r/quantum

I created a practical Python walkthrough for programmers interested in mathematical optimization with gurobipy.

The example formulates Max-Cut as a quadratic binary optimization problem and covers:

- creating a Gurobi model;

- adding binary decision variables;

- constructing a quadratic objective from a matrix;

- calling optimize();

- extracting the binary solution and objective value;

- benchmarking randomly generated problem instances;

- and understanding how MIPGap affects runtime.

It assumes familiarity with Python, and Jupyter notebooks, but no previous optimization experience.

Video: https://youtu.be/TB1ny8o4ImQ

Code: https://github.com/supreethmv/Quantum-Algorithms-and-Applications

I’d particularly appreciate feedback on whether the gurobipy implementation and explanation are approachable for Python developers encountering quadratic optimization for the first time.

The parity operator (P hat) stands as one of the most fundamental operators in quantum mechanics. For any physical system governed by a symmetric potential, the Hamiltonian (H hat) and the parity operator strictly commute, yielding the relation [H hat, P hat] = 0. This mathematically guarantees the existence of a complete set of simultaneous eigenstates. It physically implies that both the energy and the spatial symmetry of the system can be determined simultaneously with absolute precision, bypassing the constraints of Heisenberg's uncertainty principle. The attached material explicitly demonstrates the algebraic and analytic properties of P hat.

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In quantum mechanics, the Fourier transform is not merely an abstract mathematical tool for space transformation, but the strict mathematical representation of wave superposition.
To spatially localize a particle, an infinite spectrum of plane waves possessing different momenta must be continuously added (superposed).
The integral of the inverse Fourier transform precisely represents this continuous summation.
I hope this is helpful to those learning this for the first time.

Hi everyone,

As part of my PhD project, I developed an open-source Python package to rapidly analyze superconducting quantum circuits without relying on electromagnetic solvers.

The motivation behind this project was to have a fast tool that can provide an estimate of the relevant circuit parameters during the early stages of quantum chip design. During this phase, many design parameters need to be adjusted repeatedly until the circuit reaches the desired Hamiltonian regime, and running full electromagnetic simulations for every iteration can become time-consuming.

The goal of QuLTRA is to provide a lightweight and fast way to explore superconducting circuit designs before moving to more computationally expensive simulations.

I thought it could be interesting for the community, and I would really appreciate any feedback from researchers, students, or anyone working with superconducting quantum circuits. If you try it, I would be very happy to hear your thoughts and suggestions on how to improve the tool.

GitHub: https://github.com/SimonaZaccaria/QuLTRA

Thanks!