Quantum Simulation of Open Quantum Systems
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UC Berkeley UC Berkeley Electronic Theses and Dissertations
UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Applications of Near-Term Quantum Computers Permalink https://escholarship.org/uc/item/2fg3x6h7 Author Huggins, William James Publication Date 2020 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Applications of Near-Term Quantum Computers by William Huggins A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Chemistry in the Graduate Division of the University of California, Berkeley Committee in charge: Professor K. Birgitta Whaley, Chair Professor Martin Head-Gordon Professor Joel Moore Summer 2020 Applications of Near-Term Quantum Computers Copyright 2020 by William Huggins 1 Abstract Applications of Near-Term Quantum Computers by William Huggins Doctor of Philosophy in Chemistry University of California, Berkeley Professor K. Birgitta Whaley, Chair Quantum computers exist today that are capable of performing calculations that challenge the largest classical supercomputers. Now the great challenge is to make use of these devices, or their successors, to perform calculations of independent interest. This thesis focuses on a family of algorithms that aim to accomplish this goal, known as variational quantum algorithms, and the challenges that they face. We frame these challenges in terms of two kinds of resources, the number of operations that we can afford to perform in a single quantum circuit, and the overall number of circuit repetitions required. Before turning to our specific contributions, we provide a review of the electronic structure problem, which serves as a central example for our work. We also provide a self-contained introduction to the field of quantum computing and the notion of a variational quantum algorithm. -
Quantum Algorithms with Applications to Simulating Physical Systems
University of New Mexico UNM Digital Repository Physics & Astronomy ETDs Electronic Theses and Dissertations Summer 7-26-2019 Quantum Algorithms with Applications to Simulating Physical Systems Anirban Ch Narayan Chowdhury University of New Mexico - Main Campus Follow this and additional works at: https://digitalrepository.unm.edu/phyc_etds Part of the Astrophysics and Astronomy Commons, Quantum Physics Commons, and the Theory and Algorithms Commons Recommended Citation Ch Narayan Chowdhury, Anirban. "Quantum Algorithms with Applications to Simulating Physical Systems." (2019). https://digitalrepository.unm.edu/phyc_etds/229 This Dissertation is brought to you for free and open access by the Electronic Theses and Dissertations at UNM Digital Repository. It has been accepted for inclusion in Physics & Astronomy ETDs by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected], [email protected], [email protected]. i Anirban Ch Narayan Chowdhury Candidate Physics and Astronomy Department, The University of New Mexico This dissertation is approved, and it is acceptable in quality and form for publication: Approved by the Dissertation Committee: Dr. Rolando Somma, Chair Dr. Ivan Deutsch Dr. Andrew Landahl Dr. Akimasa Miyake Defended June 24th, 2019. ii Quantum Algorithms with Applications to Simulating Physical Systems by Anirban Ch Narayan Chowdhury M.S., Physics, University of New Mexico, 2015 M.S., Indian Institute of Science Education and Research Pune, 2013 B.S., Indian Institute of Science Education and Research Pune, 2013 DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Physics The University of New Mexico Albuquerque, New Mexico December, 2019 iii c 2019, Anirban Ch Narayan Chowdhury All rights reserved iv In memory of my grandparents. -
A Quantum Algorithm for Simulating Non-Sparse Hamiltonians
A quantum algorithm for simulating non-sparse Hamiltonians Chunhao Wang∗y Leonard Wossnigz Abstract We present a quantum algorithm for simulating the dynamics of Hamiltonians that are not necessarily sparse. Our algorithm is based on the input model where the entries of the Hamiltonian are stored in a data structure in a quantum random access memory (qRAM) which allows for the efficient preparation of states that encode the rows of the Hamiltonian. We use a linear combination of quantum walks to achieve poly-logarithmic dependence on precision.p The time complexity of our algorithm, measured in terms of the circuit depth, is O(t NkHk polylog(N; tkHk; 1/)), where t is the evolution time, N is the dimension of the system, and is the error in the final state, which we call precision. Our algorithm can be directly applied asp a subroutine for unitary implementation and quantum linear systems solvers, achieving Oe( N) dependence for both applications. 