Quantum Dropout: On and Over the Hardness of Quantum Approximate Optimization Algorithm
arXiv:2203.10101 · doi:10.1103/PhysRevResearch.5.023171
Abstract
A combinatorial optimization problem becomes very difficult in situations where the energy landscape is rugged, and the global minimum locates in a narrow region of the configuration space. When using the quantum approximate optimization algorithm (QAOA) to tackle these harder cases, we find that difficulty mainly originates from the QAOA quantum circuit instead of the cost function. To alleviate the issue, we selectively dropout the clauses defining the quantum circuit while keeping the cost function intact. Due to the combinatorial nature of the optimization problems, the dropout of clauses in the circuit does not affect the solution. Our numerical results confirm improvements in QAOA's performance with various types of quantum-dropout implementation.
arXiv comment15 pages, 16 figures
References in corpus (26)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- A variational eigenvalue solver on a quantum processor
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Quantum Annealing in the Transverse Ising Model
- Quantum computational advantage using photons
- Barren plateaus in quantum neural network training landscapes
- Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
- The Variational Quantum Eigensolver: a review of methods and best practices
- Quantum Computational Supremacy
- Noise-Induced Barren Plateaus in Variational Quantum Algorithms
- Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
- Quantum Annealing and Analog Quantum Computation
- Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
- Training variational quantum algorithms is NP-hard
- Quantum advantage with shallow circuits
- Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View
- Non-convex Optimization for Machine Learning
- Quantum Approximate Optimization of the Long-Range Ising Model with a Trapped-Ion Quantum Simulator
- Quantum annealing initialization of the quantum approximate optimization algorithm
- Reachability Deficits in Quantum Approximate Optimization
- Efficient variational simulation of non-trivial quantum states
- Filtering variational quantum algorithms for combinatorial optimization
- Benchmarking the Quantum Approximate Optimization Algorithm
- Classical variational simulation of the Quantum Approximate Optimization Algorithm
- Applying the Quantum Approximate Optimization Algorithm to the Tail Assignment Problem
- Quantifying the efficiency of state preparation via quantum variational eigensolvers
Cited by in corpus (9)
- A perspective on protein structure prediction using quantum computers
- Training variational quantum algorithms with random gate activation
- A General Approach to Dropout in Quantum Neural Networks
- Efficient and quantum-adaptive machine learning with fermion neural networks
- Quantum approximate optimization algorithm with random and subgraph phase operators
- Dropout is all you need: robust two-qubit gate with reinforcement learning
- Imaginary Hamiltonian variational ansatz for combinatorial optimization problems
- Escaping Local Minima with Quantum Coherent Cooling
- Constraint-Aware Quantum Optimization via Hamming Weight Operators