Parameterized Quantum Circuit for Portfolio Optimization
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Solution Overview
Problem
Current technologies face challenges in efficiently solving combinatorial optimization problems, such as portfolio optimization, due to their computational complexity and limitations in noisy intermediate-scale quantum (NISQ) devices.
Innovation Solution
The method involves designing parameterized quantum circuits using digitized counterdiabatic driving to accelerate adiabatic evolution, allowing for the efficient solution of portfolio optimization problems by encoding them into an Ising Hamiltonian and using reinforcement learning to optimize circuit design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adiabatic quantum optimization algorithms are used to solve portfolio optimization problems, then the solution quality improves, but the evolution time becomes too long and noise artifacts are introduced
Solution Approach 1:
The patent applies counterdiabatic driving by introducing an additional Hamiltonian term that anticipates and suppresses non-adiabatic transitions before they occur. This preliminary anti-action allows the system to evolve faster while maintaining adiabatic conditions, directly resolving the contradiction between evolution time and solution quality.
Solution Approach 2:
The patent changes the time evolution parameter by implementing a modified Hamiltonian with counterdiabatic terms. This parameter change enables the system to achieve the same solution quality with significantly reduced evolution time by altering the dynamics of the quantum evolution process.
2Adaptability or versatility
If digitized adiabatic quantum computing methods are used to increase flexibility in problem Hamiltonian, then adaptability improves, but the number of gates increases reducing fidelity
Solution Approach 1:
The patent extracts the essential adiabatic evolution dynamics from the complex digitized gate sequence by applying counterdiabatic driving. This extraction allows maintaining the flexibility of digitized methods while removing the excessive gate overhead that reduces fidelity, achieving a balance between adaptability and reliability.
3Measurement precision
If quantum approximate optimization algorithm with many layers is used to optimize cost function, then solution accuracy improves, but the algorithm becomes unsuitable for NISQ devices
Solution Approach 1:
The patent applies partial adiabatic evolution with counterdiabatic driving, using a modified evolution schedule that achieves sufficient solution accuracy with fewer layers. This partial action approach avoids the need for excessive layers while still obtaining meaningful optimization results suitable for NISQ devices.
4Reliability
If adiabatic evolution is performed to ensure ground state is found, then solution reliability improves, but the time involved becomes considerable
Solution Approach 1:
The patent introduces counterdiabatic driving terms that preliminarily counteract transitions away from the ground state during evolution. This preliminary protective action maintains ground state reliability while enabling faster evolution, directly addressing the time-reliability contradiction.
Data Source
AI summary
The invention relates to a method, to a computer program and to a computer device as described herein. A computer-implemented method for solving a portfolio optimization problem includes encoding the portfolio optimization problem into an Ising-Hamiltonian model whose ground state is the optimal solution of the problem, providing a cost function, providing constraints for the cost function, applying a digitized counterdiabatic driving method to a Hamiltonian encoding the cost function, and providing a parameterized circuit design with a minimum depth to a counterdiabatic accelerated adiabatic evolution.