Bulletin of the American Physical Society
2021 Fall Meeting of the APS Division of Nuclear Physics
Volume 66, Number 8
Monday–Thursday, October 11–14, 2021; Virtual; Eastern Daylight Time
Session MN: Mini-Symposium: Quantum Information Science and Nuclear Theory VI: Simulation 2 |
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Chair: Martin Savage, UW-Seattle Room: Studio 1 |
Wednesday, October 13, 2021 4:00PM - 4:12PM |
MN.00001: Prospects and Challenges for Realizing Hybrid Digital/Analog Quantum Simulations Through Optimal Control Kyle A Wendt Quantum computers will usher in a new era in simulating quantum many-body systems, bringing us to a deeper and more complete understanding of structure, dynamics, and responses of strongly interacting systems, such as atomic nuclei, and how those systems interact with other forms of matter. Even with the tremendous strides that digital quantum computers have made in the past few years, noise and imprecision continue to inhibit the realization nearly all formal quantum algorithms for simulating physical systems beyond even the simplest models. Instead, hybrid digital-analog quantum computation, where the discrete quantum processor primitives are tailored to both the theoretical system being simulated and the physical quantum processor performing the simulations, offers a path to useful computation that can be realized within the near-term noisy intermediate-scale quantum (NISQ) era. I will discuss the current state of applying optimal control to implement such hybrid computations on super conducting transmon computers with a focus on simulations of the real-time dynamics of interaction neutrons and model spin systems and discuss the challenges and prospects for scaling such computations to larger systems. |
Wednesday, October 13, 2021 4:12PM - 4:24PM |
MN.00002: Adverse Impacts of NISQ Hardware Qubit Errors on Nuclear Physics Applications Patrick Dreher, Kubra Yeter Aydeniz, Zachary Parks, Aadithya Nair, Erik J Gustafson, Alexander F Kemper, Raphael Pooser, Yannick L Meurice One of the most problematic issues that limits the implementation of applications on today's Noisy Intermediate Scale Quantum (NISQ) machines is the impact of qubit errors. Reliance on standard or minimal one and two qubit error measurements from processes such as randomized benchmarking and other similar protocols cannot reliably identify and capture the full impact of these errors. This is a critical problem because coherent qubit errors may degrade the user application results in an unpredictable manner and may compromise efforts to validate the accuracy of applications implemented on these NISQ quantum processors. We report here on an in-depth study of this issue using a transverse Ising model Hamiltonian as a sample user application test case implemented on an IBM Quantum Network superconducting transmon hardware platform using cycle benchmarking. Measurements of inter-day and intra-day qubit calibration drift and placement of the quantum circuit on separate qubit groups in different physical locations on the processor are presented. These results are discussed in the context Nuclear Physics applications implemented on these quantum computing hardware platforms. |
Wednesday, October 13, 2021 4:24PM - 4:36PM |
MN.00003: Control Optimization for Parametric Hamiltonians by Pulse Reconstruction Piero Luchi, francesco turro, Xian Wu, Sofia Quaglioni, Valentina Amitrano, Kyle A Wendt, Jonathan L DuBois, Francesco Pederiva The standard quantum computing approach, based on expressing arbitrary unitary operations in terms of a set of universal quantum gates, has been demonstrated to be in principle efficient for the simulation of complex systems on a quantum computer. In practice, the performance and reliability of the generated real-time evolution suffer from gate error rates and quantum device noise. Optimal control techniques provide a means to tailor the control pulse sequence necessary for the generation of customized quantum gates, which help to reduce gate errors and device noise since it eliminates the need to split an arbitrary gate into its primitive constituents, obtaining a shallower quantum circuit. However, the substantial amount of (classical) computing required for the generation of customized gates can quickly spoil the effectiveness of such an approach, especially when the pulse optimization needs to be iterated. We report the results of device-level quantum simulations of the unitary (real) time evolution of the hydrogen atom, based on superconducting qubit, and propose a method to reduce the computing time required for the generation of the control pulses. We use a simple interpolation scheme to accurately reconstruct the real time-propagator for a given time step starting from pulses obtained for a discrete set of pre-determined time intervals. We also explore an analogous treatment for the case in which the hydrogen atom Hamiltonian is parameterized by the mass of the electron. In both cases we obtain a reconstruction with very high fidelity and a substantial reduction of the computational effort. |
Wednesday, October 13, 2021 4:36PM - 4:48PM |
