Parallel Hybrid Networks: an interplay between quantum and classical neural networks

Kordzanganeh, Mohammad; Kosichkina, Daria; Melnikov, A. A.

doi:10.48550/arxiv.2303.03227

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…As discussed in [15], all quantum neural networks that use angle embedding 5 as their encoding strategy produces a truncated Fourier series approximation to the dataset. Schuld et al [15] also specifically explored two families of architectures of quantum neurons: a single-qubit architecture with a series of sequential SU (2) gates and a multi-qubit architecture with parallel SU(2) encoding gates.…”

Section: Background Review-linear Architecturesmentioning

confidence: 99%

“…It was argued [3] that such quantum neural networks (QNN) could have higher trainability, capacity, and generalization bound than their classical counterparts. Practically, hybrid quantum neural networks (HQNN) have shown promise in small-scale benchmarking tasks [4][5][6][7] and larger-scale industrial tasks [8][9][10]. Nevertheless, the utility, practicality, and scalability of pure QNNs are still unclear.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An exponentially-growing family of universal quantum circuits

Kordzanganeh

Sekatski

Fedichkin

et al. 2023

Mach. Learn.: Sci. Technol.

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Quantum machine learning has become an area of growing interest but has certain theoretical and hardware-specific limitations. Notably, the problem of vanishing gradients, or barren plateaus, renders the training impossible for circuits with high qubit counts, imposing a limit on the number of qubits that data scientists can use for solving problems. Independently, angle-embedded supervised quantum neural networks were shown to produce truncated Fourier series with a degree directly dependent on two factors: the depth of the encoding and the number of parallel qubits the encoding applied to. The degree of the Fourier series limits the model expressivity. This work introduces two new architectures whose Fourier degrees grow exponentially: the sequential and parallel exponential quantum machine learning architectures. This is done by efficiently using the available Hilbert space when encoding, increasing the expressivity of the quantum encoding. Therefore, the exponential growth allows staying at the low-qubit limit to create highly expressive circuits avoiding barren plateaus. Practically, parallel exponential architecture was shown to outperform the existing linear architectures by reducing their final mean square error value by up to 44.7% in a one-dimensional test problem. Furthermore, the feasibility of this technique was also shown on a trapped ion quantum processing unit.

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Section: Background Review-linear Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An exponentially-growing family of universal quantum circuits

Kordzanganeh

Sekatski

Fedichkin

et al. 2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The authors have also shown that the prediction of heart failure as the cause of death can be effectively and more precisely predicted with the help of quantum machine learning algorithms instead of classical ones. Among many existing quantum methods, quantum neural networks [27][28][29][30][31][32] are one of the most promising. For instance, in [33], the authors use a quantum generative adversarial neural network (GAN) with a hybrid generator to discover new drug molecules.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Quantum Neural Network for Drug Response Prediction

Sagingalieva

Kordzanganeh

Kenbayev

et al. 2023

Cancers

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Cancer is one of the leading causes of death worldwide. It is caused by various genetic mutations, which makes every instance of the disease unique. Since chemotherapy can have extremely severe side effects, each patient requires a personalized treatment plan. Finding the dosages that maximize the beneficial effects of the drugs and minimize their adverse side effects is vital. Deep neural networks automate and improve drug selection. However, they require a lot of data to be trained on. Therefore, there is a need for machine-learning approaches that require less data. Hybrid quantum neural networks were shown to provide a potential advantage in problems where training data availability is limited. We propose a novel hybrid quantum neural network for drug response prediction based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers. We test our model on the reduced Genomics of Drug Sensitivity in Cancer dataset and show that the hybrid quantum model outperforms its classical analog by 15% in predicting IC50 drug effectiveness values. The proposed hybrid quantum machine learning model is a step towards deep quantum data-efficient algorithms with thousands of quantum gates for solving problems in personalized medicine, where data collection is a challenge.

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“…[13,14] Unlike their classical counterparts, QNNs are able to learn a generalized model of a dataset from a substantially smaller training set [15][16][17] and typically have the potential to do so with polynomially or exponentially simpler models. [18][19][20] Thus, they provide a promising opportunity to subvert the scaling problem encountered in classical machine learning, [5,[21][22][23][24][25][26][27] which presents a serious challenge for data-intensive problems that are increasingly bottle-necked by hardware limitations. [28][29][30] Nonetheless, even for a small dataset, training QNNs requires on the order of a million circuit evaluations.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms

Kordzanganeh,

Buchberger,

Kyriacou

et al. 2023

Adv Quantum Tech

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Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms. A robust large‐scale study of how the performance of these platforms scales with the number of qubits is key to providing quantum solutions to challenging industry problems. This work benchmarks the runtime and accuracy for a representative sample of specialized high‐performance simulated and physical quantum processing units. Results show the QMware simulator can reduce the runtime for executing a quantum circuit by up to 78% compared to the next fastest option for algorithms with fewer than 27 qubits. The Amazon Web Service State‐Vector Simulator 1 offers a runtime advantage for larger circuits, up to the maximum 34 qubits. Beyond this limit, QMware can execute circuits as large as 40 qubits. Physical quantum devices, such as Rigetti's Aspen‐M2, can provide an exponential runtime advantage for circuits with more than 30 qubits. However, the high financial cost of physical quantum processing units presents a serious barrier to practical use. Moreover, only IonQ's Harmony quantum device achieves high fidelity with more than four qubits. This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.

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Parallel Hybrid Networks: an interplay between quantum and classical neural networks

Cited by 3 publications

References 18 publications

An exponentially-growing family of universal quantum circuits

An exponentially-growing family of universal quantum circuits

Hybrid Quantum Neural Network for Drug Response Prediction

Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms

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