Pub Date : 2024-03-08DOI: 10.1109/TQE.2024.3398410
Carlo Mastroianni;Francesco Plastina;Jacopo Settino;Andrea Vinci
Modern cloud/edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed edge/fog nodes, centralized data centers, and quantum devices. The optimal assignment and scheduling of computation on the different nodes is a very difficult problem, with NP-hard complexity. In this article, we explore the possibility of solving this problem with variational quantum algorithms, which can become a viable alternative to classical algorithms in the near future. In particular, we compare the performance, in terms of success probability, of two algorithms, i.e., quantum approximate optimization algorithm and variational quantum eigensolver (VQE). The simulation experiments, performed for a set of simple problems, show that the VQE algorithm ensures better performance when it is equipped with appropriate circuit ansatzes that are able to restrict the search space. Moreover, experiments executed on real quantum hardware show that the execution time, when increasing the size of the problem, grows much more slowly than the trend obtained with classical computation, which is known to be exponential.
{"title":"Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture","authors":"Carlo Mastroianni;Francesco Plastina;Jacopo Settino;Andrea Vinci","doi":"10.1109/TQE.2024.3398410","DOIUrl":"https://doi.org/10.1109/TQE.2024.3398410","url":null,"abstract":"Modern cloud/edge architectures need to orchestrate multiple layers of heterogeneous computing nodes, including pervasive sensors/actuators, distributed edge/fog nodes, centralized data centers, and quantum devices. The optimal assignment and scheduling of computation on the different nodes is a very difficult problem, with NP-hard complexity. In this article, we explore the possibility of solving this problem with variational quantum algorithms, which can become a viable alternative to classical algorithms in the near future. In particular, we compare the performance, in terms of success probability, of two algorithms, i.e., quantum approximate optimization algorithm and variational quantum eigensolver (VQE). The simulation experiments, performed for a set of simple problems, show that the VQE algorithm ensures better performance when it is equipped with appropriate circuit \u0000<italic>ansatzes</i>\u0000 that are able to restrict the search space. Moreover, experiments executed on real quantum hardware show that the execution time, when increasing the size of the problem, grows much more slowly than the trend obtained with classical computation, which is known to be exponential.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141187445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-07DOI: 10.1109/TQE.2024.3374251
Giovanni Acampora;Michele Grossi;Michael Schenk;Roberto Schiattarella
Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.
{"title":"Quantum Fuzzy Inference Engine for Particle Accelerator Control","authors":"Giovanni Acampora;Michele Grossi;Michael Schenk;Roberto Schiattarella","doi":"10.1109/TQE.2024.3374251","DOIUrl":"https://doi.org/10.1109/TQE.2024.3374251","url":null,"abstract":"Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10462538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.1109/TQE.2024.3373903
Wenbin Yu;Lei Yin;Chengjun Zhang;Yadang Chen;Alex X. Liu
Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum–classical hybrid architecture. This architecture utilizes a pretrained multilingual bidirectional encoder representations from transformer (BERT) model to obtain vector representations of words and combines the proposed batch upload quantum recurrent neural network (BUQRNN) and parameter nonshared batch upload quantum recurrent neural network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.
{"title":"Application of Quantum Recurrent Neural Network in Low-Resource Language Text Classification","authors":"Wenbin Yu;Lei Yin;Chengjun Zhang;Yadang Chen;Alex X. Liu","doi":"10.1109/TQE.2024.3373903","DOIUrl":"https://doi.org/10.1109/TQE.2024.3373903","url":null,"abstract":"Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum–classical hybrid architecture. This architecture utilizes a pretrained multilingual bidirectional encoder representations from transformer (BERT) model to obtain vector representations of words and combines the proposed batch upload quantum recurrent neural network (BUQRNN) and parameter nonshared batch upload quantum recurrent neural network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10461108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim at investigating the propagation of logical-shift errors (i.e., the unexpected modification of the qubit state) in QNNs. We propose a bottom–up evaluation reporting data from 13 322 547 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logical-shift error propagation through the qubits, channels, and subgrids, identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.
