A PyTorch-based framework for Quantum Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
I think it will be great if we have blocks to easily construct QCNN, which is a special but important category of quantum neural networks requiring mid-circuit measurements.
Proposed Solution
The key component of QCNN is a "pooling" layer, which includes measurement and a conditional gate on the measurement outcome. And I suppose we can easily implement QCNN by usi
Type an M x M matrix for your open quantum system Hamiltonian, and give a spectral density (analytic or numerical). FeynDyn gives the density matrix dynamics according to the Leggett-Caldeira bath or the Feynman-Vernon bath at any temperature. Can do up to 16 qubits (65536 levels) and infinitely many bath modes. Email nike@hpqc.org for the latest version or any questions.
Official implementation of spectrum bifurcation renormalization group(SBRG), which is suitable for quantum simulation on strong disordered systems for 1D and 2D. Paper: arXiv:2008.02285[https://arxiv.org/abs/2008.02285], Phys. Rev. B 93, 104205 (2016)[https://arxiv.org/abs/1508.03635]
EntropiQ - a community effort to build a no-code cloud platform for designing and running large-scale simulations of many-body quantum mechanical systems using Tensor Networks
This project utilizes a series of algorithms, adversarial agents, and simulation tools to study and generate new multicellular organisms; a user interface allows human guidance to tailor desired results.
In my licenciatura (BA + M.Sc. in Physics equivalent) thesis, with my director J.M. Matera, we worked in both analytical and computational techniques for the study of non-markovian quantum systems using Max-Ent approximation for instantaneous states, the latter with open code and hand-made also.
Issue Description
I think it will be great if we have blocks to easily construct QCNN, which is a special but important category of quantum neural networks requiring mid-circuit measurements.
Proposed Solution
The key component of QCNN is a "pooling" layer, which includes measurement and a conditional gate on the measurement outcome. And I suppose we can easily implement QCNN by usi