Name | qsiml JSON |
Version |
0.0.5
JSON |
| download |
home_page | None |
Summary | A simulation of quantum circuits in python |
upload_time | 2024-11-05 11:06:11 |
maintainer | None |
docs_url | None |
author | None |
requires_python | >=3.12 |
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quantum
quantum computing
simulator
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![image](https://github.com/user-attachments/assets/80a3a50c-1268-4b34-9053-edb0f06ccb12)
Qsiml is a Python-based quantum computing simulator that provides a minimalist approach to quantum circuit simulation.
## Installation
```bash
pip install qsiml
```
### Quantum Circuit
A quantum circuit is represented by the `QuantumCircuit` class. It manages a collection of qubits and applies quantum gates to manipulate their states.
```python
from qsiml import QuantumCircuit
qc = QuantumCircuit(n) # Creates a circuit with `n` qubits
```
### Gates
### Single-Qubit Gates
1. Hadamard (H): Creates superposition
```python
qc.h(qubit)
```
2. Pauli-X (NOT): Bit flip
```python
qc.px(qubit)
```
3. Pauli-Y: Rotation around Y-axis
```python
qc.py(qubit)
```
4. Pauli-Z: Phase flip
```python
qc.pz(qubit)
```
5. Phase (P): Applies a phase shift
```python
qc.phase(qubit, theta)
```
6. Rotation Gates: Rotate around X, Y, or Z axis
```python
qc.rx(qubit, theta)
qc.ry(qubit, theta)
qc.rz(qubit, theta)
```
where θ is the rotation angle in radians.
### Multi-Qubit Gates
1. CNOT: Controlled-NOT
```python
qc.cnot(control, target)
```
2. SWAP: Swaps two qubits
```python
qc.swap(qubit1, qubit2)
```
3. Toffoli (CCNOT): Controlled-Controlled-NOT
```python
qc.ccnot(control1, control2, target)
```
4. Fredkin (CSWAP): Controlled-SWAP
```python
qc.cswap(control, target1, target2)
```
## Measurement
Measure all qubits, collapsing the state vector:
```python
result = qc.measure_all() # collapses the state vector to a single basis states
# returns a bitstring of the basis state and stores the collapsed state in qc.classical_bits
```
Measure a specific qubit, partially collapsing the state vector.
```python
qc.measure(qubit) # classical state of qn is stored in qc.classical_bits[n]
```
## Circuit Visualization
```python
from qsiml import QuantumCircuit
qc = QuantumCircuit(5)
qc.px(0)
qc.h(1)
qc.h(2)
qc.h(3)
qc.ccnot(1, 2, 3)
qc.ccnot(2, 3, 4)
```
Display the circuit as an ASCII diagram:
```python
qc.draw("Circuit Visualization: ")
```
```
Circuit Visualization
|q0⟩—X————————————————
|q1⟩————H————————●————
│
|q2⟩———————H—————●——●—
│ │
|q3⟩——————————H——⨁——●—
│
|q4⟩————————————————⨁—
```
```python
qc.operations("Operations: ")
```
prints the gates applied with respect to time:
```
Operations:
1. X on qubit 0
2. H on qubit 1
3. H on qubit 2
4. H on qubit 3
5. CCNOT on qubits 1, 2, 3
6. CCNOT on qubits 2, 3, 4
```
```python
print(qc.circuit)
```
```
prints the internal circuit representation
[('X', [0]), ('H', [1]), ('H', [2]), ('H', [3]), ('CCNOT', [1, 2, 3]), ('CCNOT', [2, 3, 4])]
```
## State Inspection
View the circuit's state without collapsing it.
```python
qc.dump("Dump table: ")
```
prints a table which shows the amplitude, probability, and phase of each possible basis state.
