Min Tang, Chi Pang, Christian N. Saggau, Haiyun Dong, Ching Hua Lee, Ronny Thomale, Sebastian Klembt, Ion Cosma Fulga, Jeroen van den Brink, Yana Vaynzof, Oliver G. Schmidt, Jiawei Wang, Libo Ma
Topological boundary states localize at interfaces whenever the interface implies a change of the associated topological invariant encoded in the geometric phase. The generically present dynamic phase, however, which is energy and time‐dependent, is known to be non‐universal, and hence not to intertwine with any topological geometric phase. Using the example of topological zero modes in composite Su‐Schrieffer‐Heeger (c‐SSH) waveguide arrays with a central defect is reported on the selective excitation and transition of topological boundary mode based on dynamic phase‐steered interferences. This work thus provides a new knob for the control and manipulation of topological states in composite photonic devices, indicating promising applications where topological modes and their bandwidth can be jointly controlled by the dynamic phase, geometric phase, and wavelength in on‐chip topological devices.
{"title":"Dynamic Phase Enabled Topological Mode Steering in Composite Su‐Schrieffer–Heeger Waveguide Arrays","authors":"Min Tang, Chi Pang, Christian N. Saggau, Haiyun Dong, Ching Hua Lee, Ronny Thomale, Sebastian Klembt, Ion Cosma Fulga, Jeroen van den Brink, Yana Vaynzof, Oliver G. Schmidt, Jiawei Wang, Libo Ma","doi":"10.1002/qute.202400390","DOIUrl":"https://doi.org/10.1002/qute.202400390","url":null,"abstract":"Topological boundary states localize at interfaces whenever the interface implies a change of the associated topological invariant encoded in the geometric phase. The generically present dynamic phase, however, which is energy and time‐dependent, is known to be non‐universal, and hence not to intertwine with any topological geometric phase. Using the example of topological zero modes in composite Su‐Schrieffer‐Heeger (c‐SSH) waveguide arrays with a central defect is reported on the selective excitation and transition of topological boundary mode based on dynamic phase‐steered interferences. This work thus provides a new knob for the control and manipulation of topological states in composite photonic devices, indicating promising applications where topological modes and their bandwidth can be jointly controlled by the dynamic phase, geometric phase, and wavelength in on‐chip topological devices.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Quantum Alternating Operator Ansatz (QAOA+) is one of the Variational Quantum Algorithm (VQA) specifically developed to tackle combinatorial optimization problems by exploring the feasible space in search of a target solution. For the Constrained Binary Optimization with Unconstrained Variables Problems (CBO‐UVPs), the mixed operators in the QAOA+ circuit are applied to the constrained variables, while the single‐qubit rotating gates operate on the unconstrained variables. The expressibility of this circuit is limited by the shortage of two‐qubit gates and the parameter sharing in the single‐qubit rotating gates, which consequently impacts the performance of QAOA+ for solving CBO‐UVPs. Therefore, it is crucial to develop a suitable ansatz for CBO‐UVPs. In this paper, the Variational Quantum Algorithm‐Preserving Feasible Space (VQA‐PFS) ansatz is proposed, exemplified by the Uncapacitated Facility Location Problem (UFLP), that applies mixed operators on constrained variables while employing Hardware‐Efficient Ansatz (HEA) on unconstrained variables. The numerical results demonstrate that VQA‐PFS significantly enhances the probability of success and exhibits faster convergence than QAOA+, Quantum Approximation Optimization Algorithm (QAOA), and HEA. Furthermore, VQA‐PFS reduces the circuit depth dramatically compared to QAOA+ and QAOA. The algorithm is general and instructive in tackling CBO‐UVPs.
