Projected gradient descent algorithms for quantum state tomography. (December 2017)

• Record Type: Journal Article
• Authors: Bolduc, Eliot; Knee, George; Gauger, Erik; Leach, Jonathan
• Is Part Of: Npj quantum information. Volume 3(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-12
• Subjects: Quantum computers -- Periodicals; Quantum communication -- Periodicals; Information theory -- Periodicals; Quantum theory -- Periodicals; Quantum theory; Information theory; Quantum communication; Quantum computers; Periodicals; 006.3843
• DOI: 10.1038/s41534-017-0043-1
• Languages: English
• ISSNs: 2056-6387

Abstract

Abstract Accurate quantum tomography is a vital tool in both fundamental and applied quantum science. It is a task that involves processing a noisy measurement record in order to construct a reliable estimate of an unknown quantum state, and is central to quantum computing, metrology and communication. To date, many different approaches to quantum state estimation have been developed, yet no one method fits all applications, and all fail relatively quickly as the dimensionality of the unknown state grows. In this work, we suggest that projected gradient descent is a method that can evade some of these shortcomings. We present three tomography algorithms that use projected gradient descent and compare their performance with state-of-the-art alternatives, i.e., the diluted iterative algorithm and convex programming. Our results find in favour of the general class of projected gradient descent methods due to their speed, applicability to large states, and the range of conditions in which they perform as well as providing insight into which variant of projected gradient descent ought to be used in various measurement scenarios. Quantum science: states that wander find home faster The recovery of a quantum state from experimental measurement is a challenging task that often relies on iteratively updating the estimate of the state at hand. Letting quantum state estimates temporarily wander outside of the space of physically possible solutions helps speeding up the process of recovering them. A team led by Jonathan Leach at Heriot-Watt University developed iterative algorithms for quantum state reconstruction based on the idea of projecting unphysical states onto the space of physical ones. The state estimates are updated through steepest descent and projected onto the set of positive matrices. The algorithms converged to the correct state estimates significantly faster than state-of-the-art methods can and behaved especially well in the context of ill-conditioned problems. In particular, this work opens the door to full characterisation of large-scale quantum states.