An improved differential evolution algorithm for learning high-fidelity quantum controls

Yang, Xiaodong; Li, Jun; Peng, Xinhua

doi:10.1016/j.scib.2019.07.013

Cited by 40 publications

(19 citation statements)

References 33 publications

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“…We note that a very simple learning algorithm in the optimization of the tunnelling amplitudes was used. The application of more advanced evolutionary algorithms in quantum control would likely lead to improved results for our system [67].…”

Section: Discussionmentioning

confidence: 99%

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

Ghosh

Krisnanda

Paterek

et al. 2021

Preprint

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Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. It induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, greatly reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.

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Section: Discussionmentioning

confidence: 99%

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

Ghosh

Krisnanda

Paterek

et al. 2021

Preprint

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“…However, it has few issues such as convergence rate and local exploitation ability. In order to overcome its shortcomings, lots of robust and effective DE has been designed in the literature like IDE, 18 GRCDE, 19 UDE, 20 EFADE, 21 MMDE, 22 DEPS, 23 EDE and EBDE, 24 AGDE, 25 EAGDE, 26 daDE, 27 DE with biological-based mutation operator, 28 HDEMCO, 29 MRDE, 30 ADEwSE, 31 EJADE, 32 BADE, 33 and ADE. 34 Also, PSO has attracted attention to solve many complex optimization problems due to its efficient search ability and simplicity.…”

Section: Related Workmentioning

confidence: 99%

An advanced hybrid algorithm for nonlinear function optimization with real world applications

Verma

Parouha

2021

Concurrency and Computation

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This article presents an advanced hybrid algorithm (haDEPSO) for nonlinear constrained function optimization problem. It consists of the advised advanced differential evolution, that is, aDE (wherein a novel mutation, crossover, and selection strategy is introduced, to avoid premature convergence) and particle swarm optimization, namely, aPSO (in which novel gradually varying parameters is utilized, to escape stagnation).The proposed haDEPSO achieve better results as aDE and aPSO provides diverse convergence characteristic to the solution space. Moreover, in haDEPSO distinct population is merged with other in a predefined way, to balance between exploration and exploitation. Efficiency of the proposed hybrid haDEPSO along with its integrating component aDE and aPSO are inspected on IEEE CEC'2006 constrained benchmark functions and IEEE CEC'2011 real world problems. According to the comparison results, the proposed methods achieved better results than traditional DE and PSO with their hybrids as well as over many state-of-the-art algorithms.

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“…Optimization is typically formulated as maximizing or minimizing certain cost functions. It is widely used in science and engineering such as the training of neural network [1][2][3], EPR Steering [4], state preparation [5][6][7][8], quantum data compression [9], quantum circuit designs [10], quantum control [11,12], emergent Schrödinger equation [13], device fabrication [14] and parameter optimization in quantum secure direct communication [15], and so on. As technology develops, database size grows explosively.…”

Section: Introductionmentioning

confidence: 99%

Quantum second-order optimization algorithm for general polynomials

Gao

Li²,

Wei

et al. 2021

Sci. China Phys. Mech. Astron.

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Quantum optimization algorithms can outperform their classical counterpart and are key in modern technology. The second-order optimization algorithm (the Newton algorithm) is a critical optimization method, speeding up the convergence by employing the second-order derivative of loss functions in addition to their first derivative. Here, we propose a new quantum second-order optimization algorithm for general polynomials with a computational complexity of O(poly(log d)). We use this algorithm to solve the nonlinear equation and learning parameter problems in factorization machines. Numerical simulations show that our new algorithm is faster than its classical counterpart and the first-order quantum gradient descent algorithm. While existing quantum Newton optimization algorithms apply only to homogeneous polynomials, our new algorithm can be used in the case of general polynomials, which are more widely present in real applications.

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An improved differential evolution algorithm for learning high-fidelity quantum controls

Cited by 40 publications

References 33 publications

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

An advanced hybrid algorithm for nonlinear function optimization with real world applications

Quantum second-order optimization algorithm for general polynomials

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