Quantum AI simulator using a hybrid CPU-FPGA approach

• URL: http://arxiv.org/abs/2206.09593v3
• Date: Mon, 11 Sep 2023 04:24:13 GMT
• Title: Quantum AI simulator using a hybrid CPU-FPGA approach
• Authors: Teppei Suzuki, Tsubasa Miyazaki, Toshiki Inaritai, Takahiro Otsuka
• Abstract summary: We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features.
• Score: 9.736685719039599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noisy quantum computers have, thereby limiting the number of features encoded for quantum kernels. Hence, there is a need for an efficient, application-specific simulator for quantum computing by using classical technology. Here we focus on quantum kernels empirically designed for image classification and demonstrate a field programmable gate arrays (FPGA) implementation. We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features, up to 780-dimensional features. We apply our quantum kernel to classification tasks using Fashion-MNIST dataset and show that our quantum kernel is comparable to Gaussian kernels with the optimized hyperparameter.

Related papers

• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers [37.69303106863453]
  We propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers. Our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average.
  arXiv  Detail & Related papers  (2024-06-17T14:10:10Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• Continuous-variable quantum kernel method on a programmable photonic quantum processor [0.0]
  We experimentally prove that the CV quantum kernel method successfully classifies several datasets robustly even under the experimental imperfections. This demonstration sheds light on the utility of CV quantum systems for QML and should stimulate further study in other CV QML algorithms.
  arXiv  Detail & Related papers  (2024-05-02T08:33:31Z)
• Power Characterization of Noisy Quantum Kernels [52.47151453259434]
  We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small. We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
  arXiv  Detail & Related papers  (2024-01-31T01:02:16Z)
• Efficient Quantum Modular Arithmetics for the ISQ Era [0.0]
  This study presents an array of quantum circuits, each precision-engineered for modular arithmetic functions. We provide a theoretical framework and practical implementations in the PennyLane quantum software.
  arXiv  Detail & Related papers  (2023-11-14T21:34:39Z)
• Several fitness functions and entanglement gates in quantum kernel generation [3.6953740776904924]
  Entanglement, a fundamental concept in quantum mechanics, assumes a central role in quantum computing. We investigate the optimal number of entanglement gates in the quantum kernel feature maps by a multi-objective genetic algorithm. Our findings offer valuable guidance for enhancing the efficiency and accuracy of quantum machine learning algorithms.
  arXiv  Detail & Related papers  (2023-08-22T18:35:51Z)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• Optimal quantum kernels for small data classification [0.0]
  We show an algorithm for constructing quantum kernels for support vector machines that adapts quantum gate sequences to data. The performance of the resulting quantum models for classification problems with a small number of training points significantly exceeds that of optimized classical models.
  arXiv  Detail & Related papers  (2022-03-25T18:26:44Z)
• Distributed Quantum Computing with QMPI [11.71212583708166]
  We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ.
  arXiv  Detail & Related papers  (2021-05-03T18:30:43Z)
• Programming a quantum computer with quantum instructions [39.994876450026865]
  We use a density matrixiation protocol to execute quantum instructions on quantum data. A fixed sequence of classically-defined gates performs an operation that uniquely depends on an auxiliary quantum instruction state. The utilization of quantum instructions obviates the need for costly tomographic state reconstruction and recompilation.
  arXiv  Detail & Related papers  (2020-01-23T22:43:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.