Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets
Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets
Variational quantum algorithms are believed to be promising for solving computationally hard problems on noisy intermediate-scale quantum (NISQ) systems. Gaining computational power from these algorithms critically relies on the mitigation of errors during their execution, which for coherence-limited operations is achievable by reducing the gate count. Here, we demonstrate an …