Quantifying the Efficiency of State Preparation via Quantum Variational Eigensolvers

Matos, Gabriel; Johri, Sonika; Papić, Zlatko

doi:10.1103/prxquantum.2.010309

Cited by 24 publications

(23 citation statements)

References 43 publications

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“…preparing chemical ground states [64] as well as ground state preparation for one-dimensional quantum spin models in theory [65,66] and experiment [67].…”

Section: Quantum Approximate Optimization Algorithmmentioning

confidence: 99%

Simulations of Frustrated Ising Hamiltonians with Quantum Approximate Optimization

Lotshaw,

Xu,

Khalid

et al. 2022

Preprint

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Novel magnetic materials are important for future technological advances. Theoretical and numerical calculations of ground state properties are essential in understanding these materials, however, computational complexity limits conventional methods for studying these states. Here we investigate an alternative approach to preparing materials ground states using the quantum approximate optimization algorithm (QAOA) on near-term quantum computers. We study Ising spin models on unit cells of square, Shastry-Sutherland, and triangular lattices, with varying field amplitudes and couplings in the material Hamiltonian. We find relationships between the theoretical QAOA success probability and the structure of the ground state, indicating that only a modest number of measurements ( < ∼ 100) are needed to find the ground state of our nine-spin Hamiltonians, even for parameters leading to frustrated magnetism. We further demonstrate the approach in calculations on a trapped-ion quantum computer and succeed in recovering each ground state of the Shastry-Sutherland unit cell with probabilities close to ideal theoretical values. The results demonstrate the viability of QAOA for materials ground state preparation in the frustrated Ising limit, giving important first steps towards larger sizes and more complex Hamiltonians where quantum computational advantage may prove essential in developing a systematic understanding of novel materials.

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“…preparing chemical ground states [64] as well as ground state preparation for one-dimensional quantum spin models in theory [65,66] and experiment [67].…”

Section: Quantum Approximate Optimization Algorithmmentioning

confidence: 99%

Simulations of Frustrated Ising Hamiltonians with Quantum Approximate Optimization

Lotshaw,

Xu,

Khalid

et al. 2022

Preprint

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“…Similar to the quantum alternating operator ansatz (QAOA), the performance depends on its number of layers [37]. The performance is also related to the interaction distance to the target state [38]. The quantum circuit for the 4-qubit HVA is shown in Fig.…”

Section: B Ansatzmentioning

confidence: 99%

Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations

Liu¹,

Chen²,

Sun³

et al. 2021

Preprint

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“…For example, quantum computing often involves high-fidelity state manipulation as a necessary component of most quantum algorithms [1,2]; In quantum simulation, the underlying AMO platforms require to prepare the system in a desired state before its properties can be measured and studied [3][4][5]; And quantum metrology relies on the controlled engineering of (critical) states to maximize the sensitivity to physical parameters [6,7]. Many-body control can also be considered in its own right, as a numerical tool which offers insights into concepts such as quantum phases and phase transitions [8]. Moreover, it can reveal novel theoretical phenomena such as phase transitions in the control landscape [9], and bears a direct relation to our understanding of quantum complexity [10].…”

Section: Introductionmentioning

confidence: 99%

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Metz¹,

Bukov²

2022

Preprint

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Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols. Here, we present a novel framework for efficiently controlling quantum many-body systems based on reinforcement learning (RL). We tackle the quantum control problem by leveraging matrix product states (i) for representing the many-body state and, (ii) as part of the trainable machine learning architecture for our RL agent. The framework is applied to prepare ground states of the quantum Ising chain, including critical states. It allows us to control systems far larger than neural-network-only architectures permit, while retaining the advantages of deep learning algorithms, such as generalizability and trainable robustness to noise. In particular, we demonstrate that RL agents are capable of finding universal controls, of learning how to optimally steer previously unseen many-body states, and of adapting control protocols on-the-fly when the quantum dynamics is subject to stochastic perturbations.

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Quantifying the Efficiency of State Preparation via Quantum Variational Eigensolvers

Cited by 24 publications

References 43 publications

Simulations of Frustrated Ising Hamiltonians with Quantum Approximate Optimization

Simulations of Frustrated Ising Hamiltonians with Quantum Approximate Optimization

Mitigating Barren Plateaus with Transfer-learning-inspired Parameter Initializations

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

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