Experimentally Realizable Continuous-variable Quantum Neural Networks

Bangar, Shikha; Siopsis, George; Yeter-Aydeniz, Kübra

doi:10.1364/quantum.2022.qtu2a.4

Cited by 2 publications

(6 citation statements)

References 30 publications

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“…It should be pointed out that the introduction of an ancilla qumode is similar to the training of CV Quantum Neural Networks (QNN) that are experimentally realizable, as we discussed in our previous work [16]. Here we aim at a similar construction that would yield experimentally realizable CVQBM.…”

Section: The Methodsmentioning

confidence: 96%

“…Next, we apply our CVQBM to quantum data. We consider two cases: a Gaussian distribution and a non-Gaussian distribution (cat state) both of which are generated approximately by a quantum neural network (QNN) [16].…”

Section: Quantum Data Probability Distribution Generationmentioning

confidence: 99%

“…which is the fidelity to be used in the definition of the cost function (15). For the preparation of quantum data, we relied on our previous work on experimentally realizable CV QNNs [16]. More specifically, we used the circuits for state preparation to prepare a Gaussian and non-Gaussian state approximately.…”

Section: Quantum Data Probability Distribution Generationmentioning

confidence: 99%

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Continuous-variable Quantum Boltzmann Machine

Bangar,

Sunny,

Yeter-Aydeniz

et al. 2024

Preprint

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We propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. It can be realized experimentally on a continuous-variable (CV) photonic quantum computer. We used a CV quantum imaginary time evolution (QITE) algorithm to prepare the essential thermal state and then designed the CVQBM to proficiently generate continuous probability distributions. We applied our method to both classical and quantum data. Using real-world classical data, such as synthetic aperture radar (SAR) images, we generated probability distributions. For quantum data, we used the output of CV quantum circuits. We obtained high fidelity and low Kuller-Leibler (KL) divergence showing that our CVQBM learns distributions from given data well and generates data sampling from that distribution efficiently. We also discussed the experimental feasibility of our proposed CVQBM. Our method can be applied to a wide range of real-world problems by choosing an appropriate target distribution (corresponding to, e.g., SAR images, medical images, and risk management in finance). Moreover, our CVQBM is versatile and could be programmed to perform tasks beyond generation, such as anomaly detection.

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Section: The Methodsmentioning

confidence: 96%

Section: Quantum Data Probability Distribution Generationmentioning

confidence: 99%

Section: Quantum Data Probability Distribution Generationmentioning

confidence: 99%

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Continuous-variable Quantum Boltzmann Machine

Bangar,

Sunny,

Yeter-Aydeniz

et al. 2024

Preprint

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“…Another advantage of using continuous variable architectures is that they are easily implementable experimentally, for example using photonic quantum computers. A recent proposal with a focus on the experimental implementation is given for example in [27], while extensive reviews on this topic can also be found in [1,28].…”

Section: A Continuous Quantum Perceptron Modelmentioning

confidence: 99%

“…Firstly, in this appendix, we keep the input bias m in fixed and let mout → 1 − ; then, in the next one we treat the case when m in = mout = m → 1 − . Consider the equation with a±(M) as in (27). If mout → 1 − and m in is kept fixed, M must diverge to +∞.…”

Section: Appendix B Large Output Bias Limitmentioning

confidence: 99%

On the capacity of a quantum perceptron for storing biased patterns

Benatti,

Gramegna,

Mancini

et al. 2023

J. Phys. A: Math. Theor.

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Although different architectures of quantum perceptrons have been recently put forward, the capabilities of such quantum devices versus their classical counterparts remain debated. Here, we consider random patterns and targets independently distributed with biased probabilities and investigate the storage capacity of a continuous quantum perceptron model that admits a classical limit, thus facilitating the comparison of performances. Such a more general context extends a previous study of the quantum storage capacity where using statistical mechanics techniques in the limit of a large number of inputs, it was proved that no quantum advantages are to be expected concerning the storage properties. This outcome is due to the fuzziness inevitably introduced by the intrinsic stochasticity of quantum devices. We strengthen such an indication by showing that the possibility of indefinitely enhancing the storage capacity for highly correlated patterns, as it occurs in a classical setting, is instead prevented at the quantum level.

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Experimentally Realizable Continuous-variable Quantum Neural Networks

Abstract: We build a continuous variable hybrid quantum-classical neural network using only Gaussian gates. We achieve non-linearity through measurements on ancillary qumodes. Our protocol can be realized experimentally with current photonic technology.

Cited by 2 publications

References 30 publications

Continuous-variable Quantum Boltzmann Machine

Continuous-variable Quantum Boltzmann Machine

On the capacity of a quantum perceptron for storing biased patterns

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