56 lines
1.5 KiB
Markdown
56 lines
1.5 KiB
Markdown
|
# Machine Learning (QML)
|
||
|
|
|
||
|
|
Quantum machine learning is one of the biggest pillars of quantum computing application that we will see in the workforce of the future
|
||
|
|
One of the big contenders in this field is Xanadu.ai's [pennylane](https://pennylane.ai/) software. Recently version 0.25 was just released and a lot more applications can be created.
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
The following questions can finally be answered with [this](https://pennylane.ai/blog/2022/08/pennylane-v025-released/#new-return-types-for-qnodes-with-multiple-measurements) release:
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
#### How many qubits/gates do I need to run this algorithm?
|
||
|
|
|
||
|
|
module for estimating through process of first and second quantization:
|
||
|
|
|
||
|
|
```
|
||
|
|
# first quantization
|
||
|
|
|
||
|
|
>>> n = 100000 # number of plane waves
|
||
|
|
>>> eta = 156 # number of electrons
|
||
|
|
>>> omega = 1145.166 # unit cell volume in atomic units
|
||
|
|
>>> algo = FirstQuantization(n, eta, omega)
|
||
|
|
>>> algo.gates
|
||
|
|
1.10e+13
|
||
|
|
>>> algo.qubits
|
||
|
|
4416
|
||
|
|
```
|
||
|
|
```
|
||
|
|
# second quantization with double factored Hamiltonian
|
||
|
|
# Hamiltonia
|
||
|
|
|
||
|
|
symbols = ['O', 'H', 'H']
|
||
|
|
geometry = np.array([[0.00000000, 0.00000000, 0.28377432],
|
||
|
|
[0.00000000, 1.45278171, -1.00662237],
|
||
|
|
[0.00000000, -1.45278171, -1.00662237]], requires_grad = False)
|
||
|
|
|
||
|
|
mol = qml.qchem.Molecule(symbols, geometry, basis_name='sto-3g')
|
||
|
|
core, one, two = qml.qchem.electron_integrals(mol)()
|
||
|
|
|
||
|
|
algo = DoubleFactorization(one, two)
|
||
|
|
```
|
||
|
|
```
|
||
|
|
|
||
|
|
```
|
||
|
|
|
||
|
|
and voila!
|
||
|
|
|
||
|
|
```
|
||
|
|
|
||
|
|
>>> print(algo.gates, algo.qubits)
|
||
|
|
103969925, 290
|
||
|
|
```
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
and even more capabilities are available the more you explore! :)
|