Quantum optimization algorithms for strongly correlated many-body systems

This perspective assesses quantum algorithms for phase transitions and low-energy properties of strongly correlated many-body systems in the Noisy Intermediate-Scale Quantum era. We compare the Variational Quantum Eigensolver, the Quantum Approximate Optimization Algorithm, feedback-based protocols such as FALQON, adaptive variational methods, and quantum-subspace approaches. The comparison separates circuit depth, classical processing, and measurement overhead because no single metric establishes uniform superiority. We discuss symmetry-preserving and particle-number-conserving circuits, tensor-network-assisted initialization, variational imaginary-time evolution, Sample-based Krylov Quantum Diagonalization, and basis-adaptive algorithms. These methods are connected to deconfined quantum criticality, strange metals, many-body localization, topological transitions, and quantum spin liquids, with order-of-magnitude resource windows and measurement bottlenecks stated explicitly. Feedback-guided methods remove a high-dimensional external optimizer and can preserve selected symmetries, but exchange these advantages for layer-by-layer commutator measurements, progressive circuit growth, and sensitivity to noise. The available evidence therefore supports a problem-dependent portfolio of physics-informed, symmetry-aware, and classically assisted algorithms rather than a universal ranking among VQE, QAOA, and FALQON.