Strategies for solving the Fermi-Hubbard model on near-term quantum computers

Cade, Chris; Mineh, Lana; Montanaro, Ashley; Stanisic, Stasja

doi:10.1103/physrevb.102.235122

Cited by 140 publications

(177 citation statements)

References 56 publications

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“…We also consider applications to the Fermi-Hubbard model, which is believed to capture the physics of some high temperature superconductors. This model is classically challenging to simulate [40,79], but is a potential candidate for near-term quantum simulation [15,16,43,59]. We have…”

Section: Applicationsmentioning

confidence: 99%

Nearly tight Trotterization of interacting electrons

Huang

Campbell

2021

Quantum

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We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved by simultaneously exploiting commutativity of the target Hamiltonian, sparsity of interactions, and prior knowledge of the initial state. We achieve this using Trotterization for a class of interacting electrons that encompasses various physical systems, including the plane-wave-basis electronic structure and the Fermi-Hubbard model. We estimate the simulation error by taking the transition amplitude of nested commutators of the Hamiltonian terms within the η-electron manifold. We develop multiple techniques for bounding the transition amplitude and expectation of general fermionic operators, which may be of independent interest. We show that it suffices to use (n5/3η2/3+n4/3η2/3)no(1) gates to simulate electronic structure in the plane-wave basis with n spin orbitals and η electrons, improving the best previous result in second quantization up to a negligible factor while outperforming the first-quantized simulation when n=η2−o(1). We also obtain an improvement for simulating the Fermi-Hubbard model. We construct concrete examples for which our bounds are almost saturated, giving a nearly tight Trotterization of interacting electrons.

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Section: Applicationsmentioning

confidence: 99%

Nearly tight Trotterization of interacting electrons

Huang

Campbell

2021

Quantum

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“…For all models, we classically simulate a QAOA algorithm on an 8-qubit device with a number of layers ranging from p = 3 to 30. The parameter optimization is performed using Simultaneous perturbation stochastic approximation (SPSA) [39,[59][60][61] (see Appendix E.1 for details of optimization). As a correlated noise model, we adopt one inspired by our previous characterization of the IBM 15-qubit device.…”

Section: Simply Set Var (H) =mentioning

confidence: 99%

“…The meaning of the parameters is in agreement with standard conventions (see for example Refs. [59,60]). probability distribution p noisy α .…”

Section: D32 Effects Of Error-mitigationmentioning

confidence: 99%

Modeling and mitigation of cross-talk effects in readout noise with applications to the Quantum Approximate Optimization Algorithm

Maciejewski

Baccari

Zimborás

et al. 2021

Quantum

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Measurement noise is one of the main sources of errors in currently available quantum devices based on superconducting qubits. At the same time, the complexity of its characterization and mitigation often exhibits exponential scaling with the system size. In this work, we introduce a correlated measurement noise model that can be efficiently described and characterized, and which admits effective noise-mitigation on the level of marginal probability distributions. Noise mitigation can be performed up to some error for which we derive upper bounds. Characterization of the model is done efficiently using Diagonal Detector Overlapping Tomography – a generalization of the recently introduced Quantum Overlapping Tomography to the problem of reconstruction of readout noise with restricted locality. The procedure allows to characterize k-local measurement cross-talk on N-qubit device using O(k2klog(N)) circuits containing random combinations of X and identity gates. We perform experiments on 15 (23) qubits using IBM's (Rigetti's) devices to test both the noise model and the error-mitigation scheme, and obtain an average reduction of errors by a factor >22 (>5.5) compared to no mitigation. Interestingly, we find that correlations in the measurement noise do not correspond to the physical layout of the device. Furthermore, we study numerically the effects of readout noise on the performance of the Quantum Approximate Optimization Algorithm (QAOA). We observe in simulations that for numerous objective Hamiltonians, including random MAX-2-SAT instances and the Sherrington-Kirkpatrick model, the noise-mitigation improves the quality of the optimization. Finally, we provide arguments why in the course of QAOA optimization the estimates of the local energy (or cost) terms often behave like uncorrelated variables, which greatly reduces sampling complexity of the energy estimation compared to the pessimistic error analysis. We also show that similar effects are expected for Haar-random quantum states and states generated by shallow-depth random circuits.

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“…The application of quantum algorithms in AI techniques will increase ML abilities enabling the development of the pharma industry, specifically in vaccine discovery. Furthermore, researchers have advanced QC research bringing quantum-classical computing one step closer by demonstrating a breakthrough in optimized quantum algorithms solving the notorious Fermi-Hubbard model using presently available QC hardware offering a pathway to comprehending and developing novel materials [8,60,61]. [64] Maximum density of quantum information in a scalable CMOS.…”

Section: Iovt Archetypementioning

confidence: 99%

“…QM methods impact addressing pharmacological challenges on the time scale demanded by vaccine-discovery research applications. The selection of the most appropriate method (Molecular Mechanics-MM, or QM, or QM-MM) [61] during vaccine discovery is paramount. It is expected that QM will become a more conspicuous tool in the stockpile of the computational medicinal chemist.…”

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confidence: 99%

Quantum Biotech and Internet of Virus Things: Towards a Theoretical Framework

Padhi

Charrua-Santos

2021

ASI

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Quantumization, the process of converting information into quantum (qubit) format, is a key enabler for propelling a new and distinct infrastructure in the pharmaceutical space. Quantum messenger RNA (QmRNA) technology, an indispensable constituent of quantum biotech (QB), is a compelling alternative to conventional vaccine methods because of its capacity for rapid development, high efficacy, and low-cost manufacturing to combat infectious diseases. Internet of Virus Things (IoVT), a biological version of Internet of Things (IoT), comprises applications to fight against pandemics and provides effective vaccine administration. The integration of QB and IoVT constitutes the QBIoVT system to advance the prospect of QmRNA vaccine discovery within a few days. This research disseminates the QBIoVT system paradigm, including architectural aspects, priority areas, challenges, applications, and QmRNA research engine design to accelerate QmRNA vaccines discovery. A comprehensive review of the literature was accomplished, and a context-centered methodology was applied to the QBIoVT paradigm forensic investigations to impel QmRNA vaccine discovery. Based on the above rumination, the principal motive for this study was to develop a novel QBIoVT theoretical framework which has not been produced through earlier theories. The proposed framework shall inspire future QBIoVT system research activities to improve pandemics detection and protection.

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Strategies for solving the Fermi-Hubbard model on near-term quantum computers

Cited by 140 publications

References 56 publications

Nearly tight Trotterization of interacting electrons

Nearly tight Trotterization of interacting electrons

Modeling and mitigation of cross-talk effects in readout noise with applications to the Quantum Approximate Optimization Algorithm

Quantum Biotech and Internet of Virus Things: Towards a Theoretical Framework

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