Quantum autoencoders for efficient compression of quantum data

By Jonathan Romero, Jonathan Olson, Alán Aspuru-Guzik. 2016

621 citations recorded by the source corpus.

Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x, to map x to a lower dimensional point y such that x can likely be recovered from y. The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particular data set of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/c637364fbe58909c6986499de71ece8865fe67b4

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Citations in the corpus

Circuit-centric quantum classifiers (2018, background): “Hybrid approaches called “variational algorithms” [2, 13, 14] are much more suited to near term quantum computing and are rapidly getting popularity in the quantum research community in recent months.”
Evaluating analytic gradients on quantum hardware (2018, background): “Examples of such algorithms are variational quantum eigensolvers [1], quantum approximate optimization [2], variational autoencoders [3], quantum feature embeddings [4, 5] and variational classiﬁers [6, 7], but also more general hybrid optimization frameworks [8].”
Variational Quantum Linear Solver (2019, background): “Other VHQCAs have been proposed for chemistry [20–23], simulation [24–28], data compression [29], state diagonalization [30–32], compiling [33, 34], quantum foundations [35], fidelity estimation [36], and metrology [37].”
Variational quantum algorithms (2020, background): “[138] introduced a VQA for quantum autoencoding, with the goal of compressing quantum data.”
PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018, background): “[5] Jonathan Romero, Jonathan P Olson, and Alan AspuruGuzik, “Quantum autoencoders for efficient compression of quantum data,” Quantum Science and Technology 2, 045001 (2017).”
Quantum-assisted quantum compiling (2018, neutral): “[2, 18–25].”
Barren plateaus in quantum neural network training landscapes (2018, background): “However, in all domains of applicability, there have been implementations that utilize parametrized random circuits of varying depth [7, 13, 21, 23, 30].”
Quantum machine learning: a classical perspective (2017, background): “Recently, two similar proposals [144,145] have overcome the problem of modelling nonlinearities by using measurements and introducing a number of overhead qubits in the input and output of each node of the network.”
Parameterized quantum circuits as machine learning models (2019, background): “[ 76] and [77], respectively. ﬁnding the factors.”