A generative modeling approach for benchmarking and training shallow quantum circuits

By Marcello Benedetti, Delfina Garcia-Pintos, Oscar J. Perdomo, Vicente Leyton-Ortega, Y. Nam, Alejandro Perdomo-Ortiz. npj Quantum Information, 2018

414 citations recorded by the source corpus.

Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propose a quantum circuit learning algorithm that can be used to assist the characterization of quantum devices and to train shallow circuits for generative tasks. The procedure leverages quantum hardware capabilities to its fullest extent by using native gates and their qubit connectivity. We demonstrate that our approach can learn an optimal preparation of the Greenberger-Horne-Zeilinger states, also known as “cat states”. We further demonstrate that our approach can efficiently prepare approximate representations of coherent thermal states, wave functions that encode Boltzmann probabilities in their amplitudes. Finally, complementing proposals to characterize the power or usefulness of near-term quantum devices, such as IBM’s quantum volume, we provide a new hardware-independent metric called the qBAS score. It is based on the performance yield in a specific sampling task on one of the canonical machine learning data sets known as Bars and Stripes. We show how entanglement is a key ingredient in encoding the patterns of this data set; an ideal benchmark for testing hardware starting at four qubits and up. We provide experimental results and evaluation of this metric to probe the trade off between several architectural circuit designs and circuit depths on an ion-trap quantum computer.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/7a068d5c5ca129cc91617f0e90bcb38439e491b2

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Citations in the corpus

Variational Quantum Computation of Excited States (2018, methodology): “Here we study hybrid quantum-classical algorithms, which dramatically reduce the required gate depth to run and somewhat mitigate errors, by closely integrating classical and quantum subroutines [2, 9, 14, 15, 21, 23, 26, 40].”
Potential of quantum computing for drug discovery (2018, background): “…learning that is analogous to a Boltzmann machine uses the inherent property of sampling from a quantum circuit [156], and it has been demonstrated [137] on an ion trap quantum computer produced by IonQ, Inc. Quantum annealing devices are also useful for unsupervised learning because they enable…”
Hierarchical quantum classifiers (2018, background): “Originally proposed for quantum chemistry and combinatorial optimization, these approaches have been recently investigated for supervised [25] and unsupervised [26, 27] machine learning.”
Robust data encodings for quantum classifiers (2020, background): “[28] M.”
Variational quantum algorithms (2020, background): “[146], cat and coherent thermal states were generated experimentally in this way from specific classical data sets.”
Quantum-assisted quantum compiling (2018, methodology): “For instance, recent work [46] shows that gradient descent with momentum (GDM) using an adaptive (multiplicative) integration step update, called resilient backpropagation (rProp), can help with convergence.”
Quantum machine learning models are kernel methods (2021, background): “Throughout this manuscript I will interpret the expected measurement – or in practice, the average over measurement outcomes – as a prediction, but many results carry over to other settings, such as generative quantum models (e.g., [17]).”
Towards quantum machine learning with tensor networks (2018, neutral): “[29] Marcello Benedetti, Delfina Garcia-Pintos, Yunseong”