{"title":"Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks","authors":"Marzio Vallero;Emanuele Dri;Edoardo Giusto;Bartolomeo Montrucchio;Paolo Rech","doi":"10.1109/TQE.2024.3372880","DOIUrl":"https://doi.org/10.1109/TQE.2024.3372880","url":null,"abstract":"Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim at investigating the propagation of logical-shift errors (i.e., the unexpected modification of the qubit state) in QNNs. We propose a bottom–up evaluation reporting data from 13 322 547 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logical-shift error propagation through the qubits, channels, and subgrids, identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10458381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140546351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1109/TQE.2024.3368073
Qing Zhou;Xueming Tang;Songfeng Lu;Hao Yang
In this article, we develop a generic controlled alternate quantum walk model by combining parity-dependent quantum walks with distinct arbitrary memory lengths and propose a hash function (called QHFM-P) based on this model. The statistical properties of the proposed scheme are stable with respect to the coin parameters of the underlying controlled quantum walks, and with certain parameter values, the collision resistance property of QHFM-P is better than that of the state-of-the-art hash functions based on discrete quantum walks. Moreover, the proposed hash function can also maintain near-ideal statistical performance when the input message is of small length. In addition, we derive a type of inappropriate initial states of hash functions based on 1-D one-particle quantum walks (with ordinary shift operator) on cycles, with which all messages will be mapped to the same hash value, regardless of the angles adopted by the coin parameters.
{"title":"A Stable Hash Function Based on Parity-Dependent Quantum Walks With Memory (August 2023)","authors":"Qing Zhou;Xueming Tang;Songfeng Lu;Hao Yang","doi":"10.1109/TQE.2024.3368073","DOIUrl":"https://doi.org/10.1109/TQE.2024.3368073","url":null,"abstract":"In this article, we develop a generic controlled alternate quantum walk model by combining parity-dependent quantum walks with distinct arbitrary memory lengths and propose a hash function (called QHFM-P) based on this model. The statistical properties of the proposed scheme are stable with respect to the coin parameters of the underlying controlled quantum walks, and with certain parameter values, the collision resistance property of QHFM-P is better than that of the state-of-the-art hash functions based on discrete quantum walks. Moreover, the proposed hash function can also maintain near-ideal statistical performance when the input message is of small length. In addition, we derive a type of inappropriate initial states of hash functions based on 1-D one-particle quantum walks (with ordinary shift operator) on cycles, with which all messages will be mapped to the same hash value, regardless of the angles adopted by the coin parameters.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140192066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-19DOI: 10.1109/TQE.2024.3367234
Ryan L'Abbate;Anthony D'Onofrio;Samuel Stein;Samuel Yen-Chi Chen;Ang Li;Pin-Yu Chen;Juntao Chen;Ying Mao
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this article, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. In addition, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
{"title":"A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity","authors":"Ryan L'Abbate;Anthony D'Onofrio;Samuel Stein;Samuel Yen-Chi Chen;Ang Li;Pin-Yu Chen;Juntao Chen;Ying Mao","doi":"10.1109/TQE.2024.3367234","DOIUrl":"https://doi.org/10.1109/TQE.2024.3367234","url":null,"abstract":"Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this article, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. In addition, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10439653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140328892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-16DOI: 10.1109/TQE.2024.3366696
Gayane Vardoyan;Emily van Milligen;Saikat Guha;Stephanie Wehner;Don Towsley
We consider the problem of multipath entanglement distribution to a pair of nodes in a quantum network consisting of devices with nondeterministic entanglement swapping capabilities. Multipath entanglement distribution enables a network to establish end-to-end entangled links across any number of available paths with preestablished link-level entanglement. Probabilistic entanglement swapping, on the other hand, limits the amount of entanglement that is shared between the nodes; this is especially the case when, due to practical constraints, swaps must be performed in temporal proximity to each other. Limiting our focus to the case where only bipartite entanglement is generated across the network, we cast the problem as an instance of generalized flow maximization between two quantum end nodes wishing to communicate. We propose a mixed-integer quadratically constrained program (MIQCP) to solve this flow problem for networks with arbitrary topology. We then compute the overall network capacity, defined as the maximum number of Einstein–Podolsky–Rosen (EPR) states distributed to users per time unit, by solving the flow problem for all possible network states generated by probabilistic entangled link presence and absence, and subsequently by averaging over all network state capacities. The MIQCP can also be applied to networks with multiplexed links. While our approach for computing the overall network capacity has the undesirable property that the total number of states grows exponentially with link multiplexing capability, it nevertheless yields an exact solution that serves as an upper bound comparison basis for the throughput performance of more easily implementable yet nonoptimal entanglement routing algorithms.
