• Corpus ID: 212628244

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

@article{Broughton2020TensorFlowQA,
  title={TensorFlow Quantum: A Software Framework for Quantum Machine Learning},
  author={Mick Broughton and Guillaume Verdon and Trevor McCourt and Antonio J. Martinez and Jae Hyeon Yoo and Sergei V. Isakov and Philip Massey and Murphy Yuezhen Niu and Ramin Halavati and Evan Peters and Martin Leib and Andrea Skolik and Michael Streif and David Von Dollen and Jarrod R. McClean and Sergio Boixo and Dave Bacon and Alan K. Ho and Hartmut Neven and Masoud Mohseni},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.02989}
}
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum… 
TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance
TLDR
There is a direct benefit in performing amplitude encoding before training a TensorFlow Quantum hybrid quantum-classical model using angle encoding, with a third network of the design that utilizes amplitude encoding for enriched state preparation.
Qsun: an open-source platform towards practical quantum machine learning applications
TLDR
A quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is introduced, whose operation is underlined by quantum state wavefunctions and the platform provides native tools supporting VQAs.
Decompositional Quantum Graph Neural Network
TLDR
A novel hybrid quantum-classical algorithm for graph-structured data, which is based on a novel mapping from real-world data to a Hilbert space and maintains the distance relations present in the data and reduces information loss.
Recent Advances for Quantum Neural Networks in Generative Learning
TLDR
This paper interprets these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum extension of classical generative learning models, and explores their intrinsic relation and their fundamental differences.
Limitations of gradient-based Born Machines over tensor networks on learning quantum nonlocality
TLDR
This work investigates the task of training the Born Machine for learning the GHZ state over two different architectures of tensor networks and indicates that gradient-based training schemes over TN Born Machine fail to learn the nonlocal information of the coherent superposition (or parity) of theGHZ state.
Quantum Computing Aided Machine Learning Through Quantum State Fidelity
TLDR
This work proposes a quantum deep learning architecture and demonstrates the quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling and outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.
Neural predictor based quantum architecture search
TLDR
It is demonstrated a neural predictor guided QAS can discover powerful quantum circuit ansatz, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning.
Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
TLDR
A training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces based on the deep Q-learning algorithm and shows when recent separation results between classical and quantum agents for policy gradient RL can be extended to inferring optimal Q-values in restricted families of environments.
Reinforcement Learning with Quantum Variational Circuits
TLDR
Results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space.
...
...

References

SHOWING 1-10 OF 121 REFERENCES
Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
TLDR
A training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces based on the deep Q-learning algorithm and shows when recent separation results between classical and quantum agents for policy gradient RL can be extended to inferring optimal Q-values in restricted families of environments.
Classification with Quantum Neural Networks on Near Term Processors
TLDR
This work introduces a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning, and shows through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets.
Variational quantum policies for reinforcement learning
TLDR
This work investigates how to construct and train reinforcement learning policies based on variational quantum circuits, and proposes and shows the existence of task environments with a provable separation in performance between quantum learning agents and any polynomial-time classical learner.
Quantum Graph Neural Networks
TLDR
QGNNs are introduced, a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network.
Learning to learn with quantum neural networks via classical neural networks
TLDR
This work trains classical recurrent neural networks to assist in the quantum learning process, also know as meta-learning, to rapidly find approximate optima in the parameter landscape for several classes of quantum variational algorithms.
Tensor Networks in a Nutshell
TLDR
This tutorial concludes the tutorial with tensor contractions evaluating combinatorial counting problems and Penrose's tensor contraction algorithm, returning the number of edge-colorings of regular planar graphs.
Optimizing quantum heuristics with meta-learning
TLDR
Evidence that indicates the meta-learner trained on small problems will generalize to larger problems is presented, an important indication that meta-learning and associated machine learning methods will be integral to the useful application of noisy near-term quantum computers.
Entangling Quantum Generative Adversarial Networks
TLDR
This work proposes a new type of architecture for quantum generative adversarial networks (an entangling quantum GAN, EQ-GAN) that overcomes limitations of previously proposed quantum GAns.
Quantum optimization of maximum independent set using Rydberg atom arrays.
Realizing quantum speedup for practically relevant, computationally hard problems is a central challenge in quantum information science. Using Rydberg atom arrays with up to 289 qubits in two spatial
Universal quantum circuit for N-qubit quantum gate: a programmable quantum gate
TLDR
A general quantum circuit able to implement any specific quantum circuit by just setting correctly the parameters is proposed, which opens the possibility to construct a real quantum computer where several different quantum operations can be realized in the same hardware.
...
...