Priyabrata Senapati, Tushar M. Athawale, D. Pugmire, Qiang Guan
Noise in quantum computers presents a challenge for the users of quantum computing despite the rapid progress we have seen in the past few years in building quantum computers. Existing works have addressed the noise in quantum computers using a variety of mitigation techniques since error correction requires a large number of qubits which is infeasible at present. One of the consequences of quantum computing noise is that users are unable to reproduce similar output from the same quantum computer at different times, let alone from various quantum computers. In this work, we have made initial attempts to visualize quantum basis states for all the circuits that were used in quantum machine learning from various quantum computers and noise-free quantum simulators. We have opened up a pathway for further research into this field where we will be able to isolate noisy states from non-noisy states leading to efficient error mitigation. This is where our work provides an important step in the direction of efficient error mitigation. Our work also provides a ground for quantum noise visualization in the case of large numbers of qubits.
{"title":"Advancing Comprehension of Quantum Application Outputs: A Visualization Technique","authors":"Priyabrata Senapati, Tushar M. Athawale, D. Pugmire, Qiang Guan","doi":"10.1145/3588983.3596689","DOIUrl":"https://doi.org/10.1145/3588983.3596689","url":null,"abstract":"Noise in quantum computers presents a challenge for the users of quantum computing despite the rapid progress we have seen in the past few years in building quantum computers. Existing works have addressed the noise in quantum computers using a variety of mitigation techniques since error correction requires a large number of qubits which is infeasible at present. One of the consequences of quantum computing noise is that users are unable to reproduce similar output from the same quantum computer at different times, let alone from various quantum computers. In this work, we have made initial attempts to visualize quantum basis states for all the circuits that were used in quantum machine learning from various quantum computers and noise-free quantum simulators. We have opened up a pathway for further research into this field where we will be able to isolate noisy states from non-noisy states leading to efficient error mitigation. This is where our work provides an important step in the direction of efficient error mitigation. Our work also provides a ground for quantum noise visualization in the case of large numbers of qubits.","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122019613","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}
Quantum circuit simulations are applied in more and more circumstances as the quantum computing community becomes broader. It helps researchers to evaluate the quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilizes either CPU or GPU to store and calculate the state vector, which results in resources stravation. Morever, the mamximum number of qubits supported by simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benifits the data movement between CPU and GPU.
{"title":"HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems","authors":"Bo Zhang, B. Fang, Qiang Guan, A. Li, Dingwen Tao","doi":"10.1145/3588983.3596679","DOIUrl":"https://doi.org/10.1145/3588983.3596679","url":null,"abstract":"Quantum circuit simulations are applied in more and more circumstances as the quantum computing community becomes broader. It helps researchers to evaluate the quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilizes either CPU or GPU to store and calculate the state vector, which results in resources stravation. Morever, the mamximum number of qubits supported by simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benifits the data movement between CPU and GPU.","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957653","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}
Ethan H. Hansen, Xinpeng Li, Daniel T. Chen, Vinooth Kulkarni, V. Chaudhary, Qiang Guan, Ji Liu, Shuai Xu
At present, quantum computing is in the noisy intermediate-scale quantum (NISQ) era, marked by small qubit counts and high levels of noise and errors. Building a quantum computer with sufficient size and low error rates remains a challenge. In many promising quantum hardware architectures, the state of the physical qubits is controlled by pulse signals. In this paper, we will explore pulse-level control of quantum gates. Unlike the usual gate-level control, the pulse-level control provides increased flexibility and reduced latency. One direct application of pulse-level control is Variational Quantum Algorithms (VQA). The inherent properties of VQA allow us to disregard the gate-based evolution process and concentrate on the final target loss function. From the perspective of pulse-level control, we can generate a sequence of pulse-based gates to rotate the quantum state directly to the desired destination. In this study, we demonstrate an application of pulse-level VQA in estimating the ground state energy of molecular hydrogen. Our experiment is conducted using Quanlse which specializes in pulse-level control of quantum gates. The experimental results reveal a rapid convergence rate of optimization iterations, and the control pulses for each pulse-based gate is also displayed. These results highlight the considerable potential of pulse-level control techniques in practical applications.
