Batched Line Search Strategy for Navigating through Barren Plateaus in Quantum Circuit Training
arXiv:2402.05227 · doi:10.22331/q-2025-08-29-1841
Abstract
Variational quantum algorithms are viewed as promising candidates for demonstrating quantum advantage on near-term devices. These approaches typically involve the training of parameterized quantum circuits through a classical optimization loop. However, they often encounter challenges attributed to the exponentially diminishing gradient components, known as the barren plateau (BP) problem. This work introduces a novel optimization approach designed to alleviate the adverse effects of BPs during circuit training. In contrast to conventional gradient descent methods with a small learning parameter, our approach relies on making a finite hops along the search direction determined on a randomly chosen subsets of the free parameters. The optimization search direction, together with the range of the search, is determined by the distant features of the cost-function landscape. This enables the optimization path to navigate around barren plateaus without the need for external control mechanisms. We have successfully applied our optimization strategy to quantum circuits comprising 21 qubits and 15000 entangling gates, demonstrating robust resistance against BPs. Additionally, we have extended our optimization strategy by incorporating an evolutionary selection framework, enhancing its ability to avoid local minima in the landscape. The modified algorithm has been successfully utilized in quantum gate synthesis applications, showcasing a significantly improved efficiency in generating highly compressed quantum circuits compared to traditional gradient-based optimization approaches.
33 pages
References in corpus (48)
- A variational eigenvalue solver on a quantum processor
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Supervised learning with quantum enhanced feature spaces
- Barren plateaus in quantum neural network training landscapes
- The theory of variational hybrid quantum-classical algorithms
- Gapless Spin-Fluid Ground State in a Random Quantum Heisenberg Magnet
- Comments on the Sachdev-Ye-Kitaev model
- Quantum Chemistry in the Age of Quantum Computing
- Predicting Many Properties of a Quantum System from Very Few Measurements
- Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
- The Variational Quantum Eigensolver: a review of methods and best practices
- Hartree-Fock on a superconducting qubit quantum computer
- Towards Practical Quantum Variational Algorithms
- Connecting ansatz expressibility to gradient magnitudes and barren plateaus
- Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
- Training variational quantum algorithms is NP-hard
- qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor
- An initialization strategy for addressing barren plateaus in parametrized quantum circuits
- Layerwise learning for quantum neural networks
- Effect of barren plateaus on gradient-free optimization
- Structure optimization for parameterized quantum circuits
- Quantum circuit architecture search for variational quantum algorithms
- A perspective on the current state-of-the-art of quantum computing for drug discovery applications
- Sequential minimal optimization for quantum-classical hybrid algorithms
- Trainability of Dissipative Perceptron-Based Quantum Neural Networks
- Hardware-efficient variational quantum algorithms for time evolution
- Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
- Large gradients via correlation in random parameterized quantum circuits
- Entanglement Devised Barren Plateau Mitigation
- Application of fermionic marginal constraints to hybrid quantum algorithms
- Barren plateaus preclude learning scramblers
- Higher Order Derivatives of Quantum Neural Networks with Barren Plateaus
- On barren plateaus and cost function locality in variational quantum algorithms
- Efficient heat-bath sampling in Fock space
- Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets
- Barren plateaus in quantum tensor network optimization
- Eigenstate entanglement in the Sachdev-Ye-Kitaev model
- Genetic Algorithms for Digital Quantum Simulations
- Detecting and quantifying entanglement on near-term quantum devices
- Efficient variational synthesis of quantum circuits with coherent multi-start optimization
- Training variational quantum algorithms with random gate activation
- Molecular Quantum Circuit Design: A Graph-Based Approach
- Using Differential Evolution to avoid local minima in Variational Quantum Algorithms
- Quantum Energy Landscape and VQA Optimization
- Entanglement Diagnostics for Efficient Quantum Computation
- QFactor: A Domain-Specific Optimizer for Quantum Circuit Instantiation
- Highly optimized quantum circuits synthesized via data-flow engines
- Measurement-induced entanglement phase transitions in variational quantum circuits