quantxlib 0.1.1

Quantum computing for everyone — use quantum algorithms without knowing quantum mechanics.

QuantX

Quantum computing for everyone. Use quantum algorithms without knowing quantum mechanics.

from quantx import search

result = search(["alice", "bob", "charlie", "diana"], target="charlie")
print(result)
# SearchResult(FOUND: 'charlie', confidence=100.0%, qubits=2, iterations=1)

QuantX wraps Qiskit and handles all the quantum circuit construction, qubit management, and measurement interpretation behind a clean Python API. You pass in normal Python data, you get back normal Python results.

Install

pip install quantxlib

Requires Python 3.9+ and Qiskit 2.x (installed automatically).

Quick Start

Search a list (Grover's algorithm)

Grover's algorithm searches an unsorted collection in O(sqrt(N)) time — a quadratic speedup over classical linear search.

from quantx import search

# Search through strings
result = search(["alice", "bob", "charlie", "diana"], target="charlie")
print(result.found)       # True
print(result.confidence)  # 1.0 (100%)

# Search through numbers
result = search(list(range(16)), target=7)
print(result.found)       # True
print(result.confidence)  # ~0.96
print(result.iterations)  # 3 (optimal Grover iterations)

Inspect the circuit

Every result lets you peek under the hood at the actual quantum circuit:

result = search(["a", "b", "c", "d"], target="b")

# Print the circuit diagram
print(result.draw())

# Get the raw Qiskit circuit object
circuit = result.get_circuit()

Configure the backend

QuantX uses a local simulator by default. Switch to real IBM Quantum hardware with one line:

import quantx

# Local simulator (default — no setup needed)
quantx.set_backend("aer_simulator")

# Real quantum hardware
quantx.set_backend("ibm_brisbane", token="your-ibm-quantum-token")

# Adjust measurement shots (default: 1024)
quantx.set_shots(4096)

Get a free IBM Quantum token at quantum.ibm.com.

Modules

Module	Algorithm	Status
search	Grover's search	Available
optimise	QAOA optimisation	Planned
ml	Quantum kernel classification	Planned
crypto	Shor's factoring, BB84 QKD	Planned

Error Messages

QuantX is designed to give you clear, actionable errors — not cryptic quantum tracebacks:

InvalidInputError: Target 'eve' is not in the search space.
Grover's algorithm requires the target to exist in the list.
Your list contains: ['alice', 'bob', 'charlie', 'diana']

QubitLimitError: This operation needs 25 qubits, but the current backend
supports up to 24.
Try reducing your input size or switching to a more powerful backend:
  quantx.set_backend('ibm_brisbane', token='your-token')

API Reference

search(items, target, *, shots=None, force=False, auto_pad=True)

Search for an item using Grover's quantum search algorithm.

Parameters:

• items — list or tuple of items to search through (at least 2)
• target — the item to find (must exist in items)
• shots — number of circuit executions (default: 1024)
• force — skip warnings for large search spaces
• auto_pad — automatically pad to power-of-2 size (default: True)

Returns: SearchResult with:

• .found — whether the target was found (bool)
• .confidence — probability of correctness (0.0 to 1.0)
• .target — the searched item
• .iterations — Grover iterations used
• .n_qubits — number of qubits in the circuit
• .measurements — raw measurement counts dict
• .get_circuit() — returns the Qiskit QuantumCircuit
• .draw() — renders the circuit diagram

set_backend(backend_name, *, token=None, max_qubits=None)

Switch the quantum backend. "aer_simulator" for local, or an IBM backend name for real hardware.

set_shots(shots)

Set the default number of measurement shots (default: 1024).

How It Works

When you call search(items, target), QuantX:

1. Validates your input and gives a clear error if something is wrong
2. Calculates how many qubits are needed (ceil(log2(len(items))))
3. Pads the search space to a power of 2 (required by Grover's algorithm)
4. Computes the optimal number of Grover iterations (floor(pi/4 * sqrt(N)))
5. Builds a quantum circuit: Hadamard gates, oracle, diffusion operator
6. Runs it on the backend and interprets the measurements
7. Returns a SearchResult with the answer and confidence score

You get the quantum speedup without writing a single quantum gate.

Development

# Clone and install in dev mode
git clone https://github.com/aaravchourishi/quantx.git
cd quantx
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=quantx

Project Structure

quantx/
├── __init__.py       # Public API
├── search.py         # Grover's search
├── backends.py       # Backend management
├── exceptions.py     # Custom error classes
├── utils.py          # Shared utilities
├── _validators.py    # Input validation
tests/
├── test_search.py    # 28 tests
examples/
├── 01_basic_search.py

Standalone Algorithms

The repo also includes standalone implementations of fundamental quantum algorithms (no library needed):

File	Algorithm
01_bell_state.py	Bell State — quantum entanglement
02_deutsch_joshi.py	Deutsch-Jozsa — constant vs balanced
03_grovers_search.py	Grover's Search — unstructured search
04_quantum_teleportation.py	Quantum Teleportation
05_quantum_calculator.py	Quantum arithmetic (+, -, x)

Requirements

• Python 3.9+
• Qiskit 2.x
• Qiskit Aer 0.13+
• NumPy

Optional: qiskit-ibm-runtime for real hardware, scikit-learn for the ML module.

License

MIT