{"title":"Variational Quantum Algorithm‐Preserving Feasible Space for Solving the Uncapacitated Facility Location Problem","authors":"Sha‐Sha Wang, Hai‐Ling Liu, Yong‐Mei Li, Fei Gao, Su‐Juan Qin, Qiao‐Yan Wen","doi":"10.1002/qute.202400201","DOIUrl":"https://doi.org/10.1002/qute.202400201","url":null,"abstract":"The Quantum Alternating Operator Ansatz (QAOA+) is one of the Variational Quantum Algorithm (VQA) specifically developed to tackle combinatorial optimization problems by exploring the feasible space in search of a target solution. For the Constrained Binary Optimization with Unconstrained Variables Problems (CBO‐UVPs), the mixed operators in the QAOA+ circuit are applied to the constrained variables, while the single‐qubit rotating gates operate on the unconstrained variables. The expressibility of this circuit is limited by the shortage of two‐qubit gates and the parameter sharing in the single‐qubit rotating gates, which consequently impacts the performance of QAOA+ for solving CBO‐UVPs. Therefore, it is crucial to develop a suitable ansatz for CBO‐UVPs. In this paper, the Variational Quantum Algorithm‐Preserving Feasible Space (VQA‐PFS) ansatz is proposed, exemplified by the Uncapacitated Facility Location Problem (UFLP), that applies mixed operators on constrained variables while employing Hardware‐Efficient Ansatz (HEA) on unconstrained variables. The numerical results demonstrate that VQA‐PFS significantly enhances the probability of success and exhibits faster convergence than QAOA+, Quantum Approximation Optimization Algorithm (QAOA), and HEA. Furthermore, VQA‐PFS reduces the circuit depth dramatically compared to QAOA+ and QAOA. The algorithm is general and instructive in tackling CBO‐UVPs.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generative models realized with Machine Learning (ML) techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion Models (DMs) are an emerging framework that have recently overcome Generative Adversarial Networks (GANs) in creating high‐quality images. Here, is proposed and discussed the quantum generalization of DMs, i.e., three Quantum‐Noise‐Driven Generative Diffusion Models (QNDGDMs) that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non‐trivial interplay among coherence, entanglement, and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, the suggestion is to exploit quantum noise not as an issue to be detected and solved but instead as a beneficial key ingredient to generate complex probability distributions from which a quantum processor might sample more efficiently than a classical one. Three examples of the numerical simulations are also included for the proposed approaches. The results are expected to pave the way for new quantum‐inspired or quantum‐based generative diffusion algorithms addressing tasks as data generation with widespread real‐world applications.
{"title":"Quantum‐Noise‐Driven Generative Diffusion Models","authors":"Marco Parigi, Stefano Martina, Filippo Caruso","doi":"10.1002/qute.202300401","DOIUrl":"https://doi.org/10.1002/qute.202300401","url":null,"abstract":"Generative models realized with Machine Learning (ML) techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion Models (DMs) are an emerging framework that have recently overcome Generative Adversarial Networks (GANs) in creating high‐quality images. Here, is proposed and discussed the quantum generalization of DMs, i.e., three Quantum‐Noise‐Driven Generative Diffusion Models (QNDGDMs) that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non‐trivial interplay among coherence, entanglement, and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, the suggestion is to exploit quantum noise not as an issue to be detected and solved but instead as a beneficial key ingredient to generate complex probability distributions from which a quantum processor might sample more efficiently than a classical one. Three examples of the numerical simulations are also included for the proposed approaches. The results are expected to pave the way for new quantum‐inspired or quantum‐based generative diffusion algorithms addressing tasks as data generation with widespread real‐world applications.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José‐Enrique García‐Ramos, Álvaro Sáiz, José M. Arias, Lucas Lamata, Pedro Pérez‐Fernández
In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low‐energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low‐energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model‐type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments.
{"title":"Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning","authors":"José‐Enrique García‐Ramos, Álvaro Sáiz, José M. Arias, Lucas Lamata, Pedro Pérez‐Fernández","doi":"10.1002/qute.202300219","DOIUrl":"https://doi.org/10.1002/qute.202300219","url":null,"abstract":"In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low‐energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low‐energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model‐type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140832117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carlos Hernani‐Morales, Gabriel Alvarado, Francisco Albarrán‐Arriagada, Yolanda Vives‐Gilabert, Enrique Solano, José D. Martín‐Guerrero
Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.
{"title":"Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors","authors":"Carlos Hernani‐Morales, Gabriel Alvarado, Francisco Albarrán‐Arriagada, Yolanda Vives‐Gilabert, Enrique Solano, José D. Martín‐Guerrero","doi":"10.1002/qute.202300294","DOIUrl":"https://doi.org/10.1002/qute.202300294","url":null,"abstract":"Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leonardo Banchi, Jason Luke Pereira, Sharu Theresa Jose, Osvaldo Simeone
Learning problems involve settings in which an algorithm has to make decisions based on data, and possibly side information such as expert knowledge. This study has two main goals. First, it reviews and generalizes different results on the data and model complexity of quantum learning, where the data and/or the algorithm can be quantum, focusing on information‐theoretic techniques. Second, it introduces the notion of copy complexity, which quantifies the number of copies of a quantum state required to achieve a target accuracy level. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. As a result, empirical risk minimization is generally inapplicable. The paper presents novel results on the copy complexity for both training and testing. To make the paper self‐contained and approachable by different research communities, an extensive background material is provided on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, the differences between quantum and classical learning are highlighted by addressing both supervised and unsupervised learning, and extensive pointers are provided to the literature.
