2021 International Conference on Rebooting Computing (ICRC)最新文献

Linking Sparse Coding Dictionaries for Representation Learning 连接稀疏编码字典用于表示学习

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00022

Nicki Barari, Edward Kim

引用次数: 0

Basic Operations And Structure Of An FPGA Accelerator For Parallel Bit Pattern Computation 用于并行位模式计算的FPGA加速器的基本操作和结构

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00029

H. Dietz, P. Eberhart, Ashley Rule

{"title":"Basic Operations And Structure Of An FPGA Accelerator For Parallel Bit Pattern Computation","authors":"H. Dietz, P. Eberhart, Ashley Rule","doi":"10.1109/ICRC53822.2021.00029","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00029","url":null,"abstract":"Parallel Bit Pattern computing (PBP) has been proposed as a way to dramatically reduce power consumption per computation by minimizing the total number of gate operations. In part, this reduction is accomplished by employing aggressive compiler optimization technology to gate-level representations of computations at runtime. Massive SIMD parallelism is used to obtain speedups while executing the optimized bit-serial code. However, the PBP model also can potentially exponentially reduce the number of active gates for each such operation by recognizing and operating on symbolically-compressed patterns of bits, rather than on each individual bit within a vector. This not only provides for efficient execution of traditional parallel code, but by using bit vectors to represent entangled superposition, enables quantum-like computation to be efficiently implemented using conventional circuitry. Building on lessons learned from various software and Verilog prototypes, this paper proposes a new set of basic operations and interface structure suitable for using inexpensive Xilinx Zynq-7000 boards to implement FGPA-hardware-accelerated PBP computation. Emphasis is on how these operations will implement quantum-like computation, as the first prototype system is currently still under development.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122988067","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}

引用次数: 0

Program Committee ICRC 2021 红十字国际委员会2021项目委员会

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/icrc53822.2021.00009

引用次数: 0

Discriminating Quantum States with Quantum Machine Learning 用量子机器学习判别量子态

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00018

David Quiroga, Prasanna Date, R. Pooser

{"title":"Discriminating Quantum States with Quantum Machine Learning","authors":"David Quiroga, Prasanna Date, R. Pooser","doi":"10.1109/ICRC53822.2021.00018","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00018","url":null,"abstract":"Quantum machine learning (QML) algorithms have obtained great relevance in the machine learning (ML) field due to the promise of quantum speedups when performing basic linear algebra subroutines (BLAS), a fundamental element in most ML algorithms. By making use of BLAS operations, we propose, implement and analyze a quantum k-means (qk-means) algorithm with a low time complexity of $mathcal{O}(NK log(D)I/C)$ to apply it to the fundamental problem of discriminating quantum states at readout. Discriminating quantum states allows the identification of quantum states $vert 0rangle$ and $vert 1rangle$ from low-level in-phase and quadrature signal (IQ) data, and can be done using custom ML models. In order to reduce dependency on a classical computer, we use the qk-means to perform state discrimination on the IBMQ Bogota device and managed to find assignment fidelities of up to 98.7% that were only marginally lower than that of the k-means algorithm. We also performed a cross-talk benchmark on the quantum device by applying both algorithms to perform state discrimination on a combination of quantum states and using Pearson Correlation coefficients and assignment fidelities of discrimination results to conclude on the presence of cross-talk on qubits. Evidence shows cross-talk in the (1, 2) and (2, 3) neighboring qubit couples for the analyzed device.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124874965","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}

引用次数: 6

Enhanced methods for Evolution in-Materio Processors Evolution in-Materio处理器的增强方法

