8 research outputs found
Qutrit Circuits and Algebraic Relations: A Pathway to Efficient Spin-1 Hamiltonian Simulation
Quantum information processing has witnessed significant advancements through
the application of qubit-based techniques within universal gate sets. Recently,
exploration beyond the qubit paradigm to -dimensional quantum units or
qudits has opened new avenues for improving computational efficiency. This
paper delves into the qudit-based approach, particularly addressing the
challenges presented in the high-fidelity implementation of qudit-based
circuits due to increased complexity. As an innovative approach towards
enhancing qudit circuit fidelity, we explore algebraic relations, such as the
Yang-Baxter-like turnover equation, that may enable circuit compression and
optimization. The paper introduces the turnover relation for the three-qutrit
time propagator and its potential use in reducing circuit depth. We further
investigate whether this relation can be generalized for higher-dimensional
quantum circuits, including a focused study on the one-dimensional spin-1
Heisenberg model. Our work outlines both rigorous and numerically efficient
approaches to potentially achieve this generalization, providing a foundation
for further explorations in the field of qudit-based quantum computing
Neural network accelerator for quantum control
Efficient quantum control is necessary for practical quantum computing
implementations with current technologies. Conventional algorithms for
determining optimal control parameters are computationally expensive, largely
excluding them from use outside of the simulation. Existing hardware solutions
structured as lookup tables are imprecise and costly. By designing a machine
learning model to approximate the results of traditional tools, a more
efficient method can be produced. Such a model can then be synthesized into a
hardware accelerator for use in quantum systems. In this study, we demonstrate
a machine learning algorithm for predicting optimal pulse parameters. This
algorithm is lightweight enough to fit on a low-resource FPGA and perform
inference with a latency of 175 ns and pipeline interval of 5 ns with 0.99
gate fidelity. In the long term, such an accelerator could be used near quantum
computing hardware where traditional computers cannot operate, enabling quantum
control at a reasonable cost at low latencies without incurring large data
bandwidths outside of the cryogenic environment.Comment: 7 pages, 10 figure
Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model
Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest. While general quantum simulation algorithms likely require error-corrected qubits, there may be applications of scientific interest prior to the practical implementation of quantum error correction. The variational quantum eigensolver (VQE) is a promising approach to find energy eigenvalues on noisy quantum computers. Lattice models are of broad interest for use on near-term quantum hardware due to the sparsity of the number of Hamiltonian terms and the possibility of matching the lattice geometry to the hardware geometry. Here, we consider the Kitaev spin model on a hardware-native square-octagon qubit connectivity map, and examine the possibility of efficiently probing its rich phase diagram with VQE approaches. By benchmarking different choices of variational ansatz states and classical optimizers, we illustrate the advantage of a mixed optimization approach using the Hamiltonian variational ansatz (HVA). We further demonstrate the implementation of an HVA circuit on Rigetti's Aspen-9 chip with error mitigation.This is a pre-print of the article Li, Andy CY, M. Sohaib Alam, Thomas Iadecola, Ammar Jahin, Doga Murat Kurkcuoglu, Richard Li, Peter P. Orth, A. Barış Özgüler, Gabriel N. Perdue, and Norm M. Tubman. "Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model." arXiv preprint arXiv:2108.13375 (2021).
DOI: 10.48550/arXiv.2108.13375.
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