{"title":"Statistical Complexity of Quantum Learning","authors":"Leonardo Banchi, Jason Luke Pereira, Sharu Theresa Jose, Osvaldo Simeone","doi":"10.1002/qute.202300311","DOIUrl":"https://doi.org/10.1002/qute.202300311","url":null,"abstract":"Learning problems involve settings in which an algorithm has to make decisions based on data, and possibly side information such as expert knowledge. This study has two main goals. First, it reviews and generalizes different results on the data and model complexity of quantum learning, where the data and/or the algorithm can be quantum, focusing on information‐theoretic techniques. Second, it introduces the notion of copy complexity, which quantifies the number of copies of a quantum state required to achieve a target accuracy level. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. As a result, empirical risk minimization is generally inapplicable. The paper presents novel results on the copy complexity for both training and testing. To make the paper self‐contained and approachable by different research communities, an extensive background material is provided on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, the differences between quantum and classical learning are highlighted by addressing both supervised and unsupervised learning, and extensive pointers are provided to the literature.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Massimiliano Incudini, Francesco Martini, Alessandra Di Pierro
Supervised machine learning is a popular approach to the solution of many real-life problems. This approach is characterized by the use of labeled datasets to train algorithms for classifying data or predicting outcomes accurately. The question of the extent to which quantum computation can help improve existing classical supervised learning methods is the subject of intense research in the area of quantum machine learning. The debate centers on whether an advantage can be achieved already with current noisy quantum computer prototypes or it is strictly dependent on the full power of a fault-tolerant quantum computer. The current proposals can be classified into methods that can be suitably implemented on near-term quantum computers but are essentially empirical, and methods that use quantum algorithms with a provable advantage over their classical counterparts but only when implemented on the still unavailable fault-tolerant quantum computer.
{"title":"Toward Useful Quantum Kernels","authors":"Massimiliano Incudini, Francesco Martini, Alessandra Di Pierro","doi":"10.1002/qute.202300298","DOIUrl":"https://doi.org/10.1002/qute.202300298","url":null,"abstract":"Supervised machine learning is a popular approach to the solution of many real-life problems. This approach is characterized by the use of labeled datasets to train algorithms for classifying data or predicting outcomes accurately. The question of the extent to which quantum computation can help improve existing classical supervised learning methods is the subject of intense research in the area of quantum machine learning. The debate centers on whether an advantage can be achieved already with current noisy quantum computer prototypes or it is strictly dependent on the full power of a fault-tolerant quantum computer. The current proposals can be classified into methods that can be suitably implemented on near-term quantum computers but are essentially empirical, and methods that use quantum algorithms with a provable advantage over their classical counterparts but only when implemented on the still unavailable fault-tolerant quantum computer.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139902148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The coordination between distance and the secure key rate is one of the main challenges in the practical application of quantum key distribution (QKD). Mode‐pairing quantum key distribution is one of the schemes that can surpass the secret key capacity for repeaterless QKD. However, the protocol utilizes phase to encode the information, which leads to the problem of active stabilization in the interferometer. In this paper, a reference‐frame‐independent mode‐pairing quantum key distribution (RFI‐MP‐QKD) is proposed as an effective scheme to solve this problem. Moreover, the performance of the RFI‐MP‐QKD protocol is improved by applying the Advantage Distillation (AD) method in data post‐processing, which separates the highly correlated raw key bits from the weakly correlated information. The simulation results show that the secure key rate of RFI‐MP‐QKD has almost no degradation for reference frame deviation angles of . Compared to RFI‐MP‐QKD without AD method, the AD method decreases the quantum bit error rate from 0.04 to 0.012 and increases the maximum transmission distance from 406 to 450 km. The scheme proposed is expected to facilitate the practical implementation of RFI‐MP‐QKD, especially in cases of concerning reference frame alignment and high channel loss.
