Nonclassical kernels in continuous-variable systems

Ghobadi, Roohollah

doi:10.1103/physreva.104.052403

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…The latter results discussed above highlight the utility of photonics in quantum information. In fact, optically encoded quantum information presents itself in many machine learning examples [28][29][30][31][32][33]. The popularity of photonic quantum information stems from the photon's versatility given its numerous and highly controllable degrees of freedom [34].…”

Section: Introductionmentioning

confidence: 99%

Quantum kernel evaluation via Hong–Ou–Mandel interference

Bowie,

Shrapnel,

Kewming

2023

Quantum Sci. Technol.

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One of the fastest growing areas of interest in quantum computing is its use within machine learning methods, in particular through the application of quantum kernels. Despite this large interest, there exist very few proposals for relevant physical platforms to evaluate quantum kernels. In this article, we propose and simulate a protocol capable of evaluating quantum kernels using Hong-Ou-Mandel (HOM) interference, an experimental technique that is widely accessible to optics researchers. Our proposal utilises the orthogonal temporal modes of a single photon, allowing one to encode multi-dimensional feature vectors. As a result, interfering two photons and using the detected coincidence counts, we can perform a direct measurement and binary classification. This physical platform confers an exponential quantum advantage also described theoretically in other works. We present a complete description of this method and perform a numerical experiment to demonstrate a sample application for binary classification of classical data.

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Section: Introductionmentioning

confidence: 99%

Quantum kernel evaluation via Hong–Ou–Mandel interference

Bowie,

Shrapnel,

Kewming

2023

Quantum Sci. Technol.

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“…The combination of ideas from the photonic and quantum machine learning communities may enable further speed-ups and novel functionalities [12][13][14][15][16][17][18][19]. For example, both classical and quantum photonic neural networks are presently limited by the difficulty of incorporating nonlinear activation functions.…”

Section: Introductionmentioning

confidence: 99%

Fock state-enhanced expressivity of quantum machine learning models

Gan

Leykam

Angelakis

2022

EPJ Quantum Technol.

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The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding scheme that embeds classical data points using fewer encoding layers and circumventing the need for nonlinear optical components by mapping the data points into the high-dimensional Fock space. The expressive power of the circuit can be controlled via the number of input photons. Our work sheds some light on the unique advantages offered by quantum photonics on the expressive power of quantum machine learning models. By leveraging the photon-number dependent expressive power, we propose three different noisy intermediate-scale quantum-compatible binary classification methods with different scaling of required resources suitable for different supervised classification tasks.

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