```
Dump Table:
+---------------+---------------+----------------------+---------+
| Basis State | Probability | Amplitude | Phase |
+===============+===============+======================+=========+
| |00001⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
| |00011⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
| |00101⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
| |00111⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
| |01001⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
| |01011⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
| |11101⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
| |11111⟩ | 12.500000% | 0.353553 + 0.000000i | 0 |
+---------------+---------------+----------------------+---------+
```
## Examples
### Bell State Preparation
```python
qc = QuantumCircuit(2)
qc.h(0)
qc.cnot(0, 1)
qc.draw("Bell State diagram: ")
qc.dump("Bell State dump table: ")
```
Output:
```
Bell State diagram:
|q0⟩—H——●—
│
|q1⟩————⨁—
Bell State dump table:
+---------------+---------------+----------------------+---------+
| Basis State | Probability | Amplitude | Phase |
+===============+===============+======================+=========+
| |00⟩ | 50.000000% | 0.707107 + 0.000000i | 0 |
| |11⟩ | 50.000000% | 0.707107 + 0.000000i | 0 |
+---------------+---------------+----------------------+---------+
```
### Quantum Fourier Transform (2 qubits)
```python
qc = QuantumCircuit(2)
qc.h(0)
qc.phase(1, np.pi/2)
qc.cnot(0, 1)
qc.h(1)
qc.swap(0, 1)
qc.draw("Draw: ")
qc.dump("Dump: ")
```
```
Draw:
|q0⟩—H—————————————●—————x—
│ │
|q1⟩————-P(1.5707)—⨁——H——x—
Dump:
+---------------+---------------+-----------------------+---------+
| Basis State | Probability | Amplitude | Phase |
+===============+===============+=======================+=========+
| |00⟩ | 56.250000% | 0.750000 + 0.000000i | 0 |
| |01⟩ | 56.250000% | 0.750000 + 0.000000i | 0 |
| |10⟩ | 31.250000% | 0.250000 + 0.500000i | 1.10715 |
| |11⟩ | 31.250000% | -0.250000 + 0.500000i | 2.03444 |
+---------------+---------------+-----------------------+---------+
```
### Theory for nerds
Quantum computing leverages the principles of quantum mechanics to perform computations. Unlike classical bits, which can be in one of two states (0 or 1), quantum bits (qubits) can exist in a superposition of states, represented as a linear combination of basis states:
`|ψ⟩ = α|0⟩ + β|1⟩`
where `α` and `β` are complex numbers satisfying `|α⟩^2 + |β⟩^2 = 1.0`
A trivial example to illustrate the, albeit niche, advantage of quantum computing over classical computing is the Deutsch-Jozsa algorithm. In the problem, we're given a black box quantum computer known as an oracle that implements some function `f: {0, 1}ⁿ-> {0, 1}`, which takes an n-bit binary value as input and returns either a 0 or a 1 for each input. The function output is either constant, either 1 OR 0 for all inputs, or balanced, 0 for exactly half of the input domain and 1 for the other half. The task is to determine if `f` is constant or balanced using the function.
the deterministic classical approach requires `2^(n - 1) + 1` evaluations to prove that f is either constant or balanced. It needs to map *half + 1* the set of inputs to evaluate, with 100% certainty, the nature of the oracle. If `n := 2`:
|**x (input)** | **f(x) (output)** |
|:--------:|:------------:|
| 00 | 0 |
| 01 | 0 |
| 10 | 1 |
| 00 | 1 |
Only the first 3 calculations are required to determine that the oracle is balanced. Though, the computational complexity increases exponentially, which makes it more expensive to solve for larger values of `n`.
This is where quantum computing shines. The Deutsch-Jozsa algorithm applies the oracle to a superposition of all possible inputs, represented by `n + 1`, where the first `n` qubits are initialized to |0⟩, and the last one is initialized to |1⟩.
```python
n = 10
qc = QuantumCircuit(n + 1) # initialize a circuit with n + 1 qubits
qc.px(n) # initialize the last qubit to |1⟩
```
Apply the Hadamard gate to all qubits to create a superposition of all possible states (try it!)
```python
# applies the hadamard gate to all qubits in the system
for i in range(n + 1):
qc.h(i)
```
The next step is to create an oracle. The oracle essentially acts as a query system, which is easy to represent in classical computing by storing the mapped value in a certain memory register. In quantum computing however, this is impractical. We'll have to create a custom quantum circuit representation of an oracle. We'll use the `n + 1`th qubit as an ancilla qubit that is initialized to a state of |1⟩, and the first `n` qubits as the query. For a balanced function, the oracle should flip the ancilla qubit for exactly half of the input states.
