Simulating noisy variational quantum eigensolver with local noise models

Zeng, Jinfeng; Wu, Zipeng; Cao, Chenfeng; Zhang, Chao; Hou, Shi-Yao; Xu, Pengxiang; Zeng, Bei

doi:10.1002/que2.77

Cited by 16 publications

(13 citation statements)

References 50 publications

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“…Ravi et al [22] proposed a VQA error-mitigation approach that tunes single qubit gate scheduling and dynamical decoupling sequences in a variational approach. Other works have also explored the effects of noise on VQAs and hardware-efficient PQCs [19], [85], [86]. The work by [86] determines optimal PQC depth at different noise levels and investigates the circuit resiliency to noise with the inclusion of redundant parameterized gates.…”

Section: Related Workmentioning

confidence: 99%

Pulse-Level Optimization of Parameterized Quantum Circuits for Variational Quantum Algorithms

Ibrahim¹,

Mohammadbagherpoor²,

Rios³

et al. 2022

Preprint

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Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices. Therefore, investigating their design has become key in quantum computing research. Previous works have shown that choosing an effective parameterized quantum circuit (PQC) or ansatz for VQAs is crucial to their overall performance, especially on near-term devices. In this paper, we utilize pulse-level access to quantum machines and our understanding of their two-qubit interactions to optimize the design of two-qubit entanglers in a manner suitable for VQAs. Our analysis results show that pulse-optimized ansatze reduce state preparation times by more than half, maintain expressibility relative to standard PQCs, and are more trainable through local cost function analysis. Our algorithm performance results show that in three cases, our PQC configuration outperforms the base implementation. Our algorithm performance results, executed on IBM Quantum hardware, demonstrate that our pulse-optimized PQC configurations are more capable of solving MaxCut and Chemistry problems compared to a standard configuration.

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Section: Related Workmentioning

confidence: 99%

Pulse-Level Optimization of Parameterized Quantum Circuits for Variational Quantum Algorithms

Ibrahim¹,

Mohammadbagherpoor²,

Rios³

et al. 2022

Preprint

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“…The VQE approach has been shown to be flexible in circuit depth and insensitive to the presence of noises (Zeng et al 2021). Therefore, while there is still a lack of quantum error correction and fault-tolerant quantum computation in the NISQ era, quantum machine learning methods driven by variational quantum circuits can circumvent the complex quantum flaws in the current quantum devices.…”

Section: Variational Quantum Eigensolvermentioning

confidence: 99%

QNet: A Quantum-native Sequence Encoder Architecture

Day¹,

Chen²,

Sun³

2022

Preprint

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This work investigates how current quantum computers can improve the performance of natural language processing tasks. To achieve this goal, we proposed QNet, a novel sequence encoder model entirely inferences on the quantum computer using a minimum number of qubits. QNet is inspired by Transformer, the state-of-the-art neural network model based on the attention mechanism to relate the tokens. While the attention mechanism requires time complexity of O(n 2 • d) to perform matrix multiplication operations, QNet has merely O(n + d) quantum circuit depth, where n and d represent the length of the sequence and the embedding size, respectively. To employ QNet on the NISQ devices, ResQNet, a quantum-classical hybrid model composed of several QNet blocks linked by residual connections, is introduced. We evaluate ResQNet on various natural language processing tasks, including text classification, rating score prediction, and named entity recognition. ResQNet exhibits a 6% to 818% performance gain on all these tasks over classical state-of-the-art models using the exact embedding dimensions. In summary, this work demonstrates the advantage of quantum computing in natural language processing tasks.

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“…For example, a recent study has developed a systematic set of parameters that are argued to require layers of QAOA to reach performance comparable to the conventional Goemans–Williamson algorithm on MaxCut 36 , while another study has argued that hundreds of qubits or more are needed to compete with conventional solvers in time-to-solution 38 . Noise grows rapidly with circuit depth and affects the fidelity of the prepared quantum state so the performance that can be achieved from near-term quantum computers at these depths is questionable 39 – 49 .…”

Section: Introductionmentioning

confidence: 99%

Multi-angle quantum approximate optimization algorithm

Herrman

Lotshaw

Ostrowski

et al. 2022

Sci Rep

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The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good parameters can be found in polynomial time for a test dataset we consider. This new ansatz gives a 33% increase in the approximation ratio for an infinite family of MaxCut instances over QAOA. The optimal performance is lower bounded by the conventional ansatz, and we present empirical results for graphs on eight vertices that one layer of the multi-angle anstaz is comparable to three layers of the traditional ansatz on MaxCut problems. Similarly, multi-angle QAOA yields a higher approximation ratio than QAOA at the same depth on a collection of MaxCut instances on fifty and one-hundred vertex graphs. Many of the optimized parameters are found to be zero, so their associated gates can be removed from the circuit, further decreasing the circuit depth. These results indicate that multi-angle QAOA requires shallower circuits to solve problems than QAOA, making it more viable for near-term intermediate-scale quantum devices.

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Simulating noisy variational quantum eigensolver with local noise models

Cited by 16 publications

References 50 publications

Pulse-Level Optimization of Parameterized Quantum Circuits for Variational Quantum Algorithms

Pulse-Level Optimization of Parameterized Quantum Circuits for Variational Quantum Algorithms

QNet: A Quantum-native Sequence Encoder Architecture

Multi-angle quantum approximate optimization algorithm

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