Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction

Vincent, Trevor; O'Riordan, Lee J.; Andrenkov, Mikhail; Brown, Jack; Killoran, Nathan; Qi, Haoyu; Dhand, Ish

doi:10.48550/arxiv.2107.09793

Cited by 6 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 we demonstrate sufficient convergence in the number of qubits L to extract large-L decay rates. We fix the frequency of the drive to ω = 8, the largest frequency we consider, and plot E(t)/E(0) over time for system sizes L ∈ [22,34]. The convergence of these curves at the larger values of L ensures that the thermalization rates shown in Fig.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…Normalized energy versus time. Light to dark (right to left) curves correspond to L ∈ [22,34] in steps of 2. All data is for ω = 8, the largest frequency we consider in Fig.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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

Section: Simulations and Resultsmentioning

confidence: 99%

“…Normalized energy versus time. Light to dark (right to left) curves correspond to L ∈ [22,34] in steps of 2. All data is for ω = 8, the largest frequency we consider in Fig.…”

Section: Simulations and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

show abstract

“…On the other hand, the de-facto standard for computing individual amplitudes of the final state vector using decision diagrams is to simulate the full state vector and extract the corresponding amplitude. 3 A similar effort has been conducted for the tensor network domain in [31]. After the translation, the resulting tensor network can be fed into any available tensor network contraction tool in order to determine a suitable contraction path.…”

Section: B Utilizing Research On Tensor Network Contractionmentioning

confidence: 99%

Simulation Paths for Quantum Circuit Simulation with Decision Diagrams

Burgholzer,

Ploier,

Wille

2022

Preprint

View full text Add to dashboard Cite

Simulating quantum circuits on classical computers is a notoriously hard, yet increasingly important task for the development and testing of quantum algorithms. In order to alleviate this inherent complexity, efficient data structures and methods such as tensor networks and decision diagrams have been proposed. However, their efficiency heavily depends on the order in which the individual computations are performed. For tensor networks the order is defined by so-called contraction plans and a plethora of methods has been developed to determine suitable plans. On the other hand, simulation based on decision diagrams is mostly conducted in a straight-forward, i.e., sequential, fashion thus far.In this work, we study the importance of the path that is chosen when simulating quantum circuits using decision diagrams. We propose an open-source framework that not only allows to investigate dedicated simulation paths, but also to re-use existing findings, e.g., obtained from determining contraction plans for tensor networks. Experimental evaluations show that translating strategies from the domain of tensor networks can already yield speedups of up to several orders of magnitude compared to the state of the art. Furthermore, we show, both conceptually and experimentally, that choosing the right simulation path can result in runtimes practically independent of the number of qubitsas opposed to exponential runtimes using the state-of-the-art approach. The proposed framework is publicly available at github.com/cda-tum/ddsim.

show abstract

“…Available distributed tensor-network simulators are Ex-aTENSOR [17] and Jet [18]. An interesting library for performing distributed tensor contractions is Cyclops [3].…”

Section: Related Workmentioning

confidence: 99%

RosneT: A Block Tensor Algebra Library for Out-of-Core Quantum Computing Simulation

Sánchez-Ramírez

Conejero

Lordan

et al. 2021

2021 IEEE/ACM Second International Workshop on Quantum Computing Software (QCS)

View full text Add to dashboard Cite

Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction

Cited by 6 publications

References 27 publications

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

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

Simulation Paths for Quantum Circuit Simulation with Decision Diagrams

RosneT: A Block Tensor Algebra Library for Out-of-Core Quantum Computing Simulation

Contact Info

Product

Resources

About