System behavior description using states faces problems like state explosion, lack of clear definition of state, state identification and coordination between multiple agents. The goals of this work are to ease design...System behavior description using states faces problems like state explosion, lack of clear definition of state, state identification and coordination between multiple agents. The goals of this work are to ease design activity, to reduce engineering efforts, and to mitigate project risks. The proposed way is to improve information flow during design by adding definitions and some protocols or rules for communicating a specification or design description. This work presented an objective definition of system status (way of interaction with the rest of the world) along other concepts. This work focused in definitions as mind entities and their importance to rationalize work and mitigate project risks during design. This article presented some simple examples to illustrate the advantages of each aspect of proposed definition of system status and discussed limits and exceptions for such definition. The key finding was the proposed definition which was the simplest while keeping completeness at a given product breakdown level. Such definition of status enforced formal segregation of needs and solutions, and eased the inclusion of behavior definition in specifications.展开更多
Recent photonic quantum machine learning proposals combined linear optics with adaptivity to enhance expressivity and improve algorithm performance and scalability.The particle-number-preserving property of linear opt...Recent photonic quantum machine learning proposals combined linear optics with adaptivity to enhance expressivity and improve algorithm performance and scalability.The particle-number-preserving property of linear optical platforms was recently employed to design a quantum convolutional neural network architecture with advantages in terms of resource complexity and the number of parameters needed.Here,we design and experimentally implement a photonic quantum convolutional neural network(PQCNN)based on linear optics equipped with adaptive state injection,a tool that increases the linear optical circuits controllability.We validate the PQCNN for a binary image classification on a photonic platform utilizing a semiconductor quantum dot-based single-photon source and programmable integrated photonic interferometers comprising 8 and 12 modes.To investigate the scalability of the PQCNN design,we performed numerical simulations on datasets of different sizes.These findings demonstrate potential utilities of a simple adaptive technique for a nonlinear boson sampling task,compatible with near-term quantum devices.展开更多
文摘System behavior description using states faces problems like state explosion, lack of clear definition of state, state identification and coordination between multiple agents. The goals of this work are to ease design activity, to reduce engineering efforts, and to mitigate project risks. The proposed way is to improve information flow during design by adding definitions and some protocols or rules for communicating a specification or design description. This work presented an objective definition of system status (way of interaction with the rest of the world) along other concepts. This work focused in definitions as mind entities and their importance to rationalize work and mitigate project risks during design. This article presented some simple examples to illustrate the advantages of each aspect of proposed definition of system status and discussed limits and exceptions for such definition. The key finding was the proposed definition which was the simplest while keeping completeness at a given product breakdown level. Such definition of status enforced formal segregation of needs and solutions, and eased the inclusion of behavior definition in specifications.
基金The authors acknowledge the support of the European Union’s Horizon Europe research and innovation program under EPIQUE Project(Grant Agreement No.101135288)the ERC Advanced Grant QU-BOSS(QUantum advantage via nonlinear BOSon Sampling,Grant Agreement No.884676)+1 种基金the ICSC-Centro Nazionale di Ricerca in High Performance Computing,Big Data and Quantum Computingfunded by European Union-NextGenerationEU.E.K.acknowledges support from the EPSRC Quantum Advantage Pathfinder research program within the UK’s National Quantum Computing Center.
文摘Recent photonic quantum machine learning proposals combined linear optics with adaptivity to enhance expressivity and improve algorithm performance and scalability.The particle-number-preserving property of linear optical platforms was recently employed to design a quantum convolutional neural network architecture with advantages in terms of resource complexity and the number of parameters needed.Here,we design and experimentally implement a photonic quantum convolutional neural network(PQCNN)based on linear optics equipped with adaptive state injection,a tool that increases the linear optical circuits controllability.We validate the PQCNN for a binary image classification on a photonic platform utilizing a semiconductor quantum dot-based single-photon source and programmable integrated photonic interferometers comprising 8 and 12 modes.To investigate the scalability of the PQCNN design,we performed numerical simulations on datasets of different sizes.These findings demonstrate potential utilities of a simple adaptive technique for a nonlinear boson sampling task,compatible with near-term quantum devices.
