PennyLane: Automatic differentiation of hybrid quantum-classical computations

Abstract

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

arXiv commentCode available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this paper

References in corpus (25)

• A variational eigenvalue solver on a quantum processor
• Supervised learning with quantum enhanced feature spaces
• The theory of variational hybrid quantum-classical algorithms
• A Quantum Approximate Optimization Algorithm
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Evaluating analytic gradients on quantum hardware
• Circuit-centric quantum classifiers
• Quantum generative adversarial learning
• Quantum generative adversarial networks
• Quantum Natural Gradient
• ProjectQ: An Open Source Software Framework for Quantum Computing
• Strawberry Fields: A Software Platform for Photonic Quantum Computing
• Differentiable Learning of Quantum Circuit Born Machine
• Concrete Categorical Model of a Quantum Circuit Description Language with Measurement
• Variational Quantum State Diagonalization
• LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing
• qHiPSTER: The Quantum High Performance Software Testing Environment
• Practical optimization for hybrid quantum-classical algorithms
• Qiskit Backend Specifications for OpenQASM and OpenPulse Experiments
• Universal discriminative quantum neural networks
• A quantum algorithm to train neural networks using low-depth circuits
• TensorNetwork: A Library for Physics and Machine Learning
• QVECTOR: an algorithm for device-tailored quantum error correction
• A framework for algorithm deployment on cloud-based quantum computers

Cited by in corpus (93)

• Variational Quantum Algorithms
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Quantum Natural Gradient
• Transfer learning in hybrid classical-quantum neural networks
• Quantum convolutional neural network for classical data classification
• Qulacs: a fast and versatile quantum circuit simulator for research purpose
• General parameter-shift rules for quantum gradients
• Stochastic gradient descent for hybrid quantum-classical optimization
• Quantum circuit architecture search for variational quantum algorithms
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
• A divide-and-conquer algorithm for quantum state preparation
• Estimating the gradient and higher-order derivatives on quantum hardware
• Training Quantum Embedding Kernels on Near-Term Quantum Computers
• Qibo: a framework for quantum simulation with hardware acceleration
• Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms
• A variational toolbox for quantum multi-parameter estimation
• Quantum Krylov subspace algorithms for ground and excited state energy estimation
• Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier
• Quantum Machine Learning: from physics to software engineering
• Recent advances for quantum classifiers
• Bugs in Quantum Computing Platforms: An Empirical Study
• Distributed Quantum Computing and Network Control for Accelerated VQE
• Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays
• JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
• Pulse-level noisy quantum circuits with QuTiP
• Graph neural network initialisation of quantum approximate optimisation
• Quantum Computing: An Overview Across the System Stack
• Toward Trainability of Quantum Neural Networks
• Configurable sublinear circuits for quantum state preparation
• Training Gaussian Boson Sampling Distributions
• Application of a variational hybrid quantum-classical algorithm to heat conduction equation
• Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits
• Particle Track Reconstruction with Quantum Algorithms
• Biomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways
• Entangled Datasets for Quantum Machine Learning
• Variational Quantum Linear Solver with Dynamic Ansatz
• Quantum Computer-Aided design of Quantum Optics Hardware
• Quantum simulation of molecules in solution
• Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode
• Hybrid Quantum-Classical Graph Convolutional Network
• Efficient calculation of gradients in classical simulations of variational quantum algorithms
• The Unified Effect of Data Encoding, Ansatz Expressibility and Entanglement on the Trainability of HQNNs
• Connecting geometry and performance of two-qubit parameterized quantum circuits
• A Scalable Decoder Micro-architecture for Fault-Tolerant Quantum Computing
• Avoiding local minima in Variational Quantum Algorithms with Neural Networks
• Q${}^2$Chemistry: A quantum computation platform for quantum chemistry
• Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation
• Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack
• Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
• Quantum Generative Models for Small Molecule Drug Discovery
• Variational Quanvolutional Neural Networks with enhanced image encoding
• Efficient realization of quantum primitives for Shor's algorithm using PennyLane library
• Quantum Lazy Training
• A backend-agnostic, quantum-classical framework for simulations of chemistry in C++
• On the Principles of Differentiable Quantum Programming Languages
• QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
• Relative energies without electronic perturbations via Alchemical Integral Transform
• lambeq: An Efficient High-Level Python Library for Quantum NLP
• Qsun: an open-source platform towards practical quantum machine learning applications
• Performance of Particle Tracking Using a Quantum Graph Neural Network
• Unveiling quantum phase transitions from traps in variational quantum algorithms
• Solving Fractional Differential Equations on a Quantum Computer: A Variational Approach
• The dilemma of quantum neural networks
• Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
• Explicit block encodings of boundary value problems for many-body elliptic operators
• Drug Discovery Approaches using Quantum Machine Learning
• Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
• The Inductive Bias of Quantum Kernels
• Differentiable quantum computational chemistry with PennyLane
• Analytical Ground- and Excited-State Gradients for Molecular Electronic Structure Theory from Hybrid Quantum/Classical Methods
• QDCNN: Quantum Dilated Convolutional Neural Network
• Encoding-dependent generalization bounds for parametrized quantum circuits
• Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification
• Simulations of Quantum Circuits with Approximate Noise using qsim and Cirq
• Image Processing in Quantum Computers
• QForte: an efficient state simulator and quantum algorithms library for molecular electronic structure
• Quantum Graph Convolutional Neural Networks
• Quantum Circuit Cutting for Classical Shadows
• A Unified Framework for Quantum Supervised Learning
• Estimating the Number of States via the Rodeo Algorithm for Quantum Computation
• Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
• Accelerating variational quantum algorithms with multiple quantum processors
• OpticalGAN : Generative Adversarial Networks for Continuous Variable Quantum Computation
• Quantum constraint learning for quantum approximate optimization algorithm
• Fast quantum circuit simulation using hardware accelerated general purpose libraries
• Simultaneous Quantum Machine Learning Training and Architecture Discovery
• Solving Differential Equations via Continuous-Variable Quantum Computers
• Differentiate Everything with a Reversible Embeded Domain-Specific Language
• Comparative study of variational quantum circuit and quantum backpropagation multilayer perceptron for COVID-19 outbreak predictions
• Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
• Multiple Query Optimization using a Hybrid Approach of Classical and Quantum Computing
• A Quantum Convolutional Neural Network for Image Classification
• NordIQuEst: the Nordic-Estonian Quantum Computing e-Infrastructure Quest