{"title":"Reference‐Frame‐Independent Mode‐Pairing Quantum Key Distribution with Advantage Distillation","authors":"Yuemei Li, Zhongqi Sun, Xinhe Liu, Zhenhua Li, Jiaao Li, Haoyang Wang, Kaiyi Shi, Chang Liu, Haiqiang Ma","doi":"10.1002/qute.202300387","DOIUrl":"https://doi.org/10.1002/qute.202300387","url":null,"abstract":"The coordination between distance and the secure key rate is one of the main challenges in the practical application of quantum key distribution (QKD). Mode‐pairing quantum key distribution is one of the schemes that can surpass the secret key capacity for repeaterless QKD. However, the protocol utilizes phase to encode the information, which leads to the problem of active stabilization in the interferometer. In this paper, a reference‐frame‐independent mode‐pairing quantum key distribution (RFI‐MP‐QKD) is proposed as an effective scheme to solve this problem. Moreover, the performance of the RFI‐MP‐QKD protocol is improved by applying the Advantage Distillation (AD) method in data post‐processing, which separates the highly correlated raw key bits from the weakly correlated information. The simulation results show that the secure key rate of RFI‐MP‐QKD has almost no degradation for reference frame deviation angles of . Compared to RFI‐MP‐QKD without AD method, the AD method decreases the quantum bit error rate from 0.04 to 0.012 and increases the maximum transmission distance from 406 to 450 km. The scheme proposed is expected to facilitate the practical implementation of RFI‐MP‐QKD, especially in cases of concerning reference frame alignment and high channel loss.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139866010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiahao Huang, Min Zhuang, Jungeng Zhou, Yi Shen, Chaohong Lee
Quantum metrology aims to measure physical quantities based on fundamental quantum principles, enhancing measurement precision through resources like quantum entanglement and quantum correlations. This field holds promise for advancing quantum-enhanced sensors, including atomic clocks and magnetometers. However, practical constraints exist in the four fundamental steps of quantum metrology, including initialization, sensing, readout, and estimation. Valuable resources, such as coherence time, impose limitations on the performance of quantum sensors. Machine learning, enabling learning and prediction without explicit knowledge, provides a powerful tool in optimizing quantum metrology with limited resources. This article reviews the fundamental principles, potential applications, and recent advancements in quantum metrology assisted by machine learning.
{"title":"Quantum Metrology Assisted by Machine Learning","authors":"Jiahao Huang, Min Zhuang, Jungeng Zhou, Yi Shen, Chaohong Lee","doi":"10.1002/qute.202300329","DOIUrl":"https://doi.org/10.1002/qute.202300329","url":null,"abstract":"Quantum metrology aims to measure physical quantities based on fundamental quantum principles, enhancing measurement precision through resources like quantum entanglement and quantum correlations. This field holds promise for advancing quantum-enhanced sensors, including atomic clocks and magnetometers. However, practical constraints exist in the four fundamental steps of quantum metrology, including initialization, sensing, readout, and estimation. Valuable resources, such as coherence time, impose limitations on the performance of quantum sensors. Machine learning, enabling learning and prediction without explicit knowledge, provides a powerful tool in optimizing quantum metrology with limited resources. This article reviews the fundamental principles, potential applications, and recent advancements in quantum metrology assisted by machine learning.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139588958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kazuki Hashimoto, Dmitri B. Horoshko, Maria V. Chekhova
Nonlinear interferometry with entangled photons allows for characterizing a sample without detecting the photons interacting with it. This method enables highly sensitive optical sensing in the wavelength regions where efficient detectors are still under development. Recently, nonlinear interferometry has been applied to interferometric measurement techniques with broadband light sources, such as Fourier-transform infrared spectroscopy and infrared optical coherence tomography. However, they have been demonstrated with photon pairs produced through spontaneous parametric down-conversion (SPDC) at a low parametric gain, where the average number of photons per mode is much smaller than one. The regime of high-gain SPDC offers several important advantages, such as the amplification of light after its interaction with the sample and a large number of photons per mode at the interferometer output. This work presents broadband spectroscopy and high-resolution optical coherence tomography with undetected photons generated via high-gain SPDC in an aperiodically poled lithium niobate crystal. To prove the principle, reflective Fourier-transform near-infrared spectroscopy with a spectral bandwidth of 17 THz and optical coherence tomography with an axial resolution of 11 µm are demonstrated.
{"title":"Broadband Spectroscopy and Interferometry with Undetected Photons at Strong Parametric Amplification","authors":"Kazuki Hashimoto, Dmitri B. Horoshko, Maria V. Chekhova","doi":"10.1002/qute.202300299","DOIUrl":"https://doi.org/10.1002/qute.202300299","url":null,"abstract":"Nonlinear interferometry with entangled photons allows for characterizing a sample without detecting the photons interacting with it. This method enables highly sensitive optical sensing in the wavelength regions where efficient detectors are still under development. Recently, nonlinear interferometry has been applied to interferometric measurement techniques with broadband light sources, such as Fourier-transform infrared spectroscopy and infrared optical coherence tomography. However, they have been demonstrated with photon pairs produced through spontaneous parametric down-conversion (SPDC) at a low parametric gain, where the average number of photons per mode is much smaller than one. The regime of high-gain SPDC offers several important advantages, such as the amplification of light after its interaction with the sample and a large number of photons per mode at the interferometer output. This work presents broadband spectroscopy and high-resolution optical coherence tomography with undetected photons generated via high-gain SPDC in an aperiodically poled lithium niobate crystal. To prove the principle, reflective Fourier-transform near-infrared spectroscopy with a spectral bandwidth of 17 THz and optical coherence tomography with an axial resolution of 11 µm are demonstrated.","PeriodicalId":501028,"journal":{"name":"Advanced Quantum Technologies","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139035157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：