```python
import numpy as np
random_bits = np.random.randint(1, 2**n) # returns a random integer between 1 and 2**n - 1 inclusive.
for i in range(n):
# applies cnot with control bits that lie within the randomly generated binary number. If `random_bit` = `101`, then qubits 0 and 2 would be used as control bits.
if a & (1 << i):
qc.cnot(i, n)
```
Afterwards, we revert the query qubits back to their original state by applying the hadamard gate
```python
for i in range(n):
qc.h(i)
```
Finally, we measure the query qubits individually
```python
for i in range(n):
qc.measure(i)
```
The measured values of the nth qubit are stored in `qc.classical_bits[n]`. If all measured values are 0, i.e. `qc.classical_bits[0..n]`, then the oracle is a constant function. Anything other than that, the oracle is a balanced function.
Now that a balanced oracle function has been implemented, we can implement a constant oracle.
```python
from qsiml import QuantumCircuit
import numpy as np
class DeutschJozsa():
def __init__(self, n: int = 10):
self.qc = QuantumCircuit(n + 1)
self.n = n
def constant_oracle(self, constant_value: int):
if constant_value == 0:
self.qc.i(self.n)
else:
self.qc.px(self.n)
def balanced_oracle(self, random_bits: int):
for i in range(self.n):
if random_bits & (1 << i):
self.qc.cnot(i, self.n)
def deutsch_jozsa(self):
n = self.n
constant_or_balanced = np.random.randint(0, 2)
constant_value = np.random.randint(0, 2)
random_bits = np.random.randint(1, 2**n)
self.qc.px(n)
for i in range(n + 1):
self.qc.h(i)
if constant_or_balanced == 0:
self.constant_oracle(constant_value)
else:
self.balanced_oracle(random_bits)
for i in range(n):
self.qc.h(i)
for i in range(n):
self.qc.measure(i)
self.qc.draw()
print("Classical Bits: ", self.qc.classical_bits[:-1])
dj = DeutschJozsa(10)
dj.deutsch_jozsa()
```
returns this for a constant oracle (Notice how every measured value is 0):
```
|q00⟩—H——————————————————————————————————————H—————————————————————————————M————————————————————————————
0
|q01⟩————H——————————————————————————————————————H—————————————————————————————M—————————————————————————
0
|q02⟩———————H——————————————————————————————————————H—————————————————————————————M——————————————————————
0
|q03⟩——————————H——————————————————————————————————————H—————————————————————————————M———————————————————
0
|q04⟩—————————————H——————————————————————————————————————H—————————————————————————————M————————————————
0
|q05⟩————————————————H——————————————————————————————————————H—————————————————————————————M—————————————
0
|q06⟩———————————————————H——————————————————————————————————————H—————————————————————————————M——————————
0
|q07⟩——————————————————————H——————————————————————————————————————H—————————————————————————————M———————
0
|q08⟩—————————————————————————H——————————————————————————————————————H—————————————————————————————M————
0
|q09⟩————————————————————————————H——————————————————————————————————————H—————————————————————————————M—
0
|q10⟩———————————————————————————————X——H——X—————————————————————————————————————————————————————————————
Classical Bits: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
```
And this for a balanced oracle (The measured values form a non-zero bitstring)
```
|q00⟩—H——————————————————————————————————————————————————H—————————————————————————————M————————————————————————————
0
|q01⟩————H————————————————————————————————●—————————————————H—————————————————————————————M—————————————————————————
│ 1
|q02⟩———————H—————————————————————————————│————————————————————H—————————————————————————————M——————————————————————
│ 0
|q03⟩——————————H——————————————————————————│———————————————————————H—————————————————————————————M———————————————————
│ 0
|q04⟩—————————————H———————————————————————│——————————————————————————H—————————————————————————————M————————————————
│ 0