1 Introduction 1.1 Background and main results Hamiltonian simulation is the problem of simulating the dynamics of quantum systems, which is the original motivation for quantum computers [19, 20]. It has been shown to be BQP-hard and is hence conjectured not to be classically solvable in polynomial time, since such an algorithm would imply an efficient classical algorithm for any problem with an efficient quantum algorithm, including integer factorization [38]. Different input models have been considered in previous quantum algorithms for simulating Hamiltonian evolution. The local Hamiltonian model is specified by the local terms of a Hamiltonian. The sparse-access model for a sparse Hamiltonian H is specified by the following two oracles: OS ji; ji jzi 7! ji; ji jz ⊕ Si;ji ; and (1) OH ji; ji jzi 7! ji; ji jz ⊕ Hi;ji ; (2) where Si;j is the j-th nonzero entry of the i-th row and ⊕ denotes the bit-wise XOR. -
Time-Dependent Hamiltonian Simulation with L1-Norm Scaling
Time-dependent Hamiltonian simulation with L1-norm scaling Dominic W. Berry1, Andrew M. Childs2,3, Yuan Su2,3, Xin Wang3,4, and Nathan Wiebe5,6,7 1Department of Physics and Astronomy, Macquarie University, Sydney, NSW 2109, Australia 2Department of Computer Science, University of Maryland, College Park, MD 20742, USA 3Institute for Advanced Computer Studies and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA 4Institute for Quantum Computing, Baidu Research, Beijing 100193, China 5Department of Physics, University of Washington, Seattle, WA 98195, USA 6Pacific Northwest National Laboratory, Richland, WA 99354, USA 7Google Inc., Venice, CA 90291, USA The difficulty of simulating quantum dynamics depends on the norm of the Hamiltonian. When the Hamiltonian varies with time, the simulation complex- ity should only depend on this quantity instantaneously. We develop quantum simulation algorithms that exploit this intuition. For sparse Hamiltonian simu- 1 R t lation, the gate complexity scales with the L norm 0 dτ kH(τ)kmax, whereas the best previous results scale with t maxτ∈[0,t] kH(τ)kmax. We also show anal- ogous results for Hamiltonians that are linear combinations of unitaries. Our approaches thus provide an improvement over previous simulation algorithms that can be substantial when the Hamiltonian varies significantly. We introduce two new techniques: a classical sampler of time-dependent Hamiltonians and a rescaling principle for the Schrödinger equation. The rescaled Dyson-series al- gorithm is nearly optimal with respect to all parameters of interest, whereas the sampling-based approach is easier to realize for near-term simulation. -
Report on Enabling the Quantum Leap
Enabling the quantum leap Quantum algorithms for chemistry and materials Report on a National Science Foundation workshop Alexandria, Virginia: January 21-24, 2019 Report on the NSF Workshop on Enabling Quantum Leap: Quantum algorithms for quantum chemistry and materials Bela Bauer1, Sergey Bravyi2, Mario Motta3,and Garnet Kin-Lic Chan4 1 Station Q, Microsoft Corporation, Santa Barbara, California 93106 USA 2 IBM T. J. Watson Research Center, Yorktown Heights, USA 3 IBM Almaden Research, San Jose, CA 95120, USA 4 California Institute of Technology, Pasadena, CA 91125, USA November 12, 2019 Acknowledgements This report is derived from an NSF workshop held between January 22-24 2019 in Alexandria VA. Funding for the workshop and for the production of the report was provided by the National Science Foundation via award no. CHE 1909531. The workshop participants and website are provided below. Workshop Participants Workshop Co-Chairs: • Garnet Kin-Lic Chan (California Institute of Technology) • Sergey Bravyi (IBM T. J. Watson Research Center) • Bela Bauer (Microsoft Station Q) Participant List: Chemistry and Molecular Science • Victor Batista (Yale) • Francesco Evangelista (Emory) •Tim Berkelbach (UChicago) • Joe Subotnik (UPenn) • Tucker Carrington (Queens U.) • Haobin Wang (CU Denver) • Garnet Chan (Caltech) •Dominika Zgid (UMich) • Gavin Crooks (Rigetti Computing) Materials Science, condensed-matter physics • Bela Bauer (Microsoft) • Barbara Jones (IBM Almaden) • Bryan Clark (UIUC) • Austin Minnich (Caltech) •Tom Deveraux (Stanford) • -
Arxiv:2003.12458V2 [Quant-Ph] 14 Jun 2021 to a Classical finite-Difference Solver
