arXiv:2605.23138v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces. To overcome this bottleneck, we propose CRiSP (a Clifford Reinforcement Learning agent for State Preparation), a framework that formulates discrete prefix selectio
The proliferation of variational quantum algorithms and the persistent challenges of barren plateaus and local minima necessitate more effective initialization strategies.
Improved VQA optimization techniques like CRiSP could significantly accelerate the development of practical quantum algorithms, bringing quantum advantage closer to reality.
The ability to more effectively initialize VQAs using reinforcement learning could unblock key bottlenecks in quantum algorithm development and expand the range of solvable problems.
- · Quantum computing researchers
- · Quantum hardware manufacturers
- · Early adopters of quantum solutions
- · Heuristic-based VQA initialization methods
More efficient and reliable execution of variational quantum algorithms.
Faster progress in quantum chemistry, materials science, and financial modeling applications.
Potential for new classes of quantum-powered AI applications that rely on robust VQAs.
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Read at arXiv cs.LG