{"title":"Pulse-Level Variational Quantum Algorithms for Molecular Energy Calculations using Quanlse","authors":"Ethan H. Hansen, Xinpeng Li, Daniel T. Chen, Vinooth Kulkarni, V. Chaudhary, Qiang Guan, Ji Liu, Shuai Xu","doi":"10.1145/3588983.3596686","DOIUrl":"https://doi.org/10.1145/3588983.3596686","url":null,"abstract":"At present, quantum computing is in the noisy intermediate-scale quantum (NISQ) era, marked by small qubit counts and high levels of noise and errors. Building a quantum computer with sufficient size and low error rates remains a challenge. In many promising quantum hardware architectures, the state of the physical qubits is controlled by pulse signals. In this paper, we will explore pulse-level control of quantum gates. Unlike the usual gate-level control, the pulse-level control provides increased flexibility and reduced latency. One direct application of pulse-level control is Variational Quantum Algorithms (VQA). The inherent properties of VQA allow us to disregard the gate-based evolution process and concentrate on the final target loss function. From the perspective of pulse-level control, we can generate a sequence of pulse-based gates to rotate the quantum state directly to the desired destination. In this study, we demonstrate an application of pulse-level VQA in estimating the ground state energy of molecular hydrogen. Our experiment is conducted using Quanlse which specializes in pulse-level control of quantum gates. The experimental results reveal a rapid convergence rate of optimization iterations, and the control pulses for each pulse-based gate is also displayed. These results highlight the considerable potential of pulse-level control techniques in practical applications.","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130060238","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}
Betis Baheri, V. Chaudhary, A. Li, Shuai Xu, B. Fang, Qiang Guan
Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.
{"title":"Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput","authors":"Betis Baheri, V. Chaudhary, A. Li, Shuai Xu, B. Fang, Qiang Guan","doi":"10.1145/3588983.3596688","DOIUrl":"https://doi.org/10.1145/3588983.3596688","url":null,"abstract":"Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132727849","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}
Quantum communication between quantum processors offers new capabilities and applications in quantum computing. However, Noisy Intermediate-Scale Quantum (NISQ) devices face challenges such as decoherence, entanglement distillation latency, high communication-to-data qubit ratio, quantum error correction, and scalability. Inspired by distributed systems concepts, this paper presents two solutions for optimizing quantum communication: advanced quantum repeaters and machine learning for quantum network optimization. Advanced quantum repeaters will leverage topological quantum states to improve entanglement generation, swapping, and distillation efficiency. Concurrently, machine learning techniques using multi-armed bandit algorithms will dynamically allocate quantum processing resources across distributed quantum networks. This optimization enhances the efficiency of quantum teleportation protocols and reduces computational costs. By integrating advanced quantum repeaters with machine learning optimization, the proposed solutions aim to address the challenges in quantum communication.
