Native AI Quantum Energy Lab

Native AI Quantum Energy Lab is an experimental educational project that combines a simple quantum-computing simulator with an exploration of long-standing unsolved mathematical problems and a small library of energy and particle simulations. It is inspired by the idea of building a “native AI quantum computer” – a software system capable of running quantum circuits, thinking about deep mathematical questions and even modeling energy transfer and particle interactions on a classical computer.

What’s inside?

This repository contains two main simulation modules and a document explaining ten famous unsolved problems in mathematics:

1. Quantum Computing Simulation – a minimal yet functional simulator for quantum circuits written in pure Python (found in quantum_simulator.py). The simulator supports a small set of single-qubit gates (Hadamard and Pauli-X), a two-qubit controlled-NOT (CNOT) gate and measurement. You can create circuits, apply gates, and measure qubits to observe the probabilistic outcomes expected from quantum mechanics. The code uses the vector–state model implemented directly with Python lists and complex numbers, so it has no third-party dependencies.

2. Energy and Particle Simulation – a simple set of utilities (in energy_simulator.py) that model energy generation and consumption as well as basic particle interactions. Functions include:

   • solar_panel_output(power_watt, hours, efficiency) – computes the energy produced by a solar panel over a number of hours.
   • battery_discharge(capacity_mAh, load_mA, hours) – estimates the remaining battery capacity after discharging at a given load.
   • simulate_particle_collision(m1, v1, m2, v2) – performs a simple one-dimensional elastic collision between two particles and returns their post-collision velocities.

   These routines are not intended to be accurate physical simulations but demonstrate how one might model energy and particle dynamics in software.

3. Unsolved Mathematical Problems – the file problems.md contains short summaries of ten of the world’s most renowned open problems. Each problem entry includes a succinct description and, where appropriate, links to authoritative sources for further reading. The list includes all of the Clay Mathematics Institute (CMI) Millennium Prize problems (such as the Riemann Hypothesis and P vs NP) plus additional conjectures from number theory and analysis.

Documentation and type hints

Every public function and method in the simulators is documented with detailed NumPy-style docstrings that explain arguments, return values, units, edge cases, and provide runnable examples. All modules use Python type hints to aid static analysis and make the APIs self-documenting when used in IDEs.

Getting started

Clone this repository and ensure you have Python 3.8 or later. No external libraries are required; the simulators depend only on the Python standard library. You can run the modules directly or import the functions into your own scripts.

For example, to create a simple quantum circuit:

from native_ai_quantum_energy.quantum_simulator import QuantumCircuit

# Create a two-qubit circuit
qc = QuantumCircuit(2)

# Put the first qubit into superposition and entangle with the second qubit
qc.apply_hadamard(0)
qc.apply_cnot(0, 1)

# Measure both qubits
qc.measure_all()

print("Measurement results:", qc.measurements)

Similarly, to simulate energy production:

from native_ai_quantum_energy.energy_simulator import solar_panel_output, battery_discharge

# 100 W solar panel running for 5 hours at 15 % efficiency
energy_joules = solar_panel_output(100, 5, 0.15)
print("Energy produced (J):", energy_joules)

# Battery with 2000 mAh capacity delivering 500 mA for 3 hours
remaining = battery_discharge(2000, 500, 3)
print("Remaining capacity (mAh):", remaining)

Running tests

The project ships with a comprehensive pytest test suite that covers both the quantum and energy simulators. After installing the development dependencies (pip install -r requirements-dev.txt if available, or simply pip install pytest), run the tests with:

pytest

All tests should pass without requiring any additional configuration.

Disclaimer

The “harness energy and particles” portion of this project is a purely digital exercise. The simulations here do not allow a computer to collect real energy or manipulate physical particles; they simply model these processes in software for educational purposes. Likewise, the unsolved problems summaries are provided for learning and inspiration and do not offer solutions to those problems.