More Quantum Data for Less: Million-Fold Data Collection Speedups with PTSBE
There is a fundamental challenge in developing useful quantum computers. The quantum behavior that makes them so desirable to build also makes them difficult to simulate and design.
This becomes especially challenging when trying to model the noise in quantum computing hardware. The exact simulation of \(n\) ideal qubits requires tracking a number of parameters that grows like \( 2^n \), since an increasingly large vector must be tracked for larger quantum systems. But simulating noisy qubits requires not just tracking a Hilbert space vector, but a density matrix, described by \( 2^{2n} \) values - adding yet another quadratic overhead. Including noise is key when simulating new quantum processor designs, but this extra computational cost can be severe.
Thankfully, there is a solution. Quantum trajectory methods approximate noisy systems with an ensemble of stochastically sampled \( 2^n \)-entry statevectors rather than exact density matrices - removing the additional quadratic overhead.