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Seetharam K, Biswas D, Noel C, Risinger A, Zhu D, Katz O, Chattopadhyay S, Cetina M, Monroe C, Demler E, Sels D. Digital quantum simulation of NMR experiments. SCIENCE ADVANCES 2023; 9:eadh2594. [PMID: 37976365 PMCID: PMC10656062 DOI: 10.1126/sciadv.adh2594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/18/2023] [Indexed: 11/19/2023]
Abstract
Simulations of nuclear magnetic resonance (NMR) experiments can be an important tool for extracting information about molecular structure and optimizing experimental protocols but are often intractable on classical computers for large molecules such as proteins and for protocols such as zero-field NMR. We demonstrate the first quantum simulation of an NMR spectrum, computing the zero-field spectrum of the methyl group of acetonitrile using four qubits of a trapped-ion quantum computer. We reduce the sampling cost of the quantum simulation by an order of magnitude using compressed sensing techniques. We show how the intrinsic decoherence of NMR systems may enable the zero-field simulation of classically hard molecules on relatively near-term quantum hardware and discuss how the experimentally demonstrated quantum algorithm can be used to efficiently simulate scientifically and technologically relevant solid-state NMR experiments on more mature devices. Our work opens a practical application for quantum computation.
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Affiliation(s)
- Kushal Seetharam
- Department of Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Debopriyo Biswas
- Department of Electrical and Computer Engineering, Department of Physics, Duke Quantum Center, Duke University, Durham, NC 27708, USA
- Joint Quantum Institute, Department of Physics, University of Maryland, College Park, MD 20742, USA
| | - Crystal Noel
- Department of Electrical and Computer Engineering, Department of Physics, Duke Quantum Center, Duke University, Durham, NC 27708, USA
- Joint Quantum Institute, Department of Physics, University of Maryland, College Park, MD 20742, USA
| | - Andrew Risinger
- Joint Quantum Institute, Department of Physics, University of Maryland, College Park, MD 20742, USA
| | - Daiwei Zhu
- Joint Quantum Institute, Department of Physics, University of Maryland, College Park, MD 20742, USA
| | - Or Katz
- Department of Electrical and Computer Engineering, Department of Physics, Duke Quantum Center, Duke University, Durham, NC 27708, USA
| | | | - Marko Cetina
- Joint Quantum Institute, Department of Physics, University of Maryland, College Park, MD 20742, USA
- Department of Physics, Duke Quantum Center, Duke University, Durham, NC 27708, USA
| | - Christopher Monroe
- Department of Electrical and Computer Engineering, Department of Physics, Duke Quantum Center, Duke University, Durham, NC 27708, USA
- Joint Quantum Institute, Department of Physics, University of Maryland, College Park, MD 20742, USA
- IonQ Inc., College Park, MD 20740, USA
| | - Eugene Demler
- Institute for Theoretical Physics, ETH Zürich, 8093 Zürich, Switzerland
| | - Dries Sels
- Department of Physics, New York University, New York, NY 10003, USA
- Center for Computational Quantum Physics, Flatiron Institute, New York, NY 10010, USA
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Ueda H, Katakami S, Okada M, Yoshida S, Nakai Y, Mito T, Mizumaki M. Efficient NMR measurement and data analysis supported by the Bayesian inference: The case of the heavy fermion compound YbCo 2Zn 20. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 357:107585. [PMID: 37952430 DOI: 10.1016/j.jmr.2023.107585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
We propose a data-driven technique to infer microscopic physical quantities from nuclear magnetic resonance (NMR) spectra, in which the data size and quality required for the Bayesian inference are investigated. The 59Co-NMR measurement of YbCo2Zn20 single crystal generates complex spectra with 28 peaks. By exploiting the site symmetry in the crystal structure, the isotropic Knight shift Kiso and nuclear quadrupole resonance (NQR) frequency νQ were respectively estimated to be Kiso=0.7822±0.0090% and νQ=2.008±0.016 MHz (T=20 K and H≃10.2 T) by analyzing only 30 data points from one spectrum. The estimated νQ is consistent with the precise value obtained in the NQR experiment. Our method can significantly reduce the measurement time and the computational cost of data analysis in NMR experiments.
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Affiliation(s)
- H Ueda
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - S Katakami
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - M Okada
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan.
