Quantum learning of coherent states
arXiv:1410.8700 · doi:10.1140/epjqt/s40507-015-0030-4
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.
arXiv comment14+6 pages, 3 figures
References in corpus (37)
- Quantum key distribution using gaussian-modulated coherent states
- Quantum Illumination with Gaussian States
- Quantum control theory and applications: A survey
- Quantum algorithms for supervised and unsupervised machine learning
- Experimental realisation of quantum illumination
- Optimum Unambiguous Discrimination Between Linearly Independent Symmetric States
- Quantum Reading of a Classical Digital Memory
- Machine Learning for Precise Quantum Measurement
- Discrimination of the binary coherent signal: Gaussian-operation limit and simple non-Gaussian near-optimal receivers
- Complete Characterization of Quantum-Optical Processes
- Optimal quantum learning of a unitary transformation
- Entanglement's Benefit Survives an Entanglement-Breaking Channel
- Local asymptotic normality for finite dimensional quantum systems
- Quantum adiabatic machine learning
- Quantum learning and universal quantum matching machine
- Local asymptotic normality for qubit states
- On Asymptotic Quantum Statistical Inference
- Discriminating quantum-optical beam-splitter channels with number-diagonal signal states: Applications to quantum reading and target detection
- Quantum Reading Capacity
- Local asymptotic normality in quantum statistics
- Training a Large Scale Classifier with the Quantum Adiabatic Algorithm
- Gaussian interferometric power
- Quantum learning: optimal classification of qubit states
- Nature of light correlations in ghost imaging
- Optimal receiver for quantum cryptography with two coherent states
- Quantifying the source of enhancement in experimental continuous variable quantum illumination
- Discriminating Strength: a bona fide measure of non-classical correlations
- Programmable discriminator of coherent states - experimental realization
- Unambiguous coherent state identification: Searching a quantum database
- Multi-copy programmable discrimination of general qubit states
- Quantum reading under a local energy constraint
- Experimental Proof of Quantum Nonlocality without Squeezing
- Device-independent quantum reading and noise-assisted quantum transmitters
- Quantum reading of digital memory with non-Gaussian entangled light
- Unambiguous comparison of ensembles of quantum states
- Quantum reading capacity under thermal and correlated noise
- Unambiguous identification of coherent states II: Multiple resources
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