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}
The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum era machines. In this article, we explore quantum neural networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multilayered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. In addition, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system.
{"title":"Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers","authors":"Alon Kukliansky;Marko Orescanin;Chad Bollmann;Theodore Huffmire","doi":"10.1109/TQE.2024.3359574","DOIUrl":"https://doi.org/10.1109/TQE.2024.3359574","url":null,"abstract":"The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum era machines. In this article, we explore quantum neural networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multilayered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. In addition, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10415536","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140014864","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 : 2024-01-24DOI: 10.1109/TQE.2024.3358193
Daniel Volya;Prabhat Mishra
Quantum computers present a compelling platform for the study of open quantum systems, namely, the nonunitary dynamics of a system. Here, we investigate and report digital simulations of Markovian nonunitary dynamics that converge to a unique steady state. The steady state is programmed as a desired target state, yielding semblance to a quantum state preparation protocol. By delegating ancilla qubits and system qubits, the system state is driven to the target state by repeatedly performing the following steps: 1) executing a designated system–ancilla entangling circuit; 2) measuring the ancilla qubits; and 3) reinitializing ancilla qubits to known states through active reset. While the ancilla qubits are measured and reinitialized to known states, the system qubits undergo a nonunitary evolution and are steered from arbitrary initial states to desired target states. We show results of the method by preparing arbitrary qubit states and qutrit (three-level) states on contemporary quantum computers. We also demonstrate that the state convergence can be accelerated by utilizing the readouts of the ancilla qubits to guide the protocol in a nonblind manner. Our work serves as a nontrivial example that incorporates and characterizes essential operations, such as qubit reuse (qubit reset), entangling circuits, and measurement. These operations are not only vital for near-term noisy intermediate-scale quantum applications but are also crucial for realizing future error-correcting codes.
{"title":"State Preparation on Quantum Computers via Quantum Steering","authors":"Daniel Volya;Prabhat Mishra","doi":"10.1109/TQE.2024.3358193","DOIUrl":"https://doi.org/10.1109/TQE.2024.3358193","url":null,"abstract":"Quantum computers present a compelling platform for the study of open quantum systems, namely, the nonunitary dynamics of a system. Here, we investigate and report digital simulations of Markovian nonunitary dynamics that converge to a unique steady state. The steady state is programmed as a desired target state, yielding semblance to a quantum state preparation protocol. By delegating ancilla qubits and system qubits, the system state is driven to the target state by repeatedly performing the following steps: 1) executing a designated system–ancilla entangling circuit; 2) measuring the ancilla qubits; and 3) reinitializing ancilla qubits to known states through active reset. While the ancilla qubits are measured and reinitialized to known states, the system qubits undergo a nonunitary evolution and are steered from arbitrary initial states to desired target states. We show results of the method by preparing arbitrary qubit states and qutrit (three-level) states on contemporary quantum computers. We also demonstrate that the state convergence can be accelerated by utilizing the readouts of the ancilla qubits to guide the protocol in a nonblind manner. Our work serves as a nontrivial example that incorporates and characterizes essential operations, such as qubit reuse (qubit reset), entangling circuits, and measurement. These operations are not only vital for near-term noisy intermediate-scale quantum applications but are also crucial for realizing future error-correcting codes.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10413647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140063485","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 : 2023-12-26DOI: 10.1109/TQE.2023.3347106
Sebastian Brandhofer;Ilia Polian;Kevin Krsulich
A limited number of qubits, high error rates, and limited qubit connectivity are major challenges for effective near-term quantum computations. Quantum circuit partitioning divides a quantum computation into classical postprocessing steps and a set of smaller scale quantum computations that individually require fewer qubits, lower qubit connectivity, and typically incur less error. However, as partitioning generally increases the duration of a quantum computation exponentially in the required partitioning effort, it is crucial to select optimal partitioning points, so-called cuts, and to use optimal cut realizations. In this work, we develop the first optimal partitioning method relying on quantum circuit knitting for optimal cut realizations and an optimal selection of wire cuts and gate cuts that trades off ancilla qubit insertions for a decrease in quantum computing time. Using this combination, the developed method demonstrates a reduction in quantum computing runtime by 41% on average compared to previous quantum circuit partitioning methods. Furthermore, the qubit requirement of the evaluated quantum circuits was reduced by 40% on average for a runtime budget of one hour and a sampling frequency of 1 kHz. These results highlight the optimality gap of previous quantum circuit partitioning methods and the possible extension in the computational reach of near-term quantum computers.