Practical Quantum Computing: solving the wave equation using a quantum approach 1, 2, 1 3 Adrien Suau, ∗ Gabriel Staffelbach, and Henri Calandra 1CERFACS, 42 Avenue Gaspard Coriolis, 31057 Toulouse, France 2LIRMM, University of Montpellier, 161 rue Ada, 34095 Montpellier, France 3TOTAL SA, 2 Avenue de Vignancour, 64000 Pau, France (Dated: June 15, 2021) In the last years, several quantum algorithms that try to address the problem of partial differential equation solving have been devised. On one side, \direct" quantum algorithms that aim at encoding the solution of the PDE by executing one large quantum circuit. On the other side, variational algorithms that approximate the solution of the PDE by executing several small quantum circuits and making profit of classical optimisers. In this work we propose an experimental study of the costs (in terms of gate number and execution time on a idealised hardware created from realistic gate data) associated with one of the \direct" quantum algorithm: the wave equation solver devised in [PCS. Costa, S. Jordan, A. Ostrander, Phys. Rev. A 99, 012323, 2019]. We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm. We also explain in great details the implementation steps and discuss some possibilities of improvements. Finally, our implementation proves experimentally that some PDE can be solved on a quantum computer, even if the direct quantum algorithm chosen will require error-corrected quantum chips, which are not believed to be available in the short-term. I. INTRODUCTION Quantum computing has drawn a lot of attention in the last few years, following the successive announcements from several world-wide companies about the implementation of quantum hardware with an increasing number of qubits or reduced error rates [4, 8, 9, 12, 52]. -
Arxiv:2001.03685V2 [Quant-Ph] 11 Jul 2020 Description of Quantum Mechanical Processes Is Always Prob- Abilistic
Quantum algorithms for quantum chemistry and quantum materials science Bela Bauer,1 Sergey Bravyi,2 Mario Motta,3 and Garnet Kin-Lic Chan4 1Microsoft Quantum, Station Q, University of California, Santa Barbara, CA 93106, USA∗ 2IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USAy 3IBM Almaden Research Center, San Jose, CA 95120, USAz 4Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USAx As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world. While for many years, the ability to execute quantum algorithms was only a theoretical possibility, recent advances in hardware mean that quantum computing devices now exist that can carry out quantum computation on a limited scale. Thus it is now a real possibility, and of central importance at this time, to assess the potential impact of quantum computers on real problems of interest. One of the earliest and most compelling applications for quantum computers is Feynmans idea of simulating quantum systems with many degrees of freedom. Such systems are found across chemistry, physics, and materials science. The particular way in which quantum computing extends classical computing means that one cannot expect arbitrary simulations to be sped up by a quantum computer, thus one must carefully identify areas where quantum advantage may be achieved. In this review, we briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics, that are of potential interest for solution on a quantum computer. -
10 Apr 2018 IV
Quantum Algorithm Implementations for Beginners Patrick J. Coles, Stephan Eidenbenz,∗ Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Lokhov, Alexander Malyzhenkov, David Mascarenas, Susan Mniszewski, Balu Nadiga, Dan O’Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Phil Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard Zamora, and Wei Zhu Los Alamos National Laboratory, Los Alamos, New Mexico, USA As quantum computers have become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classic computer programs for most of their career. While currently available quantum computers have less than 100 qubits, quantum computer hardware is widely expected to grow in terms of qubit counts, quality, and connectivity. Our article aims to explain the principles of quantum programming, which are quite different from classical programming, with straight-forward algebra that makes understanding the underlying quantum mechanics optional (but still fascinating). We give an introduction to quantum computing algorithms and their implementation on real quantum hardware. We survey 20 different quantum algorithms, attempting to describe each in a succintc and self-contained fashion; we show how they are implemented on IBM’s quantum computer; and in each case we discuss the results of the implementation with respect to differences of the simulator and the actual hardware runs. This article introduces computer scientists and engineers to quantum algorithms and provides a blueprint for their implementations. Contents I. Introduction 3 A.