Tensor Network Quantum Simulator With Step-Dependent Parallelization

Lykov, Danylo; Schutski, Roman; Galda, Alexey; Vinokur, V. M.; Yurii, Alexeev,

doi:10.48550/arxiv.2012.02430

Cited by 20 publications

(30 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the QAOA parameters are optimized using measurements from a quantum computer, this optimization will also be greatly inhibited. Parameter optimization has been addressed in some instances using theoretical approaches [9,19,20,[37][38][39][40][41][42][43][44], though for generic instances it is unclear if such approaches can be applied. However, even with a good set of parameters the circuit must still be run to obtain the final bitstring solution to the problem, and in our model this requires a number of measurements that quickly becomes prohibitive at scales relevant for quantum advantage.…”

Section: Discussionmentioning

confidence: 99%

“…Classical algorithms have also been developed that outperform QAOA at low p [35,36], further suggesting large p may be necessary to compete with conventional methods. To optimize parameters at large n and p, a variety of computational [37,38] and theoretical [39][40][41][42][43][44] approaches have been developed and in some cases the theoretical performance has been characterized. With parameter setting strategies at hand, what remains to be seen is how the QAOA will perform in experimental implementations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Scaling Quantum Approximate Optimization on Near-term Hardware

Lotshaw,

Nguyen,

Santana

et al. 2022

Preprint

View full text Add to dashboard Cite

The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems. However, the viability of the QAOA depends on how its performance and resource requirements scale with problem size and complexity for realistic hardware implementations. Here, we quantify scaling of the expected resource requirements by synthesizing optimized circuits for hardware architectures with varying levels of connectivity. Assuming noisy gate operations, we estimate the number of measurements needed to sample the output of the idealized QAOA circuit with high probability. We show the number of measurements, and hence total time to solution, grows exponentially in problem size and problem graph degree as well as depth of the QAOA ansatz, gate infidelities, and inverse hardware graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scaling Quantum Approximate Optimization on Near-term Hardware

Lotshaw,

Nguyen,

Santana

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The time complexity of the contraction is heavily sensitive to the order of summations [22], with the determination of the optimal path being an NP-hard problem [23]. Despite these difficulties, several approximate and exact methods have been developed to quickly determine quasi-optimal contractions paths and contract the tensor network, with the most common methods being tree decomposition [24,25] and graph partitioning [3,5].…”

Section: Background a Tensor Networkmentioning

confidence: 99%

“…Another notable tensor-network simulator is that of Lykov et al [25], who computed 210-qubit Quantum Approximate Optimization Ansatz (QAOA) circuits with 1785 gates on 1024 nodes of the Cray XC 40 supercomputer Theta. Lykov et al used a greedy path optimizer, which is known to perform slightly worse than the hypergraph partitioners that AQCDP and CoTenGra use [3,5].…”

Section: F Overview Of Previous Tensor-network Simulators For Quantum...mentioning

confidence: 99%

“…This type of method would be very suitable for dynamic task graphs and could be incorporated into the setup of Jet very naturally. Unfortunately, [25] did not benchmark on Sycamore-53 so we do not know the extent to which this novel slicing method would perform against interleaving slicing with subtree-reconfiguration as was first done in ACQDP [5] and now supported in the current version of CoTenGra [37]. Lastly, the tensor-network simulator Ex-aTN [39] has been used to perform large quantum circuit simulations on Summit.…”

Section: F Overview Of Previous Tensor-network Simulators For Quantum...mentioning

confidence: 99%

See 1 more Smart Citation

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

Vincent,

O'Riordan,

Andrenkov

et al. 2021

Preprint

View full text Add to dashboard Cite

We introduce a new open-source software library Jet, which uses task-based parallelism to obtain speed-ups in classical tensor-network simulations of quantum circuits. These speed-ups result from i) the increased parallelism introduced by mapping the tensor-network simulation to a task-based framework, ii) a novel method of reusing shared work between tensor-network contraction tasks, and iii) the concurrent contraction of tensor networks on all available hardware. We demonstrate the advantages of our method by benchmarking our code on several Sycamore-53 and Gaussian boson sampling (GBS) supremacy circuits against other simulators. We also provide and compare theoretical performance estimates for tensor-network simulations of Sycamore-53 and GBS supremacy circuits for the first time.

show abstract

Importance of Diagonal Gates in Tensor Network Simulations

Lykov

Alexeev

2021

2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

Self Cite

View full text Add to dashboard Cite

In this work we present two techniques that tremendously increase the performance of tensor-network based quantum circuit simulations. The techniques are implemented in the QTensor package and benchmarked using Quantum Approximate Optimization Algorithm (QAOA) circuits. The techniques allowed us to increase the depth and size of QAOA circuits that can be simulated. In particular, we increased the QAOA depth from 2 to 5 and the size of a QAOA circuit from 180 to 244 qubits. Moreover, we increased the speed of simulations by up to 10 million times. Our work provides important insights into how various techniques can dramatically speed up the simulations of circuits.

show abstract

Tensor Network Quantum Simulator With Step-Dependent Parallelization

Cited by 20 publications

References 0 publications

Scaling Quantum Approximate Optimization on Near-term Hardware

Scaling Quantum Approximate Optimization on Near-term Hardware

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

Importance of Diagonal Gates in Tensor Network Simulations

Contact Info

Product

Resources

About