{"title":"On the Bipartite Entanglement Capacity of Quantum Networks","authors":"Gayane Vardoyan;Emily van Milligen;Saikat Guha;Stephanie Wehner;Don Towsley","doi":"10.1109/TQE.2024.3366696","DOIUrl":"https://doi.org/10.1109/TQE.2024.3366696","url":null,"abstract":"We consider the problem of multipath entanglement distribution to a pair of nodes in a quantum network consisting of devices with nondeterministic entanglement swapping capabilities. Multipath entanglement distribution enables a network to establish end-to-end entangled links across any number of available paths with preestablished link-level entanglement. Probabilistic entanglement swapping, on the other hand, limits the amount of entanglement that is shared between the nodes; this is especially the case when, due to practical constraints, swaps must be performed in temporal proximity to each other. Limiting our focus to the case where only bipartite entanglement is generated across the network, we cast the problem as an instance of generalized flow maximization between two quantum end nodes wishing to communicate. We propose a mixed-integer quadratically constrained program (MIQCP) to solve this flow problem for networks with arbitrary topology. We then compute the overall network capacity, defined as the maximum number of Einstein–Podolsky–Rosen (EPR) states distributed to users per time unit, by solving the flow problem for all possible network states generated by probabilistic entangled link presence and absence, and subsequently by averaging over all network state capacities. The MIQCP can also be applied to networks with multiplexed links. While our approach for computing the overall network capacity has the undesirable property that the total number of states grows exponentially with link multiplexing capability, it nevertheless yields an exact solution that serves as an upper bound comparison basis for the throughput performance of more easily implementable yet nonoptimal entanglement routing algorithms.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10438882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140309965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since people became aware of the power of quantum phenomena in the domain of traditional computation, a great number of complex problems that were once considered intractable in the classical world have been tackled. The downsides of quantum supremacy are its high cost and unpredictability. Numerous researchers are relying on quantum simulators running on classical computers. The critical obstacle facing classical computers in the task of quantum simulation is its limited memory space. Quantum simulation intrinsically models the state evolution of quantum subsystems. Qubits are mathematically constructed in the Hilbert space whose size grows exponentially. Consequently, the scalability of the straightforward statevector approach is limited. It has been proven effective in adopting decision diagrams (DDs) to mitigate the memory cost issue in various fields. In recent years, researchers have adapted DDs into different forms for representing quantum states and performing quantum calculations efficiently. This leads to the study of DD-based quantum simulation. However, their advantage of memory efficiency does not let it replace the mainstream statevector and tensor network-based approaches. We argue the reason is the lack of effective parallelization strategies in performing calculations on DDs. In this article, we explore several strategies for parallelizing DD operations with a focus on leveraging them for quantum simulations. The target is to find the optimal parallelization strategies and improve the performance of DD-based quantum simulation. Based on the experiment results, our proposed strategy achieves a 2–3 times faster simulation of Grover's algorithm and random circuits than the state-of-the-art single-thread DD-based simulator DDSIM.
