Looking upon every encoding state of quantum information systems as a quantum information pattern, A kind of pattern-distance between different patterns as a measurement of comparability of quantum information patterns is defined, and two kinds of recognition algorithms based on pattern-distance for quantum information are proposed. They can respectively recognize quantum information with known objective pattern and unknown objective pattern. In the two algorithms, the phases and occurrence probabilities of different eigenpatterns of quantum information are sufficiently considered. Two examples demonstrate the feasibility and effectiveness of the two recognition methods. These algorithms point out a new and important path for applications of quantum information and pattern recognition.
Looking upon every encoding state of quantum information systems as a quantum information pattern, A kind of pattern-distance between different patterns as a measurement of comparability of quantum information patterns is defined, and two kinds of recognition algorithms based on pattern-distance for quantum information are proposed. They can respectively recognize quantum information with known objective pattern and unknown objective pattern. In the two algorithms, the phases and occurrence probabilities of different eigenpatterns of quantum information are sufficiently considered. Two examples demonstrate the feasibility and effectiveness of the two recognition methods. These algorithms point out a new and important path for applications of quantum information and pattern recognition.
In view of recognition of states of quantum systems, a kind of quantum module distance, which is used as a measurement of comparability between two quantum states, is defined and a kind of clustering algorithm based on quantum module distance is proposed. This algorithm is suitable for recognition of both quantum superposition states and quantum entangled states.In this algorithm, firstly calculate the clustering center according to the samples of quantum states which need recognizing. Secondly calculate quantum module distance between every quantum state and the clustering center. Lastly recognize quantum states according to quantum module distances. An example is given to demonstrate the reasonableness and effectiveness of the clustering method for recognition of quantum states.
A dot matrix approach for sequence alignment is combined with a known quantum multi-pattern recognition method in order to improve the problem of sequence alignment. This dot matrix technique allows the application of some quantum computing principles on the pattern recognition problems like those used in the Grover's algorithm. When the recognized patterns exceed the limit of the 1/3 of the overall patterns, the multi-pattern recognition is accomplished simultaneously with the probability of 100%. The contribution concerns the way that the adopted quantum pattern recognition algorithm is applied speeding up the sequence alignment process. An example application demonstrates the effectiveness of the proposed method. String alignments isn't an essential task only in bioinformatics, but also in calculating the edit distance cost between strings in a natural language or in financial data.
It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.
This paper explores the potential of quantum entanglement to revolutionize information encoding. Traditional information transmission methods are limited by the speed and fragility of classical signals. Quantum entanglement, a peculiar phenomenon where two particles become linked regardless of distance, offers a fundamentally different approach. We propose a novel information encoding scheme leveraging this entanglement, aiming to achieve significantly enhanced security and data integrity compared to current methods. The core of this work focuses on the design of a quantum key distribution protocol utilizing entangled states, showcasing its potential for advanced quantum computation and cryptographic applications. We examine the theoretical advantages and practical challenges of this approach, highlighting its unique characteristics and potential impact on the future of information technology.
Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multipartite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of a barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest-neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.
Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multi-partite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.