A hybrid machine-learning algorithm for designing quantum experiments

O'Driscoll, L.; Nichols, Ralph C.; Knott, P. A.

doi:10.48550/arxiv.1812.03183

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…There are other QML approaches that could be used to optimize a quantum circuit. One approach uses classical machine learning to optimize the design of a quantum experiment in order to produce certain defined states [14]. Another QML approach consists of optimizing quantum circuits to improve the solution to some problems solved on a quantum computer [15].…”

Section: ۧ ψmentioning

confidence: 99%

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

et al. 2019

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We report on experimental implementation of a machine-learned quantum gate driven by a classical control. The gate learns optimal phase-covariant cloning in a reinforcement learning scenario having fidelity of the clones as reward. In our experiment, the gate learns to achieve nearly optimal cloning fidelity allowed for this particular class of states. This makes it a proof of present-day feasibility and practical applicability of the hybrid machine learning approach combining quantum information processing with classical control. Moreover, our experiment can be directly generalized to larger interferometers where the computational cost of classical computer is much lower than the cost of boson sampling.

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Section: ۧ ψmentioning

confidence: 99%

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

et al. 2019

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“…A very high fidelity target state can be obtained with a substantially enhanced success probability over previous methods [42]. Another machine learning method using a genetic algorithm and allowing for certain non-Gaussian input states was also recently investigated [43]. In this paper, we present a thorough study of the conditional generation of non-Gaussian states by measuring multimode Gaussian states via PNR detectors.…”

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confidence: 99%

Conversion of Gaussian states to non-Gaussian states using photon-number-resolving detectors

2019

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Generation of high fidelity photonic non-Gaussian states is a crucial ingredient for universal quantum computation using continous-variable platforms, yet it remains a challenge to do so efficiently. We present a general framework for a probabilistic production of multimode non-Gaussian states by measuring few modes of multimode Gaussian states via photon-number-resolving detectors. We use Gaussian elements consisting of squeezed displaced vacuum states and interferometers, the only non-Gaussian elements consisting of photon-number-resolving detectors. We derive analytic expressions for the output Wigner function, and the probability of generating the states in terms of the mean and the covariance matrix of the Gaussian state and the photon detection pattern. We find that the output states can be written as a Fock basis superposition state followed by a Gaussian gate, and we derive explicit expressions for these parameters. These analytic expressions show exactly what non-Gaussian states can be generated by this probabilistic scheme. Further, it provides a method to search for the Gaussian circuit and measurement pattern that produces a target non-Gaussian state with optimal fidelity and success probability. We present specific examples such as the generation of cat states, ON states, Gottesman-Kitaev-Preskill states, NOON states and bosonic code states. The proposed framework has potential far-reaching implications for the generation of bosonic error-correction codes that require non-Gaussian states, resource states for the implementation of non-Gaussian gates needed for universal quantum computation, among other applications requiring non-Gaussianity. The tools developed here could also prove useful for the quantum resource theory of non-Gaussianity. Contents 25A. From integration to derivative 26 B. Measuring subsystems of three-mode Gaussian states 26 C. Measuring subsystems of four-mode Gaussian states 28 D. Measuring subsystems of five-mode Gaussian states 29

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“…Therefore, with simple modifications AdaQuantum can, for example, search for non-Gaussian states [28,29], states for multi-parameter estimation [30][31][32][33], or states with certain non-classical properties [34][35][36]. In this paper we focus on fitness functions for quantum-metrology applications (see next section), and in a spin-off project we have already designed fitness functions -and then used AdaQuantum to design experiments -to produce states with a high fidelity to a range of target states, such as cat states [37].…”

Section: Designing Adaquantum For Flexibilitymentioning

confidence: 99%

Designing quantum experiments with a genetic algorithm

Nichols¹,

Mineh²,

Rubio³

et al. 2019

Quantum Sci. Technol.

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We introduce a genetic algorithm that designs quantum optics experiments for engineering quantum states with specific properties. Our algorithm is powerful and flexible, and can easily be modified to find methods of engineering states for a range of applications. Here we focus on quantum metrology. First, we consider the noise-free case, and use the algorithm to find quantum states with a large quantum Fisher information (QFI). We find methods, which only involve experimental elements that are available with current or near-future technology, for engineering quantum states with up to a 100-fold improvement over the best classical state, and a 20-fold improvement over the optimal Gaussian state. Such states are a superposition of the vacuum with a large number of photons (around 80), and can hence be seen as Schrödinger-cat-like states. We then apply the two most dominant noise sources in our setting -photon loss and imperfect heralding -and use the algorithm to find quantum states that still improve over the optimal Gaussian state with realistic levels of noise. This will open up experimental and technological work in using exotic non-Gaussian states for quantum-enhanced phase measurements. Finally, we use the Bayesian mean square error to look beyond the regime of validity of the QFI, finding quantum states with precision enhancements over the alternatives even when the experiment operates in the regime of limited data.PACS numbers:

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A hybrid machine-learning algorithm for designing quantum experiments

Cited by 3 publications

References 28 publications

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

Conversion of Gaussian states to non-Gaussian states using photon-number-resolving detectors

Designing quantum experiments with a genetic algorithm

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