|q05⟩————————————————H————————————————————│—————————————————————————————H—————————————————————————————M—————————————
│ 0
|q06⟩———————————————————H—————————————————│——●—————————————————————————————H—————————————————————————————M——————————
│ │ 1
|q07⟩——————————————————————H——————————————│——│——●—————————————————————————————H—————————————————————————————M———————
│ │ │ 1
|q08⟩—————————————————————————H———————————│——│——│——●—————————————————————————————H—————————————————————————————M————
│ │ │ │ 1
|q09⟩————————————————————————————H————————│——│——│——│——●—————————————————————————————H—————————————————————————————M—
│ │ │ │ │ 1
|q10⟩———————————————————————————————X——H——⨁——⨁——⨁——⨁——⨁—————————————————————————————————————————————————————————————
Classical Bits: [0, 1, 0, 0, 0, 0, 1, 1, 1, 1]
```
You can import this class using:
```python
from qsiml import DeutschJozsa
```
### State Vector Representation
In Qsiml, an n-qubit system is represented by a 2^n dimensional complex vector, known as the state vector. For example, a two-qubit system is represented by a 4-dimensional vector:
`|ψ⟩ = α|00⟩ + β|01⟩ + γ|10⟩ + δ|11⟩`
where `|α|^2 + |β|^2 + |γ|^2 + |δ|^2 = 1`.
Raw data
{
"_id": null,
"home_page": null,
"name": "qsiml",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.12",
"maintainer_email": null,
"keywords": "quantum, quantum computing, simulator",
"author": null,
"author_email": "Mustafa Aamir <mustafa.290101@gmail.com>, Yusuf Sabuwala <yusuff.0279@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/62/ae/18fe893f63ba833b2f2e99402e562aab4977b45a88cd62b3ab1e217ef58e/qsiml-0.0.5.tar.gz",
"platform": null,
"description": "![image](https://github.com/user-attachments/assets/80a3a50c-1268-4b34-9053-edb0f06ccb12)\n\nQsiml is a Python-based quantum computing simulator that provides a minimalist approach to quantum circuit simulation.\n\n## Installation\n\n```bash\npip install qsiml\n```\n\n### Quantum Circuit\n\nA quantum circuit is represented by the `QuantumCircuit` class. It manages a collection of qubits and applies quantum gates to manipulate their states.\n\n```python\nfrom qsiml import QuantumCircuit\n\nqc = QuantumCircuit(n) # Creates a circuit with `n` qubits\n```\n\n### Gates\n\n### Single-Qubit Gates\n\n1. Hadamard (H): Creates superposition\n ```python\n qc.h(qubit)\n ```\n\n2. Pauli-X (NOT): Bit flip\n ```python\n qc.px(qubit)\n ```\n\n3. Pauli-Y: Rotation around Y-axis\n ```python\n qc.py(qubit)\n ```\n\n4. Pauli-Z: Phase flip\n ```python\n qc.pz(qubit)\n ```\n\n5. Phase (P): Applies a phase shift\n ```python\n qc.phase(qubit, theta)\n ```\n\n6. Rotation Gates: Rotate around X, Y, or Z axis\n ```python\n qc.rx(qubit, theta)\n qc.ry(qubit, theta)\n qc.rz(qubit, theta)\n ```\n where \u03b8 is the rotation angle in radians.\n\n### Multi-Qubit Gates\n\n1. CNOT: Controlled-NOT\n ```python\n qc.cnot(control, target)\n ```\n\n2. SWAP: Swaps two qubits\n ```python\n qc.swap(qubit1, qubit2)\n ```\n\n3. Toffoli (CCNOT): Controlled-Controlled-NOT\n ```python\n qc.ccnot(control1, control2, target)\n ```\n\n4. Fredkin (CSWAP): Controlled-SWAP\n ```python\n qc.cswap(control, target1, target2)\n ```\n\n## Measurement\n\nMeasure all qubits, collapsing the state vector:\n\n```python\nresult = qc.measure_all() # collapses the state vector to a single basis states\n# returns a bitstring of the basis state and stores the collapsed state in qc.classical_bits\n```\n\nMeasure a specific qubit, partially collapsing the state vector.