| | - S Yoshida
- Department of Material Science, Graduate School of Science, University of Hyogo, Ako-gun, Hyogo, 678-1297, Japan
| | - Y Nakai
- Department of Material Science, Graduate School of Science, University of Hyogo, Ako-gun, Hyogo, 678-1297, Japan
| | - T Mito
- Department of Material Science, Graduate School of Science, University of Hyogo, Ako-gun, Hyogo, 678-1297, Japan
| | - M Mizumaki
- Faculty of Science, Course for Physical Sciences, Kumamoto University, Kurokami, Kumamoto 860-8555, Japan
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Passian A, Buchs G, Seck CM, Marino AM, Peters NA. The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era. SENSORS (BASEL, SWITZERLAND) 2022; 23:115. [PMID: 36616717 PMCID: PMC9823675 DOI: 10.3390/s23010115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/08/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing-an emerging paradigm to overcome the shortcomings of cloud-based computing-could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator-a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
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O’Brien TE, Ioffe LB, Su Y, Fushman D, Neven H, Babbush R, Smelyanskiy V. Quantum computation of molecular structure using data from challenging-to-classically-simulate nuclear magnetic resonance experiments. PRX QUANTUM : A PHYSICAL REVIEW JOURNAL 2022; 3:030345. [PMID: 36624758 PMCID: PMC9825292 DOI: 10.1103/prxquantum.3.030345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR). We focus on learning the anisotropic dipolar term of the Hamiltonian, which generates dynamics that are challenging to classically simulate in some contexts. We demonstrate the ability to directly estimate the Jacobian and Hessian of the corresponding learning problem on a quantum computer, allowing us to learn the Hamiltonian parameters. We develop algorithms for performing this computation on both noisy near-term and future fault-tolerant quantum computers. We argue that the former is promising as an early beyond-classical quantum application since it only requires evolution of a local spin Hamiltonian. We investigate the example of a protein (ubiquitin) confined on a membrane as a benchmark of our method. We isolate small spin clusters, demonstrate the convergence of our learning algorithm on one such example, and then investigate the learnability of these clusters as we cross the ergodic to non-ergodic phase transition by suppressing the dipolar interaction. We see a clear correspondence between a drop in the multifractal dimension measured across many-body eigenstates of these clusters, and a transition in the structure of the Hessian of the learning cost function (from degenerate to learnable). Our hope is that such quantum computations might enable the interpretation and development of new NMR techniques for analyzing molecular structure.
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Affiliation(s)
| | - Lev B. Ioffe
- Google Quantum AI, Venice, CA 90291, United States
| | - Yuan Su
- Google Quantum AI, Venice, CA 90291, United States
| | - David Fushman
- Department of Chemistry and Biochemistry, Center for Biomolecular Structure and Organization, University of Maryland, College Park, MD 20742, United States
| | | | - Ryan Babbush
- Google Quantum AI, Venice, CA 90291, United States
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Immel S, Köck M, Reggelin M. NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations. Mar Drugs 2021; 20:14. [PMID: 35049868 PMCID: PMC8781118 DOI: 10.3390/md20010014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/12/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar couplings (RDCs). In contrast to evaluating pre-calculated structures against NMR restraints, multiple configurations (diastereomers) and conformations are generated automatically within the experimental limits. In this report, we show that the "unphysical" rDG pseudo energies defined from NMR violations bear statistical significance, which allows assigning probabilities to configurational assignments made that are fully compatible with the method of Bayesian inference. These "diastereomeric differentiabilities" then even become almost independent of the actual values of the force constants used to model the restraints originating from NOE or RDC data.
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Affiliation(s)
- Stefan Immel
- Clemens-Schöpf-Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany
| | - Matthias Köck
- Alfred-Wegener-Institut für Polar-und Meeresforschung in der Helmholtz-Gemeinschaft, Am Handelshafen 12, 27570 Bremerhaven, Germany;
| | - Michael Reggelin
- Clemens-Schöpf-Institut für Organische Chemie und Biochemie, Technische Universität Darmstadt, Alarich-Weiss-Straße 4, 64287 Darmstadt, Germany
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McClean JR, Rubin NC, Lee J, Harrigan MP, O'Brien TE, Babbush R, Huggins WJ, Huang HY. What the foundations of quantum computer science teach us about chemistry. J Chem Phys 2021; 155:150901. [PMID: 34686056 DOI: 10.1063/5.0060367] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.
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Affiliation(s)
- Jarrod R McClean
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | - Nicholas C Rubin
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | - Joonho Lee
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | | | - Thomas E O'Brien
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | - Ryan Babbush
- Google Quantum AI, 340 Main Street, Venice, California 90291, USA
| | | | - Hsin-Yuan Huang
- Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences, Caltech, Pasadena, California 91125, USA
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Yu S, Gao Y, Chen BB, Li W. Learning the Effective Spin Hamiltonian of a Quantum Magnet. CHINESE PHYSICS LETTERS 2021; 38:097502. [DOI: 10.1088/0256-307x/38/9/097502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
To understand the intriguing many-body states and effects in the correlated quantum materials, inference of the microscopic effective Hamiltonian from experiments constitutes an important yet very challenging inverse problem. Here we propose an unbiased and efficient approach learning the effective Hamiltonian through the many-body analysis of the measured thermal data. Our approach combines the strategies including the automatic gradient and Bayesian optimization with the thermodynamics many-body solvers including the exact diagonalization and the tensor renormalization group methods. We showcase the accuracy and powerfulness of the Hamiltonian learning by applying it firstly to the thermal data generated from a given spin model, and then to realistic experimental data measured in the spin-chain compound copper nitrate and triangular-lattice magnet TmMgGaO4. The present automatic approach constitutes a unified framework of many-body thermal data analysis in the studies of quantum magnets and strongly correlated materials in general.
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