Improving application performance with biased distributions of quantum states
This work addresses the need for more accurate quantum state distributions in experimental settings, offering incremental improvements for quantum computing and photonics applications.
The paper tackles the problem of generating quantum state distributions with specific mean purity by introducing a biased distribution based on Dirichlet-weighted Haar mixtures, and demonstrates that this method improves performance in quantum state tomography and reconstruction, with measurable advantages over existing distributions like Bures and Hilbert–Schmidt.
We consider the properties of a specific distribution of mixed quantum states of arbitrary dimension that can be biased towards a specific mean purity. In particular, we analyze mixtures of Haar-random pure states with Dirichlet-distributed coefficients. We analytically derive the concentration parameters required to match the mean purity of the Bures and Hilbert--Schmidt distributions in any dimension. Numerical simulations suggest that this value recovers the Hilbert--Schmidt distribution exactly, offering an alternative and intuitive physical interpretation for ensembles of Hilbert--Schmidt-distributed random quantum states. We then demonstrate how substituting these Dirichlet-weighted Haar mixtures in place of the Bures and Hilbert--Schmidt distributions results in measurable performance advantages in machine-learning-based quantum state tomography systems and Bayesian quantum state reconstruction. Finally, we experimentally characterize the distribution of quantum states generated by both a cloud-accessed IBM quantum computer and an in-house source of polarization-entangled photons. In each case, our method can more closely match the underlying distribution than either Bures or Hilbert--Schmidt distributed states for various experimental conditions.