Generative quantum learning of joint probability distribution functions

Zhu, Elton Yechao; Johri, Sonika; Bacon, Dave; Esencan, Mert; Kim, Jungsang; Muir, Mark; Murgai, Nikhil; Nguyen, Jason; Pisenti, Neal C.; Schouela, Adam; Sosnova, Ksenia; Wright, Ken

doi:10.1103/physrevresearch.4.043092

Cited by 15 publications

(5 citation statements)

References 48 publications

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“…IonQ has recently introduced its next-generation quantum hardware [45]. This hardware is purported to feature an order of magnitude reduction in gate error rates in comparison to IonQ's current system on the cloud.…”

Section: Ionqmentioning

confidence: 99%

Application-Oriented Performance Benchmarks for Quantum Computing

Lubinski

Johri

Varosy

et al. 2023

IEEE Trans. Quantum Eng.

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In this work we introduce an open source suite of quantum application-oriented performance benchmarks that is designed to measure the effectiveness of quantum computing hardware at executing quantum applications. These benchmarks probe a quantum computer's performance on various algorithms and small applications as the problem size is varied, by mapping out the fidelity of the results as a function of circuit width and depth using the framework of volumetric benchmarking. In addition to estimating the fidelity of results generated by quantum execution, the suite is designed to benchmark certain aspects of the execution pipeline in order to provide end-users with a practical measure of both the quality of and the time to solution. Our methodology is constructed to anticipate advances in quantum computing hardware that are likely to emerge in the next five years. This benchmarking suite is designed to be readily accessible to a broad audience of users and provides benchmarks that correspond to many well-known quantum computing algorithms.CONTENTS * This work was sponsored by the Quantum Economic Development Consortium (QED-C) and was performed under the auspices of the QED-C Technical Advisory Committee on Standards and Performance Benchmarks. The authors acknowledge many committee members for their input to and feedback on the project and this manuscript.

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Section: Ionqmentioning

confidence: 99%

Application-Oriented Performance Benchmarks for Quantum Computing

Lubinski

Johri

Varosy

et al. 2023

IEEE Trans. Quantum Eng.

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show abstract

“…For this type of architecture, there exists a variant, first discussed in [15], referred to as copula architecture,U i , which naturally respects the properties of PIT data, i.e., data transformed via the PIT, for details see the appendix, Sec. VII A.…”

Section: Discretementioning

confidence: 99%

“…In Fig. 7, we show the copula architecture used in this work, which is the same proposed in [15]. As before, qubits q 0 to q 3 represent variable x 1 whereas qubits q 4 to q 7 represent variable x 2 .…”

Section: Discretementioning

confidence: 99%

“…In Figs. 15 and 16, we present the training curves for all QGAN experiments we ran using the discrete architectures. Note that for most of the cases, training using TABLE III: Resource estimation per generated sample and comparison among all quantum circuits used in this work for d-dimensional data.…”

Section: Resource Summarymentioning

confidence: 99%

“…The former learns and produces samples from a discretized sampling space, while the latter can produce continuous-valued samples. Moreover, as described by Zhu et al [15], quantum information theory allows for an efficient representation of the copula [16] of arbitrary probability distributions, which has the potential to simplify the learning process of complicated probability distributions. That is why, in our tests, the discrete architectures are additionally divided into copula and standard variants.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A performance characterization of quantum generative models

Riofrio¹,

Mitevski²,

Jones³

et al. 2023

Preprint

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Quantum generative modeling is a growing area of interest for industry-relevant applications. With the field still in its infancy, there are many competing techniques. This work is an attempt to systematically compare a broad range of these techniques to guide quantum computing practitioners when deciding which models and techniques to use in their applications. We compare fundamentally different architectural ansatzes of parametric quantum circuits used for quantum generative modeling: 1. A continuous architecture, which produces continuous-valued data samples, and 2. a discrete architecture, which samples on a discrete grid. We compare the performance of different data transformations: normalization by the min-max transform or by the probability integral transform. We learn the underlying probability distribution of the data sets via two popular training methods: 1. quantum circuit Born machines (QCBM), and 2. quantum generative adversarial networks (QGAN). We study their performance and trade-offs as the number of model parameters increases, with the baseline of similarly trained classical neural networks. The study is performed on six low-dimensional synthetic and two real financial data sets. Our two key findings are that: 1. For all data sets, our quantum models require similar or fewer parameters than their classical counterparts. In the extreme case, the quantum models require two of orders of magnitude less parameters. 2. We empirically find that a variant of the discrete architecture, which learns the copula of the probability distribution, outperforms all other methods.

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Rapid training of quantum recurrent neural networks

Michał

Buraczewski

Saux

et al. 2023

Quantum Mach. Intell.

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Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness recurrent neural networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus, time and energy consuming. Here, we propose to extend the concept of RRNs by including continuous-variable quantum resources in it and to use a quantum-enhanced RNN to overcome these obstacles. The design of the continuous-variable quantum RNN (CV-QRNN) is rooted in the continuous-variable quantum computing paradigm. By performing extensive numerical simulations, we demonstrate that the quantum network is capable of learning-time dependence of several types of temporal data and that it converges to the optimal weights in fewer epochs than a classical network. Furthermore, for a small number of trainable parameters, it can achieve lower losses than its classical counterpart. CV-QRNN can be implemented using commercially available quantum-photonic hardware.

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Generative quantum learning of joint probability distribution functions

Cited by 15 publications

References 48 publications

Application-Oriented Performance Benchmarks for Quantum Computing

Application-Oriented Performance Benchmarks for Quantum Computing

A performance characterization of quantum generative models

Rapid training of quantum recurrent neural networks

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