Qulacs: a fast and versatile quantum circuit simulator for research purpose

Suzuki, Yôiti; Kawase, Yoshiaki; Masumura, Yuya; Hiraga, Yuria; Nakadai, Masahiro; Chen, Jiabao; Nakanishi, Kazuhito; Mitarai, Kosuke; Imai, Ryōsuke; Tamiya, Shiro; Yamamoto, Takahiro; Yan, Tennin; Kawakubo, Toru; Nakagawa, Yasuyoshi; Ibe, Yohei; Zhang, Youyuan; Yamashita, Hirotsugu; Yoshimura, Hikaru; Hayashi, Atsushi; Fujii, Keisuke

doi:10.22331/q-2021-10-06-559

Cited by 188 publications

(107 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the wavefunction-based approach is faster than the matrix-multiplication one for small numbers of qubits (N < 8), the opposite behavior is observed for larger numbers of qubits. This observation reflects the basic properties of these two approaches as discussed above and see also [72]. However, there are some available techniques to improve the performance of the wavefunction approach for larger numbers of qubits, such as, SIMD (single-instruction, multiple data) optimization and multi-threading [72].…”

Section: Assessing the Quantum Virtual Machine Performancementioning

confidence: 54%

“…This observation reflects the basic properties of these two approaches as discussed above and see also [72]. However, there are some available techniques to improve the performance of the wavefunction approach for larger numbers of qubits, such as, SIMD (single-instruction, multiple data) optimization and multi-threading [72]. We further summarize a comparison between Qsun and other simulators in terms of practical quantum algorithms in table 2.…”

Section: Assessing the Quantum Virtual Machine Performancementioning

confidence: 56%

“…Unlike widely-used approaches based on matrix multiplication [4,56,[60][61][62][63][64][65], our platform is developed using the class of 'wavefunction' approach [66][67][68][69][70][71][72], in which a quantum register is represented by its wavefunction. The operation of quantum gates is simulated by updating the wavefunction's amplitude, and output results are obtained by measuring wavefunction's probabilities.…”

Section: Simulating Quantum Computers Using Wavefunction Basismentioning

confidence: 99%

“…Besides, QVMs are necessary for modeling various noisy channels to characterize the noises and the efficiency of quantum error correction. One can classify QVMs into two types according to the way to build them: (a) the matrix multiplication approach [4,56,[60][61][62][63][64][65], and (b) the wavefunction approach [66][67][68][69][70][71][72]. While the former performs matrix multiplication for all qubits in quantum circuits, the latter represents quantum circuits by corresponding wavefunctions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Qsun: an open-source platform towards practical quantum machine learning applications

Quoc

Tran

et al. 2022

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms have been proposed and tested on quantum virtual machines to surpass the limitations of quantum hardware. Our goal is to exploit further the variational quantum algorithms towards practical applications of quantum machine learning using state-of-the-art quantum computers. In this paper, we first introduce a quantum virtual machine named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting variational quantum algorithms. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of quantum machine learning: quantum linear regression and quantum neural network.

show abstract

Section: Assessing the Quantum Virtual Machine Performancementioning

confidence: 54%

Section: Assessing the Quantum Virtual Machine Performancementioning

confidence: 56%

Section: Simulating Quantum Computers Using Wavefunction Basismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Qsun: an open-source platform towards practical quantum machine learning applications

Quoc

Tran

et al. 2022

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…1 These ingredients naturally allow for the accelerated application of local operators to wavefunctions that are distributed over many TPU cores, and other useful operations. Similar efforts have been made to leverage graphics processing units (GPUs) to accelerate the classical simulation of quantum circuits [33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Simulation of quantum many-body dynamics with Tensor Processing Units: Floquet prethermalization

Morningstar,

Hauru,

Beall

et al. 2021

Preprint

View full text Add to dashboard Cite

Tensor Processing Units (TPUs) are specialized hardware accelerators developed by Google to support large-scale machine-learning tasks, but they can also be leveraged to accelerate and scale other linear-algebra-intensive computations. In this paper we demonstrate the usage of TPUs for massively parallel, classical simulations of quantum many-body dynamics on very long timescales. We apply our methods to study the phenomenon of Floquet prethermalization, i.e., exponentially slow heating in quantum spin chains subject to high-frequency periodic driving. We simulate the dynamics of L = 34 qubits for over 10 5 Floquet periods, corresponding to 4 × 10 6 nearest-neighbor two-qubit gates. This is achieved by distributing the computation over 128 TPU cores. The circuits simulated have no additional symmetries and represent a pure-state evolution in the full 2 Ldimensional Hilbert space. We study the computational cost of the simulations, as a function of both the number of qubits and the number of TPU cores used, up to our maximum capacity of L = 40 qubits which requires 2048 TPU cores. For a 30-qubit benchmark simulation on 128 TPU cores, we find a 230× speedup in wall-clock runtime when compared to a reference multi-core CPU simulation that we take to be representative of the current standard in quantum many-body dynamics research. We also study the accumulation of errors as a function of circuit depth. Our work demonstrates that TPUs can offer significant advantages for state-of-the-art simulations of quantum many-body dynamics.

show abstract

Range‐separated density functional theory using multiresolution analysis and quantum computing

Poirier,

Kottmann,

Aspuru‐Guzik

et al. 2024

J Comput Chem

View full text Add to dashboard Cite

Quantum computers are expected to outperform classical computers for specific problems in quantum chemistry. Such calculations remain expensive, but costs can be lowered through the partition of the molecular system. In the present study, partition was achieved with range‐separated density functional theory (RS‐DFT). The use of RS‐DFT reduces both the basis set size and the active space size dependence of the ground state energy in comparison with the use of wave function theory (WFT) alone. The utilization of pair natural orbitals (PNOs) in place of canonical molecular orbitals (MOs) results in more compact qubit Hamiltonians. To test this strategy, a basis‐set independent framework, known as multiresolution analysis (MRA), was employed to generate PNOs. Tests were conducted with the variational quantum eigensolver for a number of molecules. The results show that the proposed approach reduces the number of qubits needed to reach a target energy accuracy.

show abstract

Qulacs: a fast and versatile quantum circuit simulator for research purpose

Cited by 188 publications

References 43 publications

Qsun: an open-source platform towards practical quantum machine learning applications

Qsun: an open-source platform towards practical quantum machine learning applications

Simulation of quantum many-body dynamics with Tensor Processing Units: Floquet prethermalization

Range‐separated density functional theory using multiresolution analysis and quantum computing

Contact Info

Product

Resources

About