Advances in Quantum Technology: A Comprehensive Survey of Hardware Platforms, Quantum Algorithms, Error Correction, Cryptography, and Emerging Applications Across the Quantum Computing Ecosystem

The move from lab demonstrations to designed quantum technology is underway, but the development in hardware, algorithms, error correction and safe communication is uneven. The survey is comprehensive, covering the full stack: superconducting, trapped-ion, neutral-atom, photonic, silicon-spin and topological qubits; Shor, Grover, HHL, VQE, QAOA and quantum machine learning; stabilizer and surface-code error correction; quantum key distribution and post-quantum cryptography; and applications in chemistry, finance, logistics, healthcare and security. Published benchmarks up to 2025 are normalized to common metrics and augmented by reproducible simulations. Surface-code simulations at the circuit level show that the threshold is around 0.7-1.0%, and the error-suppression factor increases from 1.6 to 11 when the physical error rate is reduced from 0.6% to 0.1%. UCCSD-VQE reproduces H₂ precisely but misses chemical accuracy for a stretched H₄ chain (5.2 mHa), and a 48-CNOT compilation fails it already at 0.01% two-qubit error. For MaxCut on 3-regular graphs, the QAOA depth required to beat Goemans-Williamson rounding increases from 4 qubits to 6 qubits. The asymptotic decoy-state BB84 reaches 1.5 Mb s−1 over 100 km, whereas the twin-field protocols beat the repeaterless bound beyond 280 km. So, physical fidelity and fault tolerance are still the limiting factors for broad quantum advantage, not qubit count. These comparisons also inform computational decision-making by relating algorithm selection to solution quality, physical error rates, and resource constraints. They provide a basis for assessing where quantum optimization may complement established classical methods in practical decision-support applications.