vqe):
     return new_hamiltonian
 
 
-def apply_dbi_steps(dbi, nsteps, d_type=None, method=None, **kwargs):
+def apply_dbi_steps(dbi, nsteps, d_type=None, method=None, time_step=0.01, **kwargs):
     """Apply `nsteps` of `dbi` to `hamiltonian`."""
     nqubits = dbi.nqubits
 
-    p0 = [0.01]
+    p0 = [time_step]
     if d_type is not None:
         if d_type == MagneticFieldEvolutionOracle:
             p0.extend([4 - np.sin(x / 3) for x in range(nqubits)])

From 52339e597758841c21741880925dce828b2ac918 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 13 Nov 2024 11:44:57 +0400
Subject: [PATCH 21/24] refactor: rename dbi_steps in dbqa_steps

---
 src/boostvqe/boost.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index c3ab48e..9b7d80f 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -50,7 +50,7 @@ def dbqa_vqe(
     decay_rate_lr: float = 1.0,
     nboost: int = 1,
     boost_frequency: int = 10,
-    dbi_steps: int = 1,
+    dbqa_steps: int = 1,
     store_h: bool = False,
     hamiltonian: str = "XXZ",
     seed: int = SEED,
@@ -92,7 +92,7 @@ def dbqa_vqe(
         boost_frequency (int, default: 10):
             Number of optimization steps between DBI boosting calls.
 
-        dbi_steps (int, default: 1):
+        dbqa_steps (int, default: 1):
             Number of DBI iterations performed each time DBI is called.
 
         time_step (float, default: 0.01):
@@ -225,10 +225,10 @@ def dbqa_vqe(
                 dbi_hamiltonians,
                 dbi_energies,
                 dbi_fluctuations,
-                dbi_steps,
+                dbqa_steps,
                 dbi_d_matrix,
                 dbi_operators,
-            ) = apply_dbi_steps(dbi=dbi, nsteps=dbi_steps, time_step=time_step)
+            ) = apply_dbi_steps(dbi=dbi, nsteps=dbqa_steps, time_step=time_step)
             # Update the circuit appending the DBI generator
             # and the old circuit with non trainable circuit
             dbi_operators = [
@@ -250,7 +250,7 @@ def dbqa_vqe(
             dbi_energies.insert(0, energy_h0)
             boost_fluctuations_dbi[b] = np.array(dbi_fluctuations)
             boost_energies[b] = np.array(dbi_energies)
-            boost_steps[b] = np.array(dbi_steps)
+            boost_steps[b] = np.array(dbqa_steps)
             boost_d_matrix[b] = np.array(dbi_d_matrix)
             initial_parameters = np.zeros(len(initial_parameters))
             circ.set_parameters(initial_parameters)

From aee638a193cad36fa919061239a0e05d23e60ab6 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 13 Nov 2024 11:46:34 +0400
Subject: [PATCH 22/24] refactor: rename time)steps in dbr_duration

---
 src/boostvqe/boost.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index 9b7d80f..866aba2 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -56,7 +56,7 @@ def dbqa_vqe(
     seed: int = SEED,
     nshots: int = NSHOTS,
     nlayers: int = 0,
-    time_step: float = 0.1,
+    dbr_duration: float = 0.1,
     mode: DoubleBracketGeneratorType = DoubleBracketGeneratorType.single_commutator,
 ):
     """
@@ -95,7 +95,7 @@ def dbqa_vqe(
         dbqa_steps (int, default: 1):
             Number of DBI iterations performed each time DBI is called.
 
-        time_step (float, default: 0.01):
+        dbr_duration (float, default: 0.01):
             Time step used during DBI updates.
 
         store_h (bool, default: False):
@@ -228,7 +228,7 @@ def dbqa_vqe(
                 dbqa_steps,
                 dbi_d_matrix,
                 dbi_operators,
-            ) = apply_dbi_steps(dbi=dbi, nsteps=dbqa_steps, time_step=time_step)
+            ) = apply_dbi_steps(dbi=dbi, nsteps=dbqa_steps, time_step=dbr_duration)
             # Update the circuit appending the DBI generator
             # and the old circuit with non trainable circuit
             dbi_operators = [

From 33050cb49b50a7961384302640bd43d4efff0700 Mon Sep 17 00:00:00 2001
From: Edoardo-Pedicillo <edoardo.pedicillo@gmail.com>
Date: Wed, 13 Nov 2024 11:47:29 +0400
Subject: [PATCH 23/24] docs: change dbqa_steps description

---
 src/boostvqe/boost.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index 866aba2..1251c56 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -93,7 +93,7 @@ def dbqa_vqe(
             Number of optimization steps between DBI boosting calls.
 
         dbqa_steps (int, default: 1):
-            Number of DBI iterations performed each time DBI is called.
+            Number of DBQA iterations performed each time DBI is called.
 
         dbr_duration (float, default: 0.01):
             Time step used during DBI updates.

From 5301e45a1c4de941ae7f617ea3bd77d4e2f5a35d Mon Sep 17 00:00:00 2001
From: Andrea Pasquale <andreapasquale97@gmail.com>
Date: Fri, 29 Nov 2024 10:53:30 +0100
Subject: [PATCH 24/24] fix: boost.py working

---
 src/boostvqe/boost.py       | 4 ++--
 src/boostvqe/plotscripts.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/boostvqe/boost.py b/src/boostvqe/boost.py
index 1251c56..ce77aa1 100644
--- a/src/boostvqe/boost.py
+++ b/src/boostvqe/boost.py
@@ -225,7 +225,7 @@ def dbqa_vqe(
                 dbi_hamiltonians,
                 dbi_energies,
                 dbi_fluctuations,
-                dbqa_steps,
+                list_dbqa_steps,
                 dbi_d_matrix,
                 dbi_operators,
             ) = apply_dbi_steps(dbi=dbi, nsteps=dbqa_steps, time_step=dbr_duration)
@@ -250,7 +250,7 @@ def dbqa_vqe(
             dbi_energies.insert(0, energy_h0)
             boost_fluctuations_dbi[b] = np.array(dbi_fluctuations)
             boost_energies[b] = np.array(dbi_energies)
-            boost_steps[b] = np.array(dbqa_steps)
+            boost_steps[b] = np.array(list_dbqa_steps)
             boost_d_matrix[b] = np.array(dbi_d_matrix)
             initial_parameters = np.zeros(len(initial_parameters))
             circ.set_parameters(initial_parameters)
diff --git a/src/boostvqe/plotscripts.py b/src/boostvqe/plotscripts.py
index 9057038..6dafde7 100644
--- a/src/boostvqe/plotscripts.py
+++ b/src/boostvqe/plotscripts.py
@@ -156,7 +156,7 @@ def plot_gradients(
     grads = dict(np.load(path / f"{GRADS_FILE + '.npz'}"))
     config = json.loads((path / OPTIMIZATION_FILE).read_text())
     ave_grads = []
-    dbi_steps = config["dbi_steps"]
+    dbi_steps = config["dbqa_steps"]
     iterations = []
     for epoch in grads:
         len_iterations = len(iterations)