\n\n```python\nqc.measure(qubit) # classical state of qn is stored in qc.classical_bits[n]\n```\n\n## Circuit Visualization\n\n```python\nfrom qsiml import QuantumCircuit\n\nqc = QuantumCircuit(5)\nqc.px(0)\nqc.h(1)\nqc.h(2)\nqc.h(3)\nqc.ccnot(1, 2, 3)\nqc.ccnot(2, 3, 4)\n```\n\nDisplay the circuit as an ASCII diagram:\n\n```python\nqc.draw(\"Circuit Visualization: \")\n```\n```\nCircuit Visualization\n\n|q0\u27e9\u2014X\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n\n|q1\u27e9\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u25cf\u2014\u2014\u2014\u2014\n \u2502\n|q2\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u25cf\u2014\u2014\u25cf\u2014\n \u2502 \u2502\n|q3\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2a01\u2014\u2014\u25cf\u2014\n \u2502\n|q4\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2a01\u2014\n```\n\n```python\nqc.operations(\"Operations: \")\n```\nprints the gates applied with respect to time:\n```\n Operations:\n 1. X on qubit 0\n 2. H on qubit 1\n 3. H on qubit 2\n 4. H on qubit 3\n 5. CCNOT on qubits 1, 2, 3\n 6. CCNOT on qubits 2, 3, 4\n```\n\n```python\nprint(qc.circuit)\n```\n```\nprints the internal circuit representation\n\n[('X', [0]), ('H', [1]), ('H', [2]), ('H', [3]), ('CCNOT', [1, 2, 3]), ('CCNOT', [2, 3, 4])]\n```\n\n## State Inspection\n\nView the circuit's state without collapsing it.\n\n```python\nqc.dump(\"Dump table: \")\n```\n\nprints a table which shows the amplitude, probability, and phase of each possible basis state.\n```\nDump Table:\n+---------------+---------------+----------------------+---------+\n| Basis State | Probability | Amplitude | Phase |\n+===============+===============+======================+=========+\n| |00001\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n| |00011\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n| |00101\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n| |00111\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n| |01001\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n| |01011\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n| |11101\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n| |11111\u27e9 | 12.500000% | 0.353553 + 0.000000i | 0 |\n+---------------+---------------+----------------------+---------+\n```\n\n## Examples\n\n### Bell State Preparation\n\n```python\nqc = QuantumCircuit(2)\nqc.h(0)\nqc.cnot(0, 1)\nqc.draw(\"Bell State diagram: \")\nqc.dump(\"Bell State dump table: \")\n```\n\nOutput:\n```\nBell State diagram:\n|q0\u27e9\u2014H\u2014\u2014\u25cf\u2014\n \u2502\n|q1\u27e9\u2014\u2014\u2014\u2014\u2a01\u2014\n\nBell State dump table:\n+---------------+---------------+----------------------+---------+\n| Basis State | Probability | Amplitude | Phase |\n+===============+===============+======================+=========+\n| |00\u27e9 | 50.000000% | 0.707107 + 0.000000i | 0 |\n| |11\u27e9 | 50.000000% | 0.707107 + 0.000000i | 0 |\n+---------------+---------------+----------------------+---------+\n```\n\n### Quantum Fourier Transform (2 qubits)\n\n```python\nqc = QuantumCircuit(2)\nqc.h(0)\nqc.phase(1, np.pi/2)\nqc.cnot(0, 1)\nqc.h(1)\nqc.swap(0, 1)\nqc.draw(\"Draw: \")\nqc.dump(\"Dump: \")\n```\n\n```\nDraw:\n\n|q0\u27e9\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u25cf\u2014\u2014\u2014\u2014\u2014x\u2014\n \u2502 \u2502\n|q1\u27e9\u2014\u2014\u2014\u2014-P(1.5707)\u2014\u2a01\u2014\u2014H\u2014\u2014x\u2014\n\nDump:\n\n+---------------+---------------+-----------------------+---------+\n| Basis State | Probability | Amplitude | Phase |\n+===============+===============+=======================+=========+\n| |00\u27e9 | 56.250000% | 0.750000 + 0.000000i | 0 |\n| |01\u27e9 | 56.250000% | 0.750000 + 0.000000i | 0 |\n| |10\u27e9 | 31.250000% | 0.250000 + 0.500000i | 1.10715 |\n| |11\u27e9 | 31.250000% | -0.250000 + 0.500000i | 2.03444 |\n+---------------+---------------+-----------------------+---------+\n```\n\n### Theory for nerds\n\nQuantum computing leverages the principles of quantum mechanics to perform computations. Unlike classical bits, which can be in one of two states (0 or 1), quantum bits (qubits) can exist in a superposition of states, represented as a linear combination of basis states:\n\n`|\u03c8\u27e9 = \u03b1|0\u27e9 + \u03b2|1\u27e9`\n\nwhere `\u03b1` and `\u03b2` are complex numbers satisfying `|\u03b1\u27e9^2 + |\u03b2\u27e9^2 = 1.0`\n\nA trivial example to illustrate the, albeit niche, advantage of quantum computing over classical computing is the Deutsch-Jozsa algorithm. In the problem, we're given a black box quantum computer known as an oracle that implements some function `f: {0, 1}\u207f-> {0, 1}`, which takes an n-bit binary value as input and returns either a 0 or a 1 for each input. The function output is either constant, either 1 OR 0 for all inputs, or balanced, 0 for exactly half of the input domain and 1 for the other half. The task is to determine if `f` is constant or balanced using the function.\n\nthe deterministic classical approach requires `2^(n - 1) + 1` evaluations to prove that f is either constant or balanced. It needs to map *half + 1* the set of inputs to evaluate, with 100% certainty, the nature of the oracle. If `n := 2`:\n\n|**x (input)** | **f(x) (output)** |\n|:--------:|:------------:|\n| 00 | 0 |\n| 01 | 0 |\n| 10 | 1 |\n| 00 | 1 |\n\nOnly the first 3 calculations are required to determine that the oracle is balanced. Though, the computational complexity increases exponentially, which makes it more expensive to solve for larger values of `n`.\nThis is where quantum computing shines. The Deutsch-Jozsa algorithm applies the oracle to a superposition of all possible inputs, represented by `n + 1`, where the first `n` qubits are initialized to |0\u27e9, and the last one is initialized to |1\u27e9.\n\n```python\nn = 10\nqc = QuantumCircuit(n + 1) # initialize a circuit with n + 1 qubits\nqc.px(n) # initialize the last qubit to |1\u27e9\n```\nApply the Hadamard gate to all qubits to create a superposition of all possible states (try it!)\n```python\n# applies the hadamard gate to all qubits in the system\nfor i in range(n + 1):\n qc.h(i)\n```\nThe next step is to create an oracle. The oracle essentially acts as a query system, which is easy to represent in classical computing by storing the mapped value in a certain memory register. In quantum computing however, this is impractical. We'll have to create a custom quantum circuit representation of an oracle. We'll use the `n + 1`th qubit as an ancilla qubit that is initialized to a state of |1\u27e9, and the first `n` qubits as the query. For a balanced function, the oracle should flip the ancilla qubit for exactly half of the input states.\n\n```python\nimport numpy as np\nrandom_bits = np.random.randint(1, 2**n) # returns a random integer between 1 and 2**n - 1 inclusive.\nfor i in range(n):\n # applies cnot with control bits that lie within the randomly generated binary number. If `random_bit` = `101`, then qubits 0 and 2 would be used as control bits.\n if a & (1 << i):\n qc.cnot(i, n)\n```\n\nAfterwards, we revert the query qubits back to their original state by applying the hadamard gate\n\n```python\nfor i in range(n):\n qc.h(i)\n```\n\nFinally, we measure the query qubits individually\n```python\nfor i in range(n):\n qc.measure(i)\n```\nThe measured values of the nth qubit are stored in `qc.classical_bits[n]`. If all measured values are 0, i.e. `qc.classical_bits[0..n]`, then the oracle is a constant function. Anything other than that, the oracle is a balanced function.\n\nNow that a balanced oracle function has been implemented, we can implement a constant oracle.