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Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

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Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip.

S Paesani¹, A A Gentile¹, R Santagati¹

¹Quantum Engineering Technology Labs, H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering, University of Bristol, BS8 1FD, United Kingdom.

Physical Review Letters

|March 25, 2017

Summary

This summary is machine-generated.

We demonstrate a robust adaptive Bayesian approach for quantum phase estimation on near-term quantum devices. This method simulates molecular energies, proving practical for pre-threshold quantum processors despite noise.

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Area of Science:

Quantum Information Science
Quantum Computing Algorithms
Computational Chemistry

Background:

Quantum phase estimation (QPE) is crucial for quantum algorithms like Shor's factorization and quantum simulation.
Its application on near-term, non-fault-tolerant quantum devices has been questioned due to noise and decoherence.

Purpose of the Study:

To experimentally validate a novel adaptive Bayesian approach for quantum phase estimation.
To assess the practicality and robustness of this method on current quantum hardware.
To demonstrate its utility in simulating molecular energies.

Main Methods:

Implementation of an adaptive Bayesian quantum phase estimation algorithm.
Utilizing a silicon quantum photonic device for experimental execution.
Comparative analysis of noise and decoherence robustness against the iterative phase estimation algorithm.

Main Results:

The adaptive Bayesian approach shows superior robustness to noise and decoherence compared to traditional iterative methods.
Successful simulation of molecular energies was achieved on a pre-threshold quantum processor.
Experimental results confirm the practicability of QPE on near-term devices.

Conclusions:

The adaptive Bayesian approach offers a viable pathway for utilizing quantum phase estimation on near-term quantum computers.
This technique enhances the feasibility of quantum simulations and other complex quantum algorithms sooner than anticipated.
The study highlights a promising direction for advancing quantum computing applications.