Tensor network based quantum software framework for the NISQ era

Quantum computational chemistry based on TensorCircuit

The next-gen AI-native tensor-network-based quantum software framework

This repository contains a new backend which can simulate noisy quantum logic circuits using the density matrix formalism.

Simulate and optimize quantum communication networks using quantum computers.

Quantum computational chemistry based on TensorCircuit

Qiskit Global Summer School 2022 [QGSS22]: Quantum Simulations. This repository contains labs and assignments completed over the course of the program.

Implementation of the paper Quantum Error Mitigation by Pauli Check Sandwiching. The scheme was first explored by the paper Extended flag gadgets for low-overhead circuit verification. Adds Pauli parity checks to the input quantum circuit.

Supplemental code for "Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks"

Open-source, reproducible simulator for analyzing quantum error correction under explicit noise assumptions, with Monte Carlo and analytical (binomial) validation, including subplot with log-scaled explicit error analysis; SEE README

Python package containing Pauli Twirling pairs for common two-qubit gates, functions to generate Pauli Twirling pairs for additional gates, and twirling implementations

Analysis of noise effects on quantum circuits using simulations and real IBM quantum hardware.

Building a noise model for simulating a Qiskit quantum circuit in the presence of errors.

Qiskit Global Summer School 2022: Quantum Simulations by IBM Quantum. Lab assignments completed during the 2-week program.

Hardware-Aware Variational Quantum Algorithms under Noise (VQE / QAOA Benchmarking Across IBM & IonQ)

Using Qiskit to research Quantum probability distributions

This is an attempt to reduce the quantum noice we get when we run a circuit in a quantum computer.

Quantum Noise Explorer, an intuitive platform for analysing noise in quantum circuits

This repository hosts code for a thesis on learning full output distributions of quantum circuits using Transformer-based Graph Neural Networks. It includes dataset generation under noiseless and noisy settings, circuit feature engineering, GNN and CNN modeling, transfer learning across qubit sizes, and model evaluation.