{"title":"Parallelizing Quantum Simulation With Decision Diagrams","authors":"Shaowen Li;Yusuke Kimura;Hiroyuki Sato;Masahiro Fujita","doi":"10.1109/TQE.2024.3364546","DOIUrl":"https://doi.org/10.1109/TQE.2024.3364546","url":null,"abstract":"Since people became aware of the power of quantum phenomena in the domain of traditional computation, a great number of complex problems that were once considered intractable in the classical world have been tackled. The downsides of quantum supremacy are its high cost and unpredictability. Numerous researchers are relying on quantum simulators running on classical computers. The critical obstacle facing classical computers in the task of quantum simulation is its limited memory space. Quantum simulation intrinsically models the state evolution of quantum subsystems. Qubits are mathematically constructed in the Hilbert space whose size grows exponentially. Consequently, the scalability of the straightforward statevector approach is limited. It has been proven effective in adopting decision diagrams (DDs) to mitigate the memory cost issue in various fields. In recent years, researchers have adapted DDs into different forms for representing quantum states and performing quantum calculations efficiently. This leads to the study of DD-based quantum simulation. However, their advantage of memory efficiency does not let it replace the mainstream statevector and tensor network-based approaches. We argue the reason is the lack of effective parallelization strategies in performing calculations on DDs. In this article, we explore several strategies for parallelizing DD operations with a focus on leveraging them for quantum simulations. The target is to find the optimal parallelization strategies and improve the performance of DD-based quantum simulation. Based on the experiment results, our proposed strategy achieves a 2–3 times faster simulation of Grover's algorithm and random circuits than the state-of-the-art single-thread DD-based simulator DDSIM.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10430382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140104208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-02DOI: 10.1109/TQE.2024.3361810
Alberto Tarable;Rudi Paolo Paganelli;Marco Ferrari
Information reconciliation (IR) is a key step in quantum key distribution (QKD). In recent years, blind reconciliation based on low-density parity-check (LDPC) codes has replaced Cascade as a standard de facto since it guarantees efficient IR without a priori quantum bit error rate estimation and with limited interactivity between the parties, which is essential in high key-rate and long-distance QKD links. In this article, a novel blind reconciliation scheme based on rateless protograph LDPC codes is proposed. The rate adaptivity, essential for blind reconciliation, is obtained by progressively splitting LDPC check nodes, which ensures a number of degrees of freedom larger than puncturing in code design. The protograph nature of the LDPC codes allows us to use the same designed codes with a large variety of sifted-key lengths, enabling block length flexibility, which is important in largely varying key-rate link conditions. The code design is based on a new protograph discretized density evolution tool.
{"title":"Rateless Protograph LDPC Codes for Quantum Key Distribution","authors":"Alberto Tarable;Rudi Paolo Paganelli;Marco Ferrari","doi":"10.1109/TQE.2024.3361810","DOIUrl":"https://doi.org/10.1109/TQE.2024.3361810","url":null,"abstract":"Information reconciliation (IR) is a key step in quantum key distribution (QKD). In recent years, blind reconciliation based on low-density parity-check (LDPC) codes has replaced Cascade as a standard de facto since it guarantees efficient IR without a priori quantum bit error rate estimation and with limited interactivity between the parties, which is essential in high key-rate and long-distance QKD links. In this article, a novel blind reconciliation scheme based on rateless protograph LDPC codes is proposed. The rate adaptivity, essential for blind reconciliation, is obtained by progressively splitting LDPC check nodes, which ensures a number of degrees of freedom larger than puncturing in code design. The protograph nature of the LDPC codes allows us to use the same designed codes with a large variety of sifted-key lengths, enabling block length flexibility, which is important in largely varying key-rate link conditions. The code design is based on a new protograph discretized density evolution tool.