NISQ

NISQ stands for Noisy Intermediate-Scale Quantum. The term was introduced by John Preskill in 2018 to describe a class of quantum processors that are large enough to perform nontrivial quantum computations but too noisy to support full error correction. NISQ devices typically contain tens to a few hundred qubits, operate at cryogenic temperatures, and are characterized by short coherence times and imperfect gate operations. Because of noise and limited qubit connectivity, circuits with more than a modest depth quickly lose quantum information, making reliable fault-tolerant computation infeasible with current hardware.

They generally lack fully implemented quantum error correction and logical qubits; physical qubits per logical qubit are high, requiring many more physical qubits than logical; gate fidelities and readout are imperfect; coherence times limit circuit depth. Platform variants include superconducting circuits and trapped ions, with photonic approaches also explored.

NISQ devices are typically explored with hybrid quantum-classical algorithms that leverage the strengths of both paradigms. Prominent examples include the variational quantum eigensolver (VQE) for chemistry and materials science problems and the quantum approximate optimization algorithm (QAOA) for combinatorial optimization. These algorithms rely on classical optimization to adjust parameters in a quantum circuit, aiming to mitigate some effects of noise. Error mitigation techniques—rather than full error correction—are commonly employed to reduce noise impacts; approaches include extrapolation, probabilistic error cancellation, and noise tailoring.

The NISQ era is seen as a stepping stone rather than a replacement for fault-tolerant quantum computing. While some tasks may benefit from NISQ devices, achieving practical advantage in broad, scalable applications remains uncertain and dependent on advances in hardware, error mitigation, and algorithm design.