PennyLane: Automatic differentiation of hybrid quantum-classical computations

By Ville Bergholm, Josh A. Izaac, Maria Schuld, Christian Gogolin, Ankit Khandelwal, Nathan Killoran. arXiv.org, 2018

1,710 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/38d8230a7aeae6554497b253848ad5bf677e4fb3

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Citations in the corpus

Quantum machine learning models are kernel methods (2021, methodology): “These kernels are plotted in Figure 7 using simulations of quantum computations implemented in the quantum machine learning software library PennyLane [22].”
Variational Quantum Circuits for Deep Reinforcement Learning (2019, methodology): “The quantum circuit as shown in the ﬁgure is numerically simulated with the software package PennyLane [31].”
Transfer learning in hybrid classical-quantum neural networks (2019, methodology): “The numerical simulation was done through the PennyLane software platform [9].”
Quantum Natural Gradient (2019, methodology): “These experiments were performed with the open-source quantum machine learning software library PennyLane [2, 22].”
Estimating the gradient and higher-order derivatives on quantum hardware (2020, methodology): “On the other hand, for s = π/ 2 we obtain the parameter-shift rule already studied in [10–13] and used in quantum software libraries [20– 24].”
Quantum computing with Qiskit (2024, background): “Some of these tools are explicitly built around OpenQASM [18, 19, 45, 77], while others support serialization to and from it [17, 24, 33, 41, 48, 52, 83, 86, 88].”
t|ket⟩: a retargetable compiler for NISQ devices (2020, background): “Although higher-level application-oriented toolkits are becoming available [8, 9], the programmer must usually describe the algorithm to be run in terms of basic unitary gates.”
General parameter-shift rules for quantum gradients (2021, background): “However, gradient-based optimization provides signiﬁcant advantages, from convergence guarantees [32] to the availability of workhorse algorithms (e.g., stochastic gradient descent) and software tooling developed for machine learning [33, 34, 35, 36, 37].”
Evaluating analytic gradients on quantum hardware (2018, methodology): “The results of this paper are implemented in the software framework PennyLane [8], which facilitates hybrid quantum-classical optimization across ar X iv :1 81 1.”
Effect of data encoding on the expressive power of variational quantum-machine-learning models (2020, methodology): “[34] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M.”
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021, methodology): “Library: To facilitate future research in QML and variational quantum simulation, we release QuantumEngine, a PyTorchbased GPU-accelerated library to enable fast training of parameterized quantum circuits (over 200× faster than the PennyLane [6]).”
Quantum convolutional neural network for classical data classification (2021, methodology): “The variational parameters in the QCNN ansatze are optimized by minimizing the cost function with an optimizer provided in Pennylane [27].”
Qulacs: a fast and versatile quantum circuit simulator for research purpose (2020, methodology): “Qulacs has also been chosen as a fast backend simulator in several libraries and services such as PennyLane (Xanadu) [16], t|ket> (Cambridge Quantum Computing) [45], Orquestra (Zapata computing) [46], and Tequila [47].”
Potential of quantum computing for drug discovery (2018, background): “Along the same lines, software libraries for integrating quantum algorithms with existing machine learning libraries have started to emerge [189].”
Brain tumor detection and classification using machine learning: a comprehensive survey (2021, background): “Superposition of quantum states/parallelism/entanglement can all be used to establish quantum computer supremacy [258].”