Algorithm-oriented qubit mapping for variational quantum algorithms

Variational algorithms are among the first practical applications of quantum computing, but their performance is limited by today's noisy intermediate-scale quantum (NISQ) devices. The authors propose scalable, depth-optimal solutions to overcome these limitations by integrating optimal mapping algorithms applied to small submodules of a given NISQ computer (focusing on popular linear and T- and H-shaped subtopologies). Identification of the best qubits combined with postselection keeps the error rate in check. The team reports up to 82% reduction in circuit depth and an average of 138% better success probability, thus paving the way for reliable quantum computing ecosystems of tomorrow.