Quantum imaginary time evolution steered by reinforcement learning

Cao, Chenfeng; An, Zheng; Hou, Shi-Yao; Zhou, D. L.; Zeng, Bei

doi:10.1038/s42005-022-00837-y

Cited by 19 publications

(9 citation statements)

References 59 publications

(56 reference statements)

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“…Increasing the number of time-steps into which the imaginary-time evolution is broken will increase the transformation fidelity, i.e., the accuracy of the approximation.The size of the quantum circuit grows linearly with the number of time-steps, but a number of techniques have been developed to reduce the depth of these QITE circuits 47 – 50 . We also note that there exist other options for reducing the algorithmic error of QITE such as leveraging randomized compiling 51 or reinforcement learning 52 . The sub-circuit for each time-step is constructed based on a so-called domain-size, which can be set to be equal to or less than the simulated system size, and should be chosen based on the average correlation length within the simulated system.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Exploring finite temperature properties of materials with quantum computers

Powers¹,

Bassman²,

Jong³

2023

Sci Rep

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Thermal properties of nanomaterials are crucial to not only improving our fundamental understanding of condensed matter systems, but also to developing novel materials for applications spanning research and industry. Since quantum effects arise at the nano-scale, these systems are difficult to simulate on classical computers. Quantum computers can efficiently simulate quantum many-body systems, yet current quantum algorithms for calculating thermal properties of these systems incur significant computational costs in that they either prepare the full thermal state on the quantum computer, or they must sample a number of pure states from a distribution that grows with system size. Canonical thermal pure quantum (TPQ) states provide a promising path to estimating thermal properties of quantum materials as they neither require preparation of the full thermal state nor require a growing number of samples with system size. Here, we present an algorithm for preparing canonical TPQ states on quantum computers. We compare three different circuit implementations for the algorithm and demonstrate their capabilities in estimating thermal properties of quantum materials. Due to its increasing accuracy with system size and flexibility in implementation, we anticipate that this method will enable finite temperature explorations of relevant quantum materials on near-term quantum computers.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Exploring finite temperature properties of materials with quantum computers

Powers¹,

Bassman²,

Jong³

2023

Sci Rep

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“…As the expectation value of the Hamiltonian approaches the ground state energy exponentially fast along the ITE path, the total evolution time required for ITE may not be large. It is worth remarking that the idea of randomization in QITE was initially mentioned and tested in ref , whereas their scheme randomly shuffles the order of Hamiltonian terms in each Trotter step and hence the circuit depth still depends polynomially on L .…”

Section: Time-dependent Drifting Algorithmmentioning

confidence: 99%

Efficient Quantum Imaginary Time Evolution by Drifting Real-Time Evolution: An Approach with Low Gate and Measurement Complexity

Huang¹,

Shao

Ren³

et al. 2023

J. Chem. Theory Comput.

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Quantum imaginary time evolution (QITE) is one of the promising candidates for finding the eigenvalues and eigenstates of a Hamiltonian on a quantum computer. However, the original proposal suffers from large circuit depth and measurements due to the size of the Pauli operator pool and Trotterization. To alleviate the requirement for deep circuits, we propose a time-dependent drifting scheme inspired by the qDRIFT algorithm [Phys. Rev. Lett.2019123070503]. We show that this drifting scheme removes the depth dependency on the size of the operator pool and converges inversely with respect to the number of steps. We further propose a deterministic algorithm that selects the dominant Pauli term to reduce the fluctuation for the ground state preparation. We also introduce an efficient measurement reduction scheme across Trotter steps that removes its cost dependence on the number of iterations. We analyze the main source of error for our scheme both theoretically and numerically. We numerically test the validity of depth reduction, convergence performance of our algorithms, and the faithfulness of the approximation for our measurement reduction scheme on several benchmark molecules. In particular, the results on the LiH molecule give circuit depths comparable to that of the advanced adaptive variational quantum eigensolver (VQE) methods while requiring much fewer measurements.

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“…Different from other platforms [41], liquid-state NMR quantum simulators realize entangling operations on the so-called pseudo-pure states (PPSs) and benefit from its computer-aided high-fidelity pulse-engineering technology and controlling in full range of the system dynamics. Although being limited on qubit numbers and sampling cost, which are similar with some NISQ (noisy intermediate-scale quantum) tasks [3], NMR quantum simulators are able to simulate quantum systems of small-to-medium sizes with complex or time-dependent Hamiltonian and test new protocols, such as open-system dynamics [17], quantum phase transition [42], gate characterization [43], measuring correlation functions [44], quantum imaginary evolution [45], heat conduction [46] and quantum energy teleportation [47].…”

Section: Introductionmentioning

confidence: 99%

Experimental simulation of quantum superchannels

Li,

Wang,

Wei

et al. 2024

New J. Phys.

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Simulating quantum physical processes has been one of the major motivations for quantum information science. Quantum channels, which are completely positive and trace preserving processes, are the standard mathematical language to describe quantum evolution, while in recent years quantum superchannels have emerged as the substantial extension. Superchannels capture effects of quantum memory and non-Markovianality more precisely, and have found broad applications in universal models, algorithm, metrology, discrimination tasks, as examples. Here, we report an experimental simulation of qubit superchannels in a nuclear magnetic resonance (NMR) system with high accuracy, based on a recently developed quantum algorithm for superchannel simulation. Our algorithm applies to arbitrary target superchannels, and our experiment shows the high quality of NMR simulators for near-term usage. Our approach can also be adapted to other experimental systems and demonstrates prospects for more applications of superchannels.

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Quantum imaginary time evolution steered by reinforcement learning

Cited by 19 publications

References 59 publications

Exploring finite temperature properties of materials with quantum computers

Exploring finite temperature properties of materials with quantum computers

Efficient Quantum Imaginary Time Evolution by Drifting Real-Time Evolution: An Approach with Low Gate and Measurement Complexity

Experimental simulation of quantum superchannels

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