Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance
arXiv:2403.04296 · doi:10.1007/s11432-024-4185-1
Abstract
Great efforts have been dedicated in recent years to explore practical applications for noisy intermediate-scale quantum (NISQ) computers, which is a fundamental and challenging problem in quantum computing. As one of the most promising methods, the variational quantum eigensolver (VQE) has been extensively studied. In this paper, VQE is applied to solve portfolio optimization problems in finance by designing two hardware-efficient Dicke state ansatze that reach a maximum of 2n two-qubit gate depth and n^2/4 parameters, with n being the number of qubits used. Both ansatze are partitioning-friendly, allowing for the proposal of a highly scalable quantum/classical hybrid distributed computing (HDC) scheme. Combining simultaneous sampling, problem-specific measurement error mitigation, and fragment reuse techniques, we successfully implement the HDC experiments on the superconducting quantum computer Wu Kong with up to 55 qubits. The simulation and experimental results illustrate that the restricted expressibility of the ansatze, induced by the small number of parameters and limited entanglement, is advantageous for solving classical optimization problems with the cost function of the conditional value-at-risk (CVaR) for the NISQ era and beyond. Furthermore, the HDC scheme shows great potential for achieving quantum advantage in the NISQ era. We hope that the heuristic idea presented in this paper can motivate fruitful investigations in current and future quantum computing paradigms.
arXiv comment21 pages, 20 figures
References in corpus (43)
- Quantum Computing in the NISQ era and beyond
- Quantum Machine Learning
- A variational eigenvalue solver on a quantum processor
- Quantum Mechanics helps in searching for a needle in a haystack
- Quantum algorithm for solving linear systems of equations
- Variational Quantum Algorithms
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Noisy intermediate-scale quantum (NISQ) algorithms
- Quantum metrology with nonclassical states of atomic ensembles
- Quantum computational chemistry
- Quantum Convolutional Neural Networks
- Error mitigation for short-depth quantum circuits
- Parameterized quantum circuits as machine learning models
- Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
- The Variational Quantum Eigensolver: a review of methods and best practices
- Quantum computing for finance: overview and prospects
- Quantum optimization using variational algorithms on near-term quantum devices
- Connecting ansatz expressibility to gradient magnitudes and barren plateaus
- Training variational quantum algorithms is NP-hard
- Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View
- Simulating Large Quantum Circuits on a Small Quantum Computer
- The General Quantum Interference Principle and the Duality Computer
- Low-cost error mitigation by symmetry verification
- Efficient Symmetry-Preserving State Preparation Circuits for the Variational Quantum Eigensolver Algorithm
- Improving Variational Quantum Optimization using CVaR
- The Bitter Truth About Quantum Algorithms in the NISQ Era
- Performance of hybrid quantum/classical variational heuristics for combinatorial optimization
- Efficient quantum algorithms for and states, and implementation on the IBM quantum computer
- A necessary and sufficient condition to play games in quantum mechanical settings
- Iterative Quantum Assisted Eigensolver
- Efficient quantum computation of molecular forces and other energy gradients
- Delocalized information in quantum networks
- Quantum Approximate Optimization Algorithm with Adaptive Bias Fields
- Quantum Image Segmentation Based on Grayscale Morphology
- Experimental Simulation of Larger Quantum Circuits with Fewer Superconducting Qubits
- Quantum-Assisted Solution Paths for the Capacitated Vehicle Routing Problem
- An evolving objective function for improved variational quantum optimisation
- Classical Splitting of Parametrized Quantum Circuits
- Quantum Machine Learning Implementations: Proposals and Experiments
- Fast Black-Box Quantum State Preparation Based on Linear Combination of Unitaries
- Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz
- QPanda: high-performance quantum computing framework for multiple application scenarios
- Exploiting Qubit Reuse through Mid-circuit Measurement and Reset