MN.00004: Adiabatic evolution of quantum states with digital and hybrid digital/analog gates Eduardo A Coello Perez, Joseph Bonitati, Dean J Lee, Sofia Quaglioni, Kyle A Wendt Quantum computers are positioned to provide exact simulations of dynamical processes in microscopic systems currently unsolvable by classical computers. An important step in realizing these and other types of quantum simulations is the preparation of the quantum devices in arbitrary quantum states. Quantum adiabatic evolution algorithms can be employed to slowly bring the quantum computer to the ground state of a final Hamiltonian encoding the desired solution, starting from the ground state of a much simpler initial Hamiltonian. However, the large number of gates typically required by such algorithms (or gate depth) of such algorithms inhibits their efficacy and limits the subsequent use of that prepared state in follow-on simulations. I will present a study on applying hybrid digital/analog gates to mitigate this gate-depth proliferation within a two-spin system, comparing results to more typical digital quantum simulations. |
Wednesday, October 13, 2021 4:48PM - 5:00PM |
MN.00005: Quantum Learning for Accelerated Nuclear Data Analysis and Simulation Andrea Delgado A crucial element of any analysis in nuclear physics involves the simulation of the physical processes and interactions taking place at these facilities to develop new theories and models to explain experimental data and characterize background, study detector response, and plan for detector upgrades. These simulations are often computationally intensive, taking up a significant fraction of the computational resources available to nuclear physicists. Recently, alternative methods for detector simulation and data analysis tasks have been explored, like machine learning applications and quantum information science (QIS). QIS is a rapidly developing field focused on understanding the analysis, processing, and transmission of information using quantum mechanical principles and computational techniques. QIS can address the conventional computing gap associated with NP-related problems, specifically those computational tasks that challenge CPUs and GPUs, such as efficient and accurate event generators. In addition, quantum computing offers unique advantages over classical computing in machine learning and optimization. Nonetheless, adapting these new technologies to the analysis of NP data requires developing domain-specific tools and algorithms, such as quantum machine learning (QML) algorithms tailored to NP applications. In this work, we introduce a quantum generative model trained to simulate events that resemble the training data statistics at the vertex level. |
Wednesday, October 13, 2021 5:00PM - 5:12PM |
MN.00006: Application of the Quantum Equation of Motion to the Lipkin-Meshkov-Glick model. Manqoba Q Hlatshwayo Classical numerical methods for solving the nuclear many-body problem face the impediment of exponential growth of the dimension of the Hilbert space. Quantum algorithms have become an attractive alternative for practitioners. However, due to the limitations of current quantum hardware, a class of hybrid classical-quantum algorithms has been developed to achieve quantum advantage. The Quantum Equation of Motion (qEOM), which is an extension of the Variational Quantum Eigensolver (VQE), is one of the hybrid algorithms recently proposed to calculate excitation energies of molecular Hamiltonians. In this work, we apply the qEOM to find the excitation energies of the Lipkin-Meshkov-Glick (LMG) Hamiltonian and benchmark its accuracy compared with the Hartree-Fock (HF), Random Phase Approximation (RPA), second RPA, and exact analytical solution. We found that the qEOM accurately produced the energy spectrum of the LMG Hamiltonian and has a reasonable scaling of less than O(N1.9) with particle number N. We discuss possible ways to improve the qEOM method by using the qubit-ADAPT-VQE to reduce the circuit depth, and the Finite Amplitude Method to avoid diagonalizing the large classical matrix equation. |
Wednesday, October 13, 2021 5:12PM - 5:24PM |
MN.00007: Testing a quantum algorithm on the Lipkin model. Antonio Marquez Romero, Jonathan H Engel, Sophia Economou, Ho Lun Tang Quantum algorithms promise the ability to tackle problems that scale exponentially on a classical computer. ADAPT-VQE [1] is a quantum algorithm capable of obtaining exact eigenvalues of many-body Hamiltonians by repeatedly optimizing the parameter specifying the many-particle-hole excitation of a reference state as one slowly increases the number excitations. The method has been applied so far mainly to molecules but can also be useful in nuclear physics. Here we examine the method's efficiency in the Lipkin model, which, unlike molecular systems, exhibits both a phase transition and spontaneous symmetry breaking. Though these features reduce the efficiency of the algorithm, relatively simple modifications of the reference state restore it. In the end, neither a nearby phase transition nor symmetry breaking is a significant obstacle. |
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