{"title":"Optimal Partitioning of Quantum Circuits Using Gate Cuts and Wire Cuts","authors":"Sebastian Brandhofer;Ilia Polian;Kevin Krsulich","doi":"10.1109/TQE.2023.3347106","DOIUrl":"https://doi.org/10.1109/TQE.2023.3347106","url":null,"abstract":"A limited number of qubits, high error rates, and limited qubit connectivity are major challenges for effective near-term quantum computations. Quantum circuit partitioning divides a quantum computation into classical postprocessing steps and a set of smaller scale quantum computations that individually require fewer qubits, lower qubit connectivity, and typically incur less error. However, as partitioning generally increases the duration of a quantum computation exponentially in the required partitioning effort, it is crucial to select optimal partitioning points, so-called cuts, and to use optimal cut realizations. In this work, we develop the first optimal partitioning method relying on quantum circuit knitting for optimal cut realizations and an optimal selection of wire cuts and gate cuts that trades off ancilla qubit insertions for a decrease in quantum computing time. Using this combination, the developed method demonstrates a reduction in quantum computing runtime by 41% on average compared to previous quantum circuit partitioning methods. Furthermore, the qubit requirement of the evaluated quantum circuits was reduced by 40% on average for a runtime budget of one hour and a sampling frequency of 1 kHz. These results highlight the optimality gap of previous quantum circuit partitioning methods and the possible extension in the computational reach of near-term quantum computers.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10374226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504439","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 : 2023-12-26DOI: 10.1109/TQE.2023.3347476
Jordi Pérez-Guijarro;Alba Pagés-Zamora;Javier R. Fonollosa
The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantages are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This article investigates under which conditions they are found to be equivalent or, at least, highly related. This relation is analyzed by considering two definitions of learning speed-up: one tied to the distribution and another that is distribution-independent. In both cases, the existence of efficient algorithms to generate training sets emerges as the cornerstone of such conditions, although, for the distribution-independent definition, additional mild conditions must also be met. Finally, these results are applied to prove that there is a quantum speed-up for some learning tasks based on the prime factorization problem, assuming the classical intractability of this problem.
{"title":"Relation Between Quantum Advantage in Supervised Learning and Quantum Computational Advantage","authors":"Jordi Pérez-Guijarro;Alba Pagés-Zamora;Javier R. Fonollosa","doi":"10.1109/TQE.2023.3347476","DOIUrl":"https://doi.org/10.1109/TQE.2023.3347476","url":null,"abstract":"The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantages are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This article investigates under which conditions they are found to be equivalent or, at least, highly related. This relation is analyzed by considering two definitions of learning speed-up: one tied to the distribution and another that is distribution-independent. In both cases, the existence of efficient algorithms to generate training sets emerges as the cornerstone of such conditions, although, for the distribution-independent definition, additional mild conditions must also be met. Finally, these results are applied to prove that there is a quantum speed-up for some learning tasks based on the prime factorization problem, assuming the classical intractability of this problem.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10374234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694994","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 : 2023-12-15DOI: 10.1109/TQE.2023.3343625
Fang Qi;Kaitlin N. Smith;Travis LeCompte;Nian-feng Tzeng;Xu Yuan;Frederic T. Chong;Lu Peng
While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the state-of-the-art metric used to gauge quantum program performance. The ESP suffers poor prediction since it fails to account for the unique combination of circuit structure, quantum state, and quantum computer properties specific to each program execution. Thus, an urgent need exists for a systematic approach that can elucidate various noise impacts and accurately and robustly predict quantum computer success rates, emphasizing application and device scaling. In this article, we propose quantum vulnerability analysis (QVA) to systematically quantify the error impact on quantum applications and address the gap between current success rate (SR) estimators and real quantum computer results. The QVA determines the cumulative quantum vulnerability (CQV) of the target quantum computation, which quantifies the quantum error impact based on the entire algorithm applied to the target quantum machine. By evaluating the CQV with well-known benchmarks on three 27-qubit quantum computers, the CQV success estimation outperforms the estimated probability of success state-of-the-art prediction technique by achieving on average six times less relative prediction error, with best cases at 30 times, for benchmarks with a real SR rate above 0.1%. Direct application of QVA has been provided that helps researchers choose a promising compiling strategy at compile time.
