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Open source software in quantum computing
Open source software is becoming crucial in the design and testing of quantum algorithms.
Quantum Physics Mathematical Software Programming Languages
DeepQMC: an open-source software suite for variational optimization of deep-learning molecular wave functions
Computing accurate yet efficient approximations to the solutions of the electronic Schr\"odinger equation has been a paramount challenge of computational chemistry for decades.
Chemical Physics
DeePKS-kit: a package for developing machine learning-based chemically accurate energy and density functional models
We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models.
Chemical Physics Computational Physics
QASMBench: A Low-level QASM Benchmark Suite for NISQ Evaluation and Simulation
For evaluation, we measure the execution fidelity of a subset of QASMBench applications on 12 IBM-Q machines through density matrix state tomography, which comprises 25K circuit evaluations.
Quantum Physics
DQC: a Python program package for Differentiable Quantum Chemistry
Automatic differentiation represents a paradigm shift in scientific programming, where evaluating both functions and their derivatives is required for most applications.
QuantumDynamics.jl: A modular approach to simulations of dynamics of open quantum systems
Thus, while the built-in methods can be used as end-points in themselves, the package provides an integrated platform for experimentation, exploration, and method development.
Quantum Physics Chemical Physics
Automatic Differentiable Monte Carlo: Theory and Application
Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored.
When Machine Learning Meets Quantum Computers: A Case Study
It is imminent to know how to design the quantum circuit for accelerating neural networks.
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows.
On the Principles of Differentiable Quantum Programming Languages
We also conduct a case study of training a VQC instance with controls, which shows the advantage of our scheme over existing auto-differentiation for quantum circuits without controls.
