Improving Trainability of Variational Quantum Circuits via ...

Improving Trainability of Variational Quantum Circuits via ... - arXiv

In this work, we propose a strategy that regularizes model parameters with prior knowledge of the train data and Gaussian noise diffusion.

Improving Trainability of Variational Quantum Circuits ... - Inspire HEP

Similar to classic models, regular VQCs can be optimized by various gradient-based methods. However, the optimization may be initially trapped ...

Improving Trainability of Variational Quantum Circuits via ... - Linnk AI

Regularizing model parameters with prior knowledge of training data and Gaussian noise diffusion can improve the trainability of variational quantum ...

Trainability enhancement of parameterized quantum circuits via ...

... parameter initialization strategy, offering insights to enhance the trainability and convergence of variational quantum algorithms.

Improving Trainability of Variational Quantum Circuits via ... - Synthical

In the era of noisy intermediate-scale quantum (NISQ), variational quantum circuits (VQCs) have been widely applied in various domains, advancing the ...

Efficient Estimation of Trainability for Variational Quantum Circuits

Since a generic quantum circuit cannot be simulated efficiently, the determination of its trainability is an important problem. Here we find an ...

Can Error Mitigation Improve Trainability of Noisy Variational ...

Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that ...

Trainability Enhancement of Parameterized Quantum Circuits via ...

Our results highlight the significance of an appropriate parameter initialization strategy and can be used to enhance the trainability of PQCs in variational ...

An Improved Neural Network Approach for Barren Plateau Mitigation

Combining classical optimization with parameterized quantum circuit evaluation, variational quantum algorithms (VQAs) are among the most ...

Can Error Mitigation Improve Trainability of Noisy Variational ...

Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can ...

Trainability Enhancement of Parameterized Quantum Circuits via ...

Our results highlight the significance of an appropriate parameter initialization strategy, offering insights to enhance the trainability and ...

Synergistic pretraining of parametrized quantum circuits via tensor ...

This work introduces a synergistic training framework for quantum algorithms, which employs classical tensor network simulations towards ...

A Study on Optimization Techniques for Variational Quantum ...

This paper investigates methods to enhance the trainability of Variational Quantum Circuits (VQCs) for reinforcement learning, with a focus on ...

EVOVAQ: EVOlutionary algorithms-based toolbox for VAriational ...

Evolutionary Algorithms (EAs) are becoming increasingly popular for training Variational Quantum Circuits (VQCs) due to their ability to conserve quantum ...

Training of quantum circuits on a hybrid quantum computer - PMC

Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The ...

Improving Variational Quantum Optimization using CVaR

Hybrid quantum/classical variational algorithms can be implemented on noisy intermediate-scale quantum computers and can be used to find ...

Optimal training of variational quantum algorithms without barren ...

This work identifies a VQA for quantum simulation with such a constraint that thus can be trained free of barren plateaus and introduces the generalized ...

On the relation between trainability and dequantization of variational ...

We prove that a classical-to-quantum transfer learning architecture using a Variational Quantum Circuit (VQC) improves the representation and ...

Alternating Layered Variational Quantum Circuits Can Be ...

In this work, we introduce a training algorithm with an exponential reduction in training cost of such VQAs. Moreover, our algorithm uses ...

[PDF] Improving Variational Quantum Optimization using CVaR

This paper empirically shows that the Conditional Value-at-Risk as an aggregation function leads to faster convergence to better solutions for all ...