Markovian Quantum Neuroevolution for Machine Learning

Lu, Zhide; Shen, Pei-Xin; Deng, Dong-Ling

doi:10.1103/physrevapplied.16.044039

Cited by 26 publications

(17 citation statements)

References 67 publications

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“…We use the neuroevolution method [58] to find a suitable variational circuit architecture. The elementary gates used in our experiments are single qubit rotation gates (X, Y, Z) and control-rotation gate along z axis.…”

Section: Details Of Tebd Methodsmentioning

confidence: 99%

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Observation of a symmetry-protected topological time crystal with superconducting qubits

Zhang,

Jiang,

Deng

et al. 2021

Preprint

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We report the observation of a symmetry-protected topological time crystal, which is implemented with an array of programmable superconducting qubits. Unlike the time crystals reported in previous experiments, where spontaneous breaking of the discrete time translational symmetry occurs for local observables throughout the whole system, the topological time crystal observed in our experiment breaks the time translational symmetry only at the boundaries and has trivial dynamics in the bulk. More concretely, we observe robust long-lived temporal correlations and sub-harmonic temporal response for the edge spins up to 40 driving cycles. We demonstrate that the sub-harmonic response is independent of whether the initial states are random product states or symmetry-protected topological states, and experimentally map out the phase boundary between the time crystalline and thermal phases. Our work paves the way to exploring peculiar non-equilibrium phases of matter emerged from the interplay between topology and localization as well as periodic driving, with current noisy intermediate-scale quantum processors.

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Section: Details Of Tebd Methodsmentioning

confidence: 99%

“…quantum circuits with depth equal to one. We construct the direct graph of those layers consisting of those elementary gates with respect to the rules in [58]. Then, a quantum circuit can be represented as a path in this graph.…”

Section: Details Of Tebd Methodsmentioning

confidence: 99%

Observation of a symmetry-protected topological time crystal with superconducting qubits

Zhang,

Jiang,

Deng

et al. 2021

Preprint

Self Cite

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“…In particular, we focus on a one-dimensional (1D) time-periodic Hamiltonian with three-body interactions and symmetry as an example. We digitally simulate this Hamiltonian through a large-depth quantum circuit obtained using a neuroevolution algorithm 46 . We then measure local spin magnetizations and their temporal correlations and demonstrate that both quantities show a subharmonic response at the boundaries but not in the bulk of the chain.…”

Section: Mainmentioning

confidence: 99%

Digital quantum simulation of Floquet symmetry-protected topological phases

Zhang

Jiang

Deng

et al. 2022

Nature

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Quantum many-body systems away from equilibrium host a rich variety of exotic phenomena that are forbidden by equilibrium thermodynamics. A prominent example is that of discrete time crystals1–8, in which time-translational symmetry is spontaneously broken in periodically driven systems. Pioneering experiments have observed signatures of time crystalline phases with trapped ions9,10, solid-state spin systems11–15, ultracold atoms16,17 and superconducting qubits18–20. Here we report the observation of a distinct type of non-equilibrium state of matter, Floquet symmetry-protected topological phases, which are implemented through digital quantum simulation with an array of programmable superconducting qubits. We observe robust long-lived temporal correlations and subharmonic temporal response for the edge spins over up to 40 driving cycles using a circuit of depth exceeding 240 and acting on 26 qubits. We demonstrate that the subharmonic response is independent of the initial state, and experimentally map out a phase boundary between the Floquet symmetry-protected topological and thermal phases. Our results establish a versatile digital simulation approach to exploring exotic non-equilibrium phases of matter with current noisy intermediate-scale quantum processors21.

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“…Thereby in the following numerical experiments, we propose three kinds of QCap-sNets with different sub-QNNs, and then benchmark their performance by the classification accuracy. The first sub-QNN is the parameterized quantum circuit (PQC), which has been widely used as a standard ansatz for quantum classifiers [57][58][59][60][61][62][63]. The second one is the deep quantum feed-forward neural network (DQFNN), which has been proposed to solve the supervised learning problem [41] and the dynamics of quantum open system [64].…”

Section: Numerical Experiments a Performance Benchmarkingmentioning

confidence: 99%

Quantum Capsule Networks

Zidu¹,

Shen²,

Li³

et al. 2022

Preprint

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Capsule networks, which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence. The capsule, as the building block of capsule networks, is a group of neurons represented by a vector to encode different features of an entity. The information is extracted hierarchically through capsule layers via routing algorithms. Here, we introduce a quantum capsule network (dubbed QCap-sNet) together with a quantum dynamic routing algorithm. Our model enjoys an exponential speedup in the dynamic routing process and exhibits an enhanced representation power. To benchmark the performance of the QCapsNet, we carry out extensive numerical simulations on the classification of handwritten digits and symmetry-protected topological phases, and show that the QCapsNet can achieve the state-of-the-art accuracy and outperforms conventional quantum classifiers evidently. We further unpack the output capsule state and find that a particular subspace may correspond to a human-understandable feature of the input data, which indicates the potential explainability of such networks. Our work reveals an intriguing prospect of quantum capsule networks in quantum machine learning, which may provide a valuable guide towards explainable quantum artificial intelligence.

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Markovian Quantum Neuroevolution for Machine Learning

Cited by 26 publications

References 67 publications

Observation of a symmetry-protected topological time crystal with superconducting qubits

Observation of a symmetry-protected topological time crystal with superconducting qubits

Digital quantum simulation of Floquet symmetry-protected topological phases

Quantum Capsule Networks

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