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418979","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140015165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantum protocols commonly require a certain number of quantum resource states to be available simultaneously. An important class of examples is quantum network protocols that require a certain number of entangled pairs. Here, we consider a setting in which a process generates a quantum resource state with some probability $p$ in each time step and stores it in a quantum memory that is subject to time-dependent noise. To maintain sufficient quality for an application, each resource state is discarded from the memory after $w$ time steps. Let $s$ be the number of desired resource states required by a protocol. We characterize the probability distribution $X_{(w,s)}$ of the ages of the quantum resource states, once $s$ states have been generated in a window $w$ . Combined with a time-dependent noise model, knowledge of this distribution allows for the calculation of fidelity statistics of the $s$ quantum resources. We also give exact solutions for the first and second moments of the waiting time $tau _{(w,s)}$ until $s$ resources are produced within a window $w$ , which provides information about the rate of the protocol. Since it is difficult to obtain general closed-form expressions for statistical quantities describing the expected waiting time $mathbb {E}(tau _{(w,s)})$ and the distribution $X_{(w,s)}$ , we present two novel results that aid their computation in certain parameter regimes. The methods presented in this work can be used to analyze and optimize the execution of quantum protocols. Specifically, with an example of a blind quantum computing protocol, we illustrate how they may be used to infer $w$ and $p$ to optimize the rate of successful protocol execution.
{"title":"Tools for the Analysis of Quantum Protocols Requiring State Generation Within a Time Window","authors":"Bethany Davies;Thomas Beauchamp;Gayane Vardoyan;Stephanie Wehner","doi":"10.1109/TQE.2024.3358674","DOIUrl":"https://doi.org/10.1109/TQE.2024.3358674","url":null,"abstract":"Quantum protocols commonly require a certain number of quantum resource states to be available simultaneously. An important class of examples is quantum network protocols that require a certain number of entangled pairs. Here, we consider a setting in which a process generates a quantum resource state with some probability \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000 in each time step and stores it in a quantum memory that is subject to time-dependent noise. To maintain sufficient quality for an application, each resource state is discarded from the memory after \u0000<inline-formula><tex-math>$w$</tex-math></inline-formula>\u0000 time steps. Let \u0000<inline-formula><tex-math>$s$</tex-math></inline-formula>\u0000 be the number of desired resource states required by a protocol. We characterize the probability distribution \u0000<inline-formula><tex-math>$X_{(w,s)}$</tex-math></inline-formula>\u0000 of the ages of the quantum resource states, once \u0000<inline-formula><tex-math>$s$</tex-math></inline-formula>\u0000 states have been generated in a window \u0000<inline-formula><tex-math>$w$</tex-math></inline-formula>\u0000. Combined with a time-dependent noise model, knowledge of this distribution allows for the calculation of fidelity statistics of the \u0000<inline-formula><tex-math>$s$</tex-math></inline-formula>\u0000 quantum resources. We also give exact solutions for the first and second moments of the waiting time \u0000<inline-formula><tex-math>$tau _{(w,s)}$</tex-math></inline-formula>\u0000 until \u0000<inline-formula><tex-math>$s$</tex-math></inline-formula>\u0000 resources are produced within a window \u0000<inline-formula><tex-math>$w$</tex-math></inline-formula>\u0000, which provides information about the rate of the protocol. Since it is difficult to obtain general closed-form expressions for statistical quantities describing the expected waiting time \u0000<inline-formula><tex-math>$mathbb {E}(tau _{(w,s)})$</tex-math></inline-formula>\u0000 and the distribution \u0000<inline-formula><tex-math>$X_{(w,s)}$</tex-math></inline-formula>\u0000, we present two novel results that aid their computation in certain parameter regimes. The methods presented in this work can be used to analyze and optimize the execution of quantum protocols. Specifically, with an example of a blind quantum computing protocol, we illustrate how they may be used to infer \u0000<inline-formula><tex-math>$w$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$p$</tex-math></inline-formula>\u0000 to optimize the rate of successful protocol execution.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10417724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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