qreservoir

Qreserveoir is a Python package for quantum reservoir engineering and quantum extreme learning built on top of qulacs.

Installation

The easiest way to install qreservoir is using pip:

$ pip install qreservoir

qreservoir requires Python version 3.10 or 3.11.

Modules

There are four main modules in qreservoir: encoders, reservoirs, models and datasets. Each of these gives access to a variety of classes to build and train reservoir and extreme learning models.

The general composition of a model is as follows:

  1. An encoder, which encodes the input data into a quantum state
  2. A reservoir, which evolves the quantum state to which we pass an encoder
  3. A model, which is a wrapper around reservoirs allowing for prediction and training

Models take a scikit-learn estimator as an argument, which is used to train the model and make predictions. The model also takes a list of qulacs observables as an argument, which are used to calculate the expectation values and translate the quantum dynamics model to a classical value.

Example

from qulacs import Observable
from sklearn.linear_model import LinearRegression

from qreservoir.datasets import Complex_Fourrier
from qreservoir.encoders import ExpEncoder
from qreservoir.models import QELModel
from qreservoir.reservoirs import RotationReservoir


dataset = Complex_Fourrier(complexity=1, size=1000, noise=0.0)

encoder = ExpEncoder(1, 1, 3) # 1 feature, 1 layer, 1 qubit per feature
reservoir = RotationReservoir(encoder, 0, 10)  # 0 ancilla qubits, 10 depth

observables = [Observable(3) for _ in range(9)] # create observable set
for i, ob in enumerate(observables[:3]):
    ob.add_operator(1.0, f"X {i}")
for i, ob in enumerate(observables[3:6]):
    ob.add_operator(1.0, f"Z {i}")
for i, ob in enumerate(observables[6:]):
    ob.add_operator(1.0, f"Y {i}")

model = QELModel(reservoir, observables, LinearRegression()) # observable is a qulacs Observable object
X, _, y, _ = dataset.get_train_test()
model.fit(X, y)
print(model.score(X, y))

 1"""
 2
 3Qreserveoir is a Python package for quantum reservoir engineering and quantum extreme learning built on top of qulacs.
 4
 5Installation
 6=====
 7The easiest way to install `qreservoir` is using pip:
 8
 9    $ pip install qreservoir
10
11`qreservoir` requires Python version 3.10 or 3.11.
12
13Modules
14=====
15There are four main modules in `qreservoir`: `encoders`, `reservoirs`, `models` and `datasets`. Each of these
16gives access to a variety of classes to build and train reservoir and extreme learning models.
17
18The general composition of a model is as follows:
19
201. An *encoder*, which encodes the input data into a quantum state
212. A *reservoir*, which evolves the quantum state to which we pass an encoder
223. A *model*, which is a wrapper around reservoirs allowing for prediction and training
23
24Models take a scikit-learn estimator as an argument, which is used to train the model and make predictions.
25The model also takes a list of qulacs observables as an argument, which are used to calculate the expectation
26values and translate the quantum dynamics model to a classical value.
27
28Example
29======
30    from qulacs import Observable
31    from sklearn.linear_model import LinearRegression
32
33    from qreservoir.datasets import Complex_Fourrier
34    from qreservoir.encoders import ExpEncoder
35    from qreservoir.models import QELModel
36    from qreservoir.reservoirs import RotationReservoir
37
38
39    dataset = Complex_Fourrier(complexity=1, size=1000, noise=0.0)
40
41    encoder = ExpEncoder(1, 1, 3) # 1 feature, 1 layer, 1 qubit per feature
42    reservoir = RotationReservoir(encoder, 0, 10)  # 0 ancilla qubits, 10 depth
43
44    observables = [Observable(3) for _ in range(9)] # create observable set
45    for i, ob in enumerate(observables[:3]):
46        ob.add_operator(1.0, f"X {i}")
47    for i, ob in enumerate(observables[3:6]):
48        ob.add_operator(1.0, f"Z {i}")
49    for i, ob in enumerate(observables[6:]):
50        ob.add_operator(1.0, f"Y {i}")
51
52    model = QELModel(reservoir, observables, LinearRegression()) # observable is a qulacs Observable object
53    X, _, y, _ = dataset.get_train_test()
54    model.fit(X, y)
55    print(model.score(X, y))
56
57"""