{"title":"Quantum Vulnerability Analysis to Guide Robust Quantum Computing System Design","authors":"Fang Qi;Kaitlin N. Smith;Travis LeCompte;Nian-feng Tzeng;Xu Yuan;Frederic T. Chong;Lu Peng","doi":"10.1109/TQE.2023.3343625","DOIUrl":"https://doi.org/10.1109/TQE.2023.3343625","url":null,"abstract":"While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the state-of-the-art metric used to gauge quantum program performance. The ESP suffers poor prediction since it fails to account for the unique combination of circuit structure, quantum state, and quantum computer properties specific to each program execution. Thus, an urgent need exists for a systematic approach that can elucidate various noise impacts and accurately and robustly predict quantum computer success rates, emphasizing application and device scaling. In this article, we propose quantum vulnerability analysis (QVA) to systematically quantify the error impact on quantum applications and address the gap between current success rate (SR) estimators and real quantum computer results. The QVA determines the cumulative quantum vulnerability (CQV) of the target quantum computation, which quantifies the quantum error impact based on the entire algorithm applied to the target quantum machine. By evaluating the CQV with well-known benchmarks on three 27-qubit quantum computers, the CQV success estimation outperforms the estimated probability of success state-of-the-art prediction technique by achieving on average six times less relative prediction error, with best cases at 30 times, for benchmarks with a real SR rate above 0.1%. Direct application of QVA has been provided that helps researchers choose a promising compiling strategy at compile time.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10361567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434858","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 : 2023-12-11DOI: 10.1109/TQE.2023.3341151
Paolo Fittipaldi;Anastasios Giovanidis;Frédéric Grosshans
Quantum internetworking is a recent field that promises numerous interesting applications, many of which require the distribution of entanglement between arbitrary pairs of users. This article deals with the problem of scheduling in an arbitrary entanglement swapping quantum network—often called first-generation quantum network—in its general topology, multicommodity, loss-aware formulation. We introduce a linear algebraic framework that exploits quantum memory through the creation of intermediate entangled links. The framework is then employed to apply Lyapunov drift minimization (a standard technique in classical network science) to mathematically derive a natural class of scheduling policies for quantum networks minimizing the square norm of the user demand backlog. Moreover, an additional class of Max-Weight-inspired policies is proposed and benchmarked, reducing significantly the computation cost at the price of a slight performance degradation. The policies are compared in terms of information availability, localization, and overall network performance through an ad hoc simulator that admits user-provided network topologies and scheduling policies in order to showcase the potential application of the provided tools to quantum network design.
{"title":"A Linear Algebraic Framework for Dynamic Scheduling Over Memory-Equipped Quantum Networks","authors":"Paolo Fittipaldi;Anastasios Giovanidis;Frédéric Grosshans","doi":"10.1109/TQE.2023.3341151","DOIUrl":"10.1109/TQE.2023.3341151","url":null,"abstract":"Quantum internetworking is a recent field that promises numerous interesting applications, many of which require the distribution of entanglement between arbitrary pairs of users. This article deals with the problem of scheduling in an arbitrary entanglement swapping quantum network—often called first-generation quantum network—in its general topology, multicommodity, loss-aware formulation. We introduce a linear algebraic framework that exploits quantum memory through the creation of intermediate entangled links. The framework is then employed to apply Lyapunov drift minimization (a standard technique in classical network science) to mathematically derive a natural class of scheduling policies for quantum networks minimizing the square norm of the user demand backlog. Moreover, an additional class of Max-Weight-inspired policies is proposed and benchmarked, reducing significantly the computation cost at the price of a slight performance degradation. The policies are compared in terms of information availability, localization, and overall network performance through an ad hoc simulator that admits user-provided network topologies and scheduling policies in order to showcase the potential application of the provided tools to quantum network design.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10352642","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139360177","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 : 2023-12-04DOI: 10.1109/TQE.2023.3338970
Soronzonbold Otgonbaatar;Dieter Kranzlmüller
This article examines the current status of quantum computing (QC) in Earth observation and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persistent challenges of profiting from quantum advantage and finding the optimal sharing between high-performance computing (HPC) and QC. We then assess some parameterized quantum circuit models transpiled into a Clifford+T universal gate set. The T-gates shed light on the quantum resources required to deploy quantum models, either on an HPC system or several QC systems. In particular, if the T-gates cannot be simulated efficiently on an HPC system, we can apply a quantum computer and its computational power over conventional techniques. Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. Lastly, we defined the optimal sharing between an HPC+QC system for executing QML models for hyperspectral satellite images. These are a unique dataset compared with other satellite images since they have a limited number of input quantum bits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers.
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