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00026

Benedict. A. H. Jones, N. A. Moubayed, D. Zeze, C. Groves

{"title":"Enhanced methods for Evolution in-Materio Processors","authors":"Benedict. A. H. Jones, N. A. Moubayed, D. Zeze, C. Groves","doi":"10.1109/ICRC53822.2021.00026","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00026","url":null,"abstract":"Evolution-in-Materio (EiM) is an unconventional computing paradigm, which uses an Evolutionary Algorithm (EA) to configure a material's parameters so that it can perform a computational task. While EiM processors show promise, slow manufacturing and physical experimentation hinder their development. Simulations based on a physical model were used to efficiently investigate three specific enhancements to EiM processors which operate as classifiers. Firstly, an adapted Differential Evolution algorithm that includes batching and a validation dataset. This allows more generational updates and a validation metric which could tune hyper-parameters. Secondly, the introduction of Binary Cross Entropy as an objective function for the EA, a continuous fitness metric with several advantages over the commonly used classification error objective function. Finally, the use of regression to quickly assess the material processor's output states and produce an optimal readout layer, a significant improvement over fixed or evolved interpretation schemes which can ‘hide’ the true performance of a material processor. Together these enhancements provide guidance on the production of more flexible, better performing, and robust EiM processors.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128164451","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}

引用次数: 0

Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling 评估用于科学随机抽样的神经形态平台

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00019

J. Aimone, R. Lehoucq, William M. Severa, J. D. Smith

{"title":"Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling","authors":"J. Aimone, R. Lehoucq, William M. Severa, J. D. Smith","doi":"10.1109/ICRC53822.2021.00019","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00019","url":null,"abstract":"Recent advances in neuromorphic algorithm development have shown that neural inspired architectures can efficiently solve scientific computing problems including graph decision problems and partial-integro differential equations (PIDEs). The latter requires the generation of a large number of samples from a stochastic process. While the Monte Carlo approximation of the solution of the PIDEs converges with an increasing number of sampled neuromorphic trajectories, the fidelity of samples from a given stochastic process using neuromorphic hardware requires verification. Such an exercise increases our trust in this emerging hardware and works toward unlocking its energy and scaling efficiency for scientific purposes such as synthetic data generation and stochastic simulation. In this paper, we focus our verification efforts on a one-dimensional Ornstein- Uhlenbeck stochastic differential equation. Using a discrete-time Markov chain approximation, we sample trajectories of the stochastic process across a variety of parameters on an Intel 8- Loihi chip Nahuku neuromorphic platform. Using relative entropy as a verification measure, we demonstrate that the random samples generated on Loihi are, in an average sense, acceptable. Finally, we demonstrate how Loihi's fidelity to the distribution changes as a function of the parameters of the Ornstein- Uhlenbeck equation, highlighting a trade-off between the lower-precision random number generation of the neuromorphic platform and our algorithm's ability to represent a discrete-time Markov chain.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127043750","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}

引用次数: 2

Optimized Quantum Program Execution Ordering to Mitigate Errors in Simulations of Quantum Systems 优化量子程序执行顺序以减轻量子系统模拟中的错误

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.48550/arXiv.2203.12713

T. Tomesh, Kaiwen Gui, P. Gokhale, Yunong Shi, F. Chong, M. Martonosi, Martin Suchara

{"title":"Optimized Quantum Program Execution Ordering to Mitigate Errors in Simulations of Quantum Systems","authors":"T. Tomesh, Kaiwen Gui, P. Gokhale, Yunong Shi, F. Chong, M. Martonosi, Martin Suchara","doi":"10.48550/arXiv.2203.12713","DOIUrl":"https://doi.org/10.48550/arXiv.2203.12713","url":null,"abstract":"Simulating the time evolution of a physical system at quantum mechanical levels of detail - known as Hamiltonian Simulation (HS) - is an important and interesting problem across physics and chemistry. For this task, algorithms that run on quantum computers are known to be exponentially faster than classical algorithms; in fact, this application motivated Feynman to propose the construction of quantum computers. Nonetheless, there are challenges in reaching this performance potential. Prior work has focused on compiling circuits (quantum programs) for HS with the goal of maximizing either accuracy or gate cancellation. Our work proposes a compilation strategy that simultaneously advances both goals. At a high level, we use classical optimizations such as graph coloring and travelling salesperson to order the execution of quantum programs. Specifically, we group together mutually commuting terms in the Hamiltonian (a matrix characterizing the quantum mechanical system) to improve the accuracy of the simulation. We then rearrange the terms within each group to maximize gate cancellation in the final quantum circuit. These optimizations work together to improve HS performance and result in an average 40% reduction in circuit depth. This work advances the frontier of HS which in turn can advance physical and chemical modeling in both basic and applied sciences.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129116561","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}

