Abstract
We develop a quantum learning scheme for binary discrimination of coherent states of light. This is a problem of technological relevance for the reading of information stored in a digital memory. In our setting, a coherent light source is used to illuminate a memory cell and retrieve its encoded bit by determining the quantum state of the reflected signal. We consider a situation where the amplitude of the states produced by the source is not fully known, but instead this information is encoded in a large training set comprising many copies of the same coherent state. We show that an optimal global measurement, performed jointly over the signal and the training set, provides higher successful identification rates than any learning strategy based on first estimating the unknown amplitude by means of Gaussian measurements on the training set, followed by an adaptive discrimination procedure on the signal. By considering a simplified variant of the problem, we argue that this is the case even for non-Gaussian estimation measurements. Our results show that, even in absence of entanglement, collective quantum measurements yield an enhancement in the readout of classical information, which is particularly relevant in the operating regime of low-energy signals.
Highlights
Programmable processors are expected to automate information processing tasks, lessening human intervention by adapting their functioning according to some input program
Whereas conventional machine learning theory implicitly assumes the training set to be made of classical data, a more recent variation, which can be referred to as quantum machine learning, focuses on the exploration and optimisation of training with fundamentally quantum objects
The reading of information, encoded in the state of a signal that comes reflected by a memory cell, is achieved by measuring the signal and deciding its state to be either the vacuum state or some coherent state of unknown amplitude
Summary
Programmable processors are expected to automate information processing tasks, lessening human intervention by adapting their functioning according to some input program. The asymptotic behaviour of any statistical inference problem that uses this model is determined by the structure of a local (Gaussian) quantum model around a fixed coherent state |α .f In our case, we consider this ‘localisation’ of the prior probability distribution as an innocuous preparatory process in both the collective and the E&D strategies, in the sense that the comparison between their asymptotic discrimination power will not be affected. Under these considerations, the initial prio√r for α will be a Gaussian probability distribution centred at α , whose width goes as ∼ / n.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
