Fock state-enhanced expressivity of quantum machine learning models

@article{Gan2021FockSE,
  title={Fock state-enhanced expressivity of quantum machine learning models},
  author={Beng Yee Gan and Daniel Leykam and D. G. Angelakis},
  journal={EPJ Quantum Technology},
  year={2021},
  volume={9},
  pages={1-23}
}
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… 

Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing

We introduce Perceval , an evolutive open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers, and describe its main features and components. Its

Strong Simulation of Linear Optical Processes

TLDR
An algorithm and general framework for the simulation of photons passing through linear optical interferometers that outperforms the na¨ıve method by an exponential factor, and for the restricted problem of computing the probability for one given output it matches the current state-of-the-art.

References

SHOWING 1-10 OF 108 REFERENCES

Experimental kernel-based quantum machine learning in finite feature space

TLDR
An all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems using specialized multiphoton quantum optical circuits exhibits exponentially better scaling in the required number of qubits than a direct generalization of kernels described in the literature.

Effect of data encoding on the expressive power of variational quantum-machine-learning models

TLDR
It is shown that there exist quantum models which can realise all possible sets of Fourier coefficients, and therefore, if the accessible frequency spectrum is asymptotically rich enough, such models are universal function approximators.

Quantum embeddings for machine learning

TLDR
This work proposes to train the first part of the circuit with the objective of maximally separating data classes in Hilbert space, a strategy it calls quantum metric learning, which provides a powerful analytic framework for quantum machine learning.

Quantum optical neural networks

TLDR
Through numerical simulation and analysis, the QONN is trained to perform a range of quantum information processing tasks, including newly developed protocols for quantum optical state compression, reinforcement learning, black-box quantum simulation, and one-way quantum repeaters.

Quantum machine learning with adaptive linear optics

TLDR
This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive process of computer programming called “seed-to-solar integration”.

Towards understanding the power of quantum kernels in the NISQ era

TLDR
This work proves that the advantage of quantum kernels is vanished for large size of datasets, few number of measurements, and large system noise and provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.

The power of quantum neural networks

TLDR
This work is the first to demonstrate that well-designed quantum neural networks offer an advantage over classical neural networks through a higher effective dimension and faster training ability, which is verified on real quantum hardware.

Large-scale quantum machine learning

TLDR
This work measures quantum kernels using randomized measurements to gain a quadratic speedup in computation time and quickly process large datasets and efficiently encode high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth.

Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces.

TLDR
This work proves that the machine learning models induced from the quantum-enhanced feature space are universal approximators of continuous functions under typical quantum feature maps, and enables an important theoretical analysis to ensure that machine learning algorithms based on quantum feature Maps can handle a broad class of machine learning tasks.

Power of data in quantum machine learning

TLDR
This work shows that some problems that are classically hard to compute can be easily predicted by classical machines learning from data and proposes a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.
...