Quantum Circuit Evolution on NISQ Devices

Franken, Lukas; Georgiev, Bogdan; Sascha, Mücke,; Heese, Raoul; Bauckhage, Christian; Piatkowski, Nico

doi:10.1109/cec55065.2022.9870269

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Powerful optimization approaches for reservoir noise are offered by dual annealing and evolutionary optimization (EO). EO is capable of optimizing quantum systems at various levels, such as quantum circuit parameters, successfully realized in this work and in previous works [24][25][26] and quantum circuit architecture. Here we use a previously successful EO algorithm 27 in which model parameters were evolved for quantum reinforcement learning agents in a hybrid quantum-classical neural network approach.…”

Section: Optimizing Quantum Noise-induced Reservoir Computing For Non...mentioning

confidence: 64%

Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction

Fry,

Deshmukh,

Chen

et al. 2023

Sci Rep

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Quantum reservoir computing is strongly emerging for sequential and time series data prediction in quantum machine learning. We make advancements to the quantum noise-induced reservoir, in which reservoir noise is used as a resource to generate expressive, nonlinear signals that are efficiently learned with a single linear output layer. We address the need for quantum reservoir tuning with a novel and generally applicable approach to quantum circuit parameterization, in which tunable noise models are programmed to the quantum reservoir circuit to be fully controlled for effective optimization. Our systematic approach also involves reductions in quantum reservoir circuits in the number of qubits and entanglement scheme complexity. We show that with only a single noise model and small memory capacities, excellent simulation results were obtained on nonlinear benchmarks that include the Mackey-Glass system for 100 steps ahead in the challenging chaotic regime.

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Section: Optimizing Quantum Noise-induced Reservoir Computing For Non...mentioning

confidence: 64%

Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction

Fry,

Deshmukh,

Chen

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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“…The normalized root-mean-square error (NRMSE) is defined NRMSE = 1 T t (y i − ŷi ) 2 σ(y) (30) where σ(y) is the sample standard deviation of the true values. The mean absolute scaled error (MASE) forecasting metric is defined…”

Section: A Metricsmentioning

confidence: 99%

“…Evolutionary optimization (EO) can be used to optimize various quantum computing components at the various levels. For example, the use of EO in optimizing quantum circuit architecture or quantum circuit parameters can be found in these recent works [29][30][31]. A particular EO algorithm is used in this work due to previous success [32], in which model parameters were evolved for quantum reinforcement learning agents in a hybrid quantumclassical neural network approach.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Quantum Noise-induced Reservoir Computing for Nonlinear and Chaotic Time Series Prediction

Fry¹,

Deshmukh²,

Chen³

et al. 2023

Preprint

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Quantum reservoir computing (QRC) has been strongly emerging as a time series prediction approach in quantum machine learning (QML). This work is the first to apply optimization to resource noise in a quantum reservoir to effectively improve time series prediction. Based on this development, we propose a new approach to quantum circuit optimization.We build on the work of Suzuki et al., who used quantum hardware noise as an essential resource in quantum noise-induced reservoir (QNIR) computer for generating non-trivial output sequences, and we achieve a novel, optimized QNIR, in which the artificial noise channels are parameterized. To optimize the parameterized resource noise, we use dual annealing and evolutionary optimization. Another essential component of our approach is reducing quantum resources in the number of artificial noise models, number of qubits, entanglement scheme complexity, and circuit depth. A key result is the reduction from a general multi-component noise model to a single reset noise model.Reservoir computers are especially well-suited for modelling nonlinear dynamical systems. In this paper we consider NARMA and Mackey-Glass systems, which are common univariate time series benchmarks for reservoir computers and recurrent neural networks. Recently QRCs have demonstrated good prediction capability with small numbers of qubits. QNIR simulations based on our optimization approach demonstrate high performance on NARMA benchmarks while using only a 12-qubit reservoir and a single noise model. Good prediction performances over 100 timesteps ahead for the Mackey-Glass system are demonstrated in the chaotic regime. In addition, these results provide valuable insight into resource noise requirements for the QNIR.

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