A comprehensive benchmark suite for quantum optimization methods, featuring ten challenging problem classes with practical applications.

The Swiss Army Knife of Applied Quantum Technology (Experimental Tech)

A Python library to automate generating, parallelizing, and executing quantum programs.

A modular quantum computing library in Python featuring QAOA, Grover, HHL, and VQC — built on Qiskit, with future plans to become a QPU-agnostic, from-scratch quantum SDK.

First place, CWRU Quantum Computing Club Hackathon, Fall 2022

QuESO (Quantum Enhanced Seat Optimizer) is an open-source, QUBO-based office seating optimizer.

Runtime-aware QAOA benchmark for constrained J1-J2 Ising ground-state search on NISQ hardware.

Implicit-differentiation experiments for parametrized Hamiltonians and bilevel Max-Cut optimization.

A PyTorch topological regularizer based on the Helix-TTD Constitutional Hamiltonian. Enforces phase-locked AI alignment via trefoil knot invariants to suppress drift and barren plateaus.

Reproducible quantum optimisation research for tune-family inference using QUBO, Ising models and local Qiskit QAOA.

Optimization-Driven Quantum Circuit Reduction Reproduction

GATE (Gate Assessment and Threshold Evaluation) and GSI (Gate Significance Index)

Qiskit-compatible distributed quantum approximate optimization algorithm simulator for solving general quadratic unconstrained binary optimization problems.

Quantum-inspired optimization for supply chain network design

This project aims to solve qubit placement problems in nearest neighbour compliance quantum circuits. We approached the problem using integer linear programming, and solve for the exact global optimal solution.

Hybrid quantum optimization for Max-Cut and VRPTW using QUBO, QAOA, Simulated Annealing, and Google OR-Tools. QAIG Optimization Screening Assignment implemented with Qiskit and modular Python architecture.

A quantum-inspired optimization solution for the Warehouse Selection Puzzle. This project uses OpenJij (Simulated Annealing) to solve a QUBO model, maximizing inventory value while satisfying strict mass, category, and compatibility constraints.

Benchmarking budget-aware classical optimizers vs COBYLA/SPSA for QAOA parameter tuning on IBM quantum hardware — negative result: shallow QAOA landscapes are too smooth to benefit from constrained optimization techniques.