Importance of Diagonal Gates in Tensor Network Simulations

Lykov, Danylo; Alexeev, Yuri

doi:10.1109/isvlsi51109.2021.00088

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…This library allows quantum circuits to be simulated in parallel on CPUs and GPUs, which is a highly desirable property for sampling a large number of circuits. The library is based on the QTensor simulator [39][40][41] , a tensor networkbased quantum simulator that represents the network as an undirected graph.…”

Section: Methods Tensor Network Simulatormentioning

confidence: 99%

“…For more details on the representation, see refs. 15,40,65 . The circuit shown here is a parallel random unitary circuit with 4 qudits.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…It is critical to perform simulations of quantum circuits efficiently. To achieved this, we developed an efficient tensor network contraction algorithm is developed in the QTensor package [39][40][41] . QTensor is optimized to simulate large quantum circuits on supercomputers.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the randomness of quantum circuit ensembles up to 50 qubits

Liu

Alexeev

et al. 2022

npj Quantum Inf

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Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of random circuits, we develop numerical protocols for estimating the frame potential, the distance between a given ensemble and the exact randomness. Our tensor-network-based algorithm has polynomial complexity for shallow circuits and is high-performing using CPU and GPU parallelism. We study 1. local and parallel random circuits to verify the linear growth in complexity as stated by the Brown–Susskind conjecture, and; 2. hardware-efficient ansätze to shed light on its expressibility and the barren plateau problem in the context of variational algorithms. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.

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Section: Methods Tensor Network Simulatormentioning

confidence: 99%

“…For more details on the representation, see refs. 15,40,65 . The circuit shown here is a parallel random unitary circuit with 4 qudits.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

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Estimating the randomness of quantum circuit ensembles up to 50 qubits

Liu

Alexeev

et al. 2022

npj Quantum Inf

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“…We used the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm [56] to optimize the QAOA parameters, selecting the best solution from 10 optimization runs with random initial parameters for each graph. QAOA energy expectation values were calculated by using the tensor-network quantum simulator QTensor [38], [57], [58]. The approximation values for the standard p = 1 and p = 2 QAOA were obtained from the QAOAKit database [59].…”

Section: Qaoa+mentioning

confidence: 99%

Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer

Chalupnik¹,

Melo²,

Alexeev³

et al. 2022

Preprint

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The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the QAOA ansatz is not optimal, however. To address this problem, we propose an alternative ansatz, which we call QAOA+, that augments the traditional p = 1 QAOA ansatz with an additional multiparameter problem-independent layer. The QAOA+ ansatz allows obtaining higher approximation ratios than p = 1 QAOA while keeping the circuit depth below that of p = 2 QAOA, as benchmarked on the MaxCut problem for random regular graphs. We additionally show that the proposed QAOA+ ansatz, while using a larger number of trainable classical parameters than with the standard QAOA, in most cases outperforms alternative multiangle QAOA ansätze.

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“…The memory and computational resources of a bucket contraction scale exponentially with the associated bucket width. For more information on tensor network contraction, see [20]- [22]. If some observable Σ acts on a small subset of qubits, most of the gates in the quantum circuit Û cancel out when evaluating the expectation value.…”

Section: B Tensor Network Contractionsmentioning

confidence: 99%

Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs

Lykov

Chen

et al. 2021

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

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This work studies the porting and optimization of the tensor network simulator QTensor on GPUs, with the ultimate goal of simulating quantum circuits efficiently at scale on large GPU supercomputers. We implement NumPy, PyTorch, and CuPy backends and benchmark the codes to find the optimal allocation of tensor simulations to either a CPU or a GPU. We also present a dynamic mixed backend to achieve optimal performance. To demonstrate the performance, we simulate QAOA circuits for computing the MaxCut energy expectation. Our method achieves 176× speedup on a GPU over the NumPy baseline on a CPU for the benchmarked QAOA circuits to solve MaxCut problem on a 3-regular graph of size 30 with depth p = 4.

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Importance of Diagonal Gates in Tensor Network Simulations

Cited by 12 publications

References 12 publications

Estimating the randomness of quantum circuit ensembles up to 50 qubits

Estimating the randomness of quantum circuit ensembles up to 50 qubits

Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer

Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs

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