Quantum Computer Architecture: Towards Full-Stack Quantum Accelerators

Bertels, Koen; Sarkar, Aritra; Hubregtsen, Thomas; Serrao, M.; Mouedenne, A.A.; Yadav, A.; Krol, A. M.; Ashraf, Imran

doi:10.23919/date48585.2020.9116502

Cited by 46 publications

(44 citation statements)

References 6 publications

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“…There are several techniques for building a quantum computer, and the way it is currently done is by combining multiple various multicore processors [9]. All els, quantum circuit models, adiabatic quantum computing, measurement-based quantum computing, and topological quantum computing, being equivalent to each other within the reduction of polynomial-time.…”

Section: Quantum Techniquesmentioning

confidence: 99%

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Formulation of the Social Workers’ Problem in Quadratic Unconstrained Binary Optimization Form and Solve It on a Quantum Computer

Atchade-Adelomou¹,

Golobardes²,

Cardona³

2020

JCC

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Section: Quantum Techniquesmentioning

confidence: 99%

“…The QUBO [7] [8] [9] is one of the most relevant categories of optimization problems. It was designed to solve quantum annealing problems and maps to perfection with D-wave technology [7].…”

Section: Introductionmentioning

confidence: 99%

Formulation of the Social Workers’ Problem in Quadratic Unconstrained Binary Optimization Form and Solve It on a Quantum Computer

Atchade-Adelomou¹,

Golobardes²,

Cardona³

2020

JCC

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“…The key factor is the significantly improved prediction accuracy by making networks deeper, known as deep learning; however, with the growing depth of neural networks, the storage and computation requirement sharply increases 15 , which gradually becomes the performance bottleneck in classical computers (e.g., the well-known memory wall issues 16 ). Among all computing platforms, the quantum computer is one of the most promising ones to address such challenges 17,18 to act as a quantum accelerator for deep neural networks [19][20][21] . Unlike classical computers with N digit bits to represent 1 N-bit number at one time, quantum computers with M qbits can represent 2 M M-bit numbers and manipulate them at the same time 22 .…”

Section: Introductionmentioning

confidence: 99%

Can Quantum Computers Learn Like Classical Computers? A Co-Design Framework of Machine Learning and Quantum Circuits

Jiang

Xiong

Shi

2020

Preprint

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Abstract Despite the pursuit of quantum supremacy in various applications, the power of quantum computers in machine learning (such as neural network models) has mostly remained unknown, primarily due to a missing link that effectively designs a neural network model suitable for quantum circuit implementation. In this article, we present the first co-design framework, namelyQuantumFlow, to fixed the missing link. QuantumFlow consists of a novel quantum-friendly neural network (QF-Net) design, an automatic tool (QF-Map) to generate the quantum circuit (QF-Circ) for QF-Net, and a theoretic-based execution engine (QF-FB) to efficiently support the training of QF-Net on a classical computer. We discover that, in order to make full use of the strength of quantum representation, data in QF-Net is best modeled as random variables rather than real numbers. Moreover, instead of using the classical batch normalization (which is key to achieve high accuracy for deep neural networks), a quantum-aware batch normalization method is proposed for QF-Net. Evaluation results show that QF-Net can achieve 97.01% accuracy in distinguishing digits 3 and 6 in the widely used MNIST dataset, which is 14.55% higher than the state-of-the-art quantum-aware implementation. A case study on a binary classification application is conducted. Running on IBM Quantum processor’s“ibmq_essex” backend, a neural network designed by QuantumFlow can achieve 82% accuracy. To the best of our knowledge,QuantumFlow is the first framework that co-designs both the machine learning model and its quantum circuit.

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“…System architecture with heterogeneous accelerators includes the main CPU and heterogeneous coprocessors such as floating-point math coprocessors, graphics and neural accelerators, FPGAs. In [2] and [3] heterogeneous quantum computer which consists of a main CPU and a quantum accelerator has been proposed. In this paper heterogeneous quantum computer which consists of a main CPU and a FPGA-based quantum accelerator (coprocessor) has been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper heterogeneous quantum computer which consists of a main CPU and a FPGA-based quantum accelerator (coprocessor) has been proposed. And these FPGA-based digital quantum coprocessors can have a homogenous or heterogeneous structure and can be used at the lowest level of full-stack quantum accelerators along with quantum chips and quantum simulators [3].…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Homogeneous and Heterogeneous Digital Quantum Coprocessors

Hlukhov¹

2020

Adv. sci. technol. eng. syst. j.

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Quantum Computer Architecture: Towards Full-Stack Quantum Accelerators

Cited by 46 publications

References 6 publications

Formulation of the Social Workers’ Problem in Quadratic Unconstrained Binary Optimization Form and Solve It on a Quantum Computer

Formulation of the Social Workers’ Problem in Quadratic Unconstrained Binary Optimization Form and Solve It on a Quantum Computer

Can Quantum Computers Learn Like Classical Computers? A Co-Design Framework of Machine Learning and Quantum Circuits

FPGA-Based Homogeneous and Heterogeneous Digital Quantum Coprocessors

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