{"title":"Robust and Efficient Quantum Communication","authors":"Connor Howe, Xinran Wang, Ali Anwar","doi":"10.1145/3588983.3596687","DOIUrl":"https://doi.org/10.1145/3588983.3596687","url":null,"abstract":"Quantum communication between quantum processors offers new capabilities and applications in quantum computing. However, Noisy Intermediate-Scale Quantum (NISQ) devices face challenges such as decoherence, entanglement distillation latency, high communication-to-data qubit ratio, quantum error correction, and scalability. Inspired by distributed systems concepts, this paper presents two solutions for optimizing quantum communication: advanced quantum repeaters and machine learning for quantum network optimization. Advanced quantum repeaters will leverage topological quantum states to improve entanglement generation, swapping, and distillation efficiency. Concurrently, machine learning techniques using multi-armed bandit algorithms will dynamically allocate quantum processing resources across distributed quantum networks. This optimization enhances the efficiency of quantum teleportation protocols and reduces computational costs. By integrating advanced quantum repeaters with machine learning optimization, the proposed solutions aim to address the challenges in quantum communication.","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125718400","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}
Variational Quantum Algorithms (VQA) aim to enhance the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. These algorithms utilize parameterized circuits and classical optimizers to iteratively execute circuits with varying parameters. However, VQA faces computational overheads due to repeated iterations and random restarts. Prior work suggests using basic sub-graphs to transfer parameters for the input graph, reducing optimizer overheads but limiting applicability to structured regular graphs. In real-world applications, random irregular graphs are common, and existing methods are not scalable or practical for such graphs. This paper presents a framework that aims to improve random irregular graphs in VQA. The framework uses graph similarity and important features like total edge counts, average edge counts, and variance. It follows an iterative process to choose basis sub-graphs from a small database and adjust parameters accordingly. Classical optimizers then utilize these parameters to determine when to restart and perform gradient descent. This approach increases the chances of reaching global maximum points.
{"title":"Efficient QAOA Optimization using Directed Restarts and Graph Lookup","authors":"M. Wang, B. Fang, A. Li, Prashant J. Nair","doi":"10.1145/3588983.3596680","DOIUrl":"https://doi.org/10.1145/3588983.3596680","url":null,"abstract":"Variational Quantum Algorithms (VQA) aim to enhance the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. These algorithms utilize parameterized circuits and classical optimizers to iteratively execute circuits with varying parameters. However, VQA faces computational overheads due to repeated iterations and random restarts. Prior work suggests using basic sub-graphs to transfer parameters for the input graph, reducing optimizer overheads but limiting applicability to structured regular graphs. In real-world applications, random irregular graphs are common, and existing methods are not scalable or practical for such graphs. This paper presents a framework that aims to improve random irregular graphs in VQA. The framework uses graph similarity and important features like total edge counts, average edge counts, and variance. It follows an iterative process to choose basis sub-graphs from a small database and adjust parameters accordingly. Classical optimizers then utilize these parameters to determine when to restart and perform gradient descent. This approach increases the chances of reaching global maximum points.","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124225983","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}
This paper presents a quantum architecture search (QAS) framework using quantum reinforcement learning (QRL) to generate quantum gate sequences for multi-qubit GHZ states. The proposed framework employs the asynchronous advantage actor-critic (A3C) algorithm to optimize the QRL agent, which has access to Pauli-X, Y, Z expectation values and a predefined set of quantum operations. Our approach does not require any prior knowledge of quantum physics. The framework can be used with other QRL architectures or optimization methods to explore gate synthesis and compilation for various quantum states.
{"title":"Quantum Reinforcement Learning for Quantum Architecture Search","authors":"Samuel Yen-Chi Chen","doi":"10.1145/3588983.3596692","DOIUrl":"https://doi.org/10.1145/3588983.3596692","url":null,"abstract":"This paper presents a quantum architecture search (QAS) framework using quantum reinforcement learning (QRL) to generate quantum gate sequences for multi-qubit GHZ states. The proposed framework employs the asynchronous advantage actor-critic (A3C) algorithm to optimize the QRL agent, which has access to Pauli-X, Y, Z expectation values and a predefined set of quantum operations. Our approach does not require any prior knowledge of quantum physics. The framework can be used with other QRL architectures or optimization methods to explore gate synthesis and compilation for various quantum states.","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128780150","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}
{"title":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","authors":"","doi":"10.1145/3588983","DOIUrl":"https://doi.org/10.1145/3588983","url":null,"abstract":"","PeriodicalId":342715,"journal":{"name":"Proceedings of the 2023 International Workshop on Quantum Classical Cooperative","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114839708","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}
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