Accelerating Feedback-Based Quantum Algorithms through Time Rescaling
arXiv:2504.01256 · doi:10.1103/qc91-5mj2
Abstract
This work investigates the impact of time rescaling on the performance of Feedback Quantum Algorithms (FQA) and their variant for optimization tasks, FALQON. We introduce TR-FQA and TR-FALQON, time-rescaled versions of FQA and FALQON, respectively. The method is applied to two representative problems: the MaxCut combinatorial optimization problem and ground-state preparation in the ANNNI quantum many-body model. The results show that TR-FALQON accelerates convergence to the optimal solution in the early layers of the circuit, significantly outperforming its standard counterpart in shallow-depth regimes. In the context of state preparation, TR-FQA demonstrates superior convergence, reducing the required circuit depth by several hundred layers. These findings highlight the potential of time rescaling as a strategy to enhance algorithmic performance on near-term quantum devices.
8 pages, 4 figures
References in corpus (17)
- A variational eigenvalue solver on a quantum processor
- Variational Quantum Algorithms
- Ising formulations of many NP problems
- Noisy intermediate-scale quantum (NISQ) algorithms
- High-fidelity quantum driving
- More bang for your buck: Towards super-adiabatic quantum engines
- Accelerated quantum control using superadiabatic dynamics in a solid-state lambda system
- Shortcuts to adiabaticity by counterdiabatic driving for trapped-ion displacement in phase space
- Superadiabatic quantum friction suppression in finite-time thermodynamics
- The experimental realization of high-fidelity `shortcut-to-adiabaticity' quantum gates in a superconducting Xmon qubit
- Feedback-based quantum optimization
- Quantum variational optimization: The role of entanglement and problem hardness
- Lyapunov control-inspired strategies for quantum combinatorial optimization
- Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
- Time-rescaled quantum dynamics as a shortcut to adiabaticity
- Using a Feedback-Based Quantum Algorithm to Analyze the Critical Properties of the ANNNI Model Without Classical Optimization
- Scalable circuit depth reduction in feedback-based quantum optimization with a quadratic approximation