\n\n```python\nfrom qsiml import QuantumCircuit\nimport numpy as np\n\nclass DeutschJozsa():\n def __init__(self, n: int = 10):\n self.qc = QuantumCircuit(n + 1)\n self.n = n\n\n def constant_oracle(self, constant_value: int):\n if constant_value == 0:\n self.qc.i(self.n)\n else:\n self.qc.px(self.n)\n\n def balanced_oracle(self, random_bits: int):\n for i in range(self.n):\n if random_bits & (1 << i):\n self.qc.cnot(i, self.n)\n\n def deutsch_jozsa(self):\n n = self.n\n constant_or_balanced = np.random.randint(0, 2)\n constant_value = np.random.randint(0, 2)\n random_bits = np.random.randint(1, 2**n)\n\n self.qc.px(n)\n for i in range(n + 1):\n self.qc.h(i)\n\n if constant_or_balanced == 0:\n self.constant_oracle(constant_value)\n else:\n self.balanced_oracle(random_bits)\n\n for i in range(n):\n self.qc.h(i)\n\n for i in range(n):\n self.qc.measure(i)\n\n\n self.qc.draw()\n print(\"Classical Bits: \", self.qc.classical_bits[:-1])\n\ndj = DeutschJozsa(10)\ndj.deutsch_jozsa()\n```\n\nreturns this for a constant oracle (Notice how every measured value is 0):\n```\n|q00\u27e9\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q01\u27e9\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q02\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q03\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q04\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q05\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q06\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q07\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q08\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\n 0\n|q09\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\n 0\n|q10\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014X\u2014\u2014H\u2014\u2014X\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n\nClassical Bits: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n```\n\nAnd this for a balanced oracle (The measured values form a non-zero bitstring)\n```\n|q00\u27e9\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n 0\n|q01\u27e9\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u25cf\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n \u2502 1\n|q02\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n \u2502 0\n|q03\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n \u2502 0\n|q04\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n \u2502 0\n|q05\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n \u2502 0\n|q06\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u25cf\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n \u2502 \u2502 1\n|q07\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u2502\u2014\u2014\u25cf\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n \u2502 \u2502 \u2502 1\n|q08\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u2502\u2014\u2014\u2502\u2014\u2014\u25cf\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\u2014\u2014\u2014\n \u2502 \u2502 \u2502 \u2502 1\n|q09\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2502\u2014\u2014\u2502\u2014\u2014\u2502\u2014\u2014\u2502\u2014\u2014\u25cf\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014H\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014M\u2014\n \u2502 \u2502 \u2502 \u2502 \u2502 1\n|q10\u27e9\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014X\u2014\u2014H\u2014\u2014\u2a01\u2014\u2014\u2a01\u2014\u2014\u2a01\u2014\u2014\u2a01\u2014\u2014\u2a01\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\n\nClassical Bits: [0, 1, 0, 0, 0, 0, 1, 1, 1, 1]\n```\nYou can import this class using:\n\n```python\nfrom qsiml import DeutschJozsa\n```\n\n### State Vector Representation\n\nIn Qsiml, an n-qubit system is represented by a 2^n dimensional complex vector, known as the state vector. For example, a two-qubit system is represented by a 4-dimensional vector:\n\n`|\u03c8\u27e9 = \u03b1|00\u27e9 + \u03b2|01\u27e9 + \u03b3|10\u27e9 + \u03b4|11\u27e9`\n\nwhere `|\u03b1|^2 + |\u03b2|^2 + |\u03b3|^2 + |\u03b4|^2 = 1`.\n\n",
"bugtrack_url": null,
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"summary": "A simulation of quantum circuits in python",
"version": "0.0.5",
"project_urls": {
"Homepage": "https://github.com/MustafaAamir/qsiml"
},
"split_keywords": [
"quantum",
" quantum computing",
" simulator"
],
"urls": [
{
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