# Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip

## TL;DR

This paper demonstrates the successful implementation of an adaptive Bayesian quantum phase estimation method on a silicon photonic chip, showing its robustness and potential for near-term quantum devices in simulating molecular energies.

## Contribution

It introduces an experimental realization of adaptive Bayesian quantum phase estimation on a silicon photonic chip, highlighting its advantages over traditional methods for pre-threshold quantum processors.

## Key findings

- Robustness to noise and decoherence demonstrated
- Effective simulation of molecular energies achieved
- Potential for near-term quantum applications shown

## Abstract

Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, non-fault tolerant, quantum devices. Here we report experimental results demonstrating that this intuition need not be true. We implement a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a Silicon quantum photonic device. The approach is verified to be well suited for pre-threshold quantum processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. This shows a promising route to unlock the power of quantum phase estimation much sooner than previously believed.