引用次数: 3

Reoptimization of Quantum Circuits via Hierarchical Synthesis 基于层次综合的量子电路再优化

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00016

Xin-Chuan Wu, M. Davis, F. Chong, Costin Iancu

{"title":"Reoptimization of Quantum Circuits via Hierarchical Synthesis","authors":"Xin-Chuan Wu, M. Davis, F. Chong, Costin Iancu","doi":"10.1109/ICRC53822.2021.00016","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00016","url":null,"abstract":"The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block opti-mization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noisy simulations, and large-size circuits on analytical models. Our technique can be applied after existing optimizations to achieve higher circuit fidelity. Using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compiler optimizations such as t|ket). When executed on the IBM Athens system, shorter depth leads to higher circuit fidelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits, Our technique is the first demonstration of successfully employing and scaling synthesis in the compilation tool chain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains to further improve the circuit fidelity.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123298875","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}

引用次数: 8

Organizing Committee ICRC 2021 组委会红十字国际委员会2021

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/icrc53822.2021.00008

引用次数: 0

Digital Annealing Route to Complex Magnetic Phase Discovery 复杂磁相发现的数字退火路径

2021 International Conference on Rebooting Computing (ICRC) Pub Date : 2021-11-01 DOI: 10.1109/ICRC53822.2021.00027

Akshat A. Jha, Eliana L. Stoyanoff, G. Khundzakishvili, Paul Kairys, Hayato Ushijima-Mwesigwa, A. Banerjee

{"title":"Digital Annealing Route to Complex Magnetic Phase Discovery","authors":"Akshat A. Jha, Eliana L. Stoyanoff, G. Khundzakishvili, Paul Kairys, Hayato Ushijima-Mwesigwa, A. Banerjee","doi":"10.1109/ICRC53822.2021.00027","DOIUrl":"https://doi.org/10.1109/ICRC53822.2021.00027","url":null,"abstract":"Emerging computational paradigms and device architectures may provide more robust, efficient routes for scientific discovery compared to traditional architectures. One such paradigm is that of the Digital Annealer (DA), a hardware-accelerated device designed to implement Markov Chain Monte Carlo algorithms with lower overhead than traditional device architectures. To better understand the applicability of digital annealing for scientific discovery, we explore the application of the fully connected 8000 variable Fujitsu DA for a complex material science problem. We identify the intricate phases and the phase transitions of an Ising model defined on an extended Shastry-Sutherland lattice, which is believed to effectively describe the magnetic physics in a host of potential spintronic materials. To validate our implementation, we identify all previously known solutions to the model, including the nontrivial and highly non-degenerate 1/3,1/2,1/5, and 5/9 fractional magnetization plateaus. Accounting for the boundary effects, we find that the Fujitsu DA provides immaculate quality of solutions, even close to a phase transition where classical Monte Carlo codes can often struggle to converge. We then take advantage of the full connectivity of the DA, and its tunable parameters to discover new phases and their interesting spin motifs not previously known. We conclude that digital annealing provides a novel route for discovery of complex magnetic phases, opening avenues for the understanding and engineering of spintronics materials.","PeriodicalId":139766,"journal":{"name":"2021 International Conference on Rebooting Computing (ICRC)","volume":"33 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114040785","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}

引用次数: 2