Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session B01: Towards Discovery in Chemistry with Quantum Computers IIFocus Recordings Available

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Sponsoring Units: DCP Chair: Barbara Jones, IBM Room: McCormick Place W175A 
Monday, March 14, 2022 11:30AM  12:06PM 
B01.00001: Quantum MachineLearning algorithm for Complex Chemical Systems Invited Speaker: Sabre Kais In this talk, I will give a brief overview of developing quantum algorithms for electronic structure calculations and open quantum dynamics of chemical systems on quantum devices. Then focus on quantum machine learning, particularly the Restricted Boltzmann Machine (RBM), as it emerged to be a promising alternative approach leveraging the power of quantum computers. To demonstrate its efficacy, we show its performance on calculating electronic structure of small molecular systems like LiH, H_{2}O and also computation of band structures in 2D materials like graphene, hBN and monolayer transitionmetal dichalcogenides which are hitherto unexplored in quantum simulations. We also discuss extending the approach to treat complex open quantum dynamical processes. We thus expect our protocol to provide a new alternative in exploring electronic structure and dynamics of complex chemical systems. 
Monday, March 14, 2022 12:06PM  12:18PM 
B01.00002: Quantum Computational Quantification of ProteinLigand Interactions Cono Di Paola, Josh J Kirsopp, David Zsolt Manrique, Michal Krompiec, Gabriel GreeneDiniz, Wolfgang Guba, Agnes Meyder, Detlef Wolf, Martin Strahm, David Munoz Ramo A prototypical hybrid classical and quantum computational tools stack for the quantification of proteinligand interactions is presented herein by using binding energy differences to rankorder a series of βsecretase (BACE1) inhibitors. Due to the large Hilbert space involved, the combination of QM/MM and Density Matrix Embedding Theory (DMET) procedure involving reduced active orbital space with the Variational Quantum Eigensolver (VQE) approach is proven to be effective for finding the ground states of such complex systems. To reduce the deviceinduced noise, two different techniques were employed: (i) the State Preparation and Measurement (SPAM) error mitigation and (ii) a new flavour of Symmetry Verification approach, the inhouse developed Partition Measurement Symmetry Verification (PMSV) algorithm, which is unique in the sense that it does not require any extra quantum resources. The experiments were conducted on the latest superconducting transmon (IBM) and trappedion (Honeywell) Noisy Intermediate Scale Quantum (NISQ) devices. This is the first application of real quantum computers to the calculation of proteinligand interaction. The results shed light on hardware and software requirements which would enable the application of NISQ algorithms in drug design. 
Monday, March 14, 2022 12:18PM  12:30PM 
B01.00003: Towards the Simulation of Large Scale ProteinLigand Interactions on NISQera Quantum Computers Michael Streif, Fionn D Malone, Robert M Parrish, Alicia R Welden, Thomas Fox, Matthias Degroote, Elica Kyoseva, Nikolaj Moll, Raffaele Santagati We explore the use of symmetryadapted perturbation theory (SAPT) as a simple and efficient means to compute interaction energies between large molecular systems with a hybrid method combing NISQera quantum and classical computers. From the one and twoparticle reduced density matrices of the monomer wavefunctions obtained by the variational quantum eigensolver (VQE), we compute SAPT contributions to the interaction energy [SAPT(VQE)]. At first order, this energy yields the electrostatic and exchange contributions for noncovalently bound systems. We empirically find from ideal statevector simulations that the SAPT(VQE) interaction energy components display orders of magnitude lower absolute errors than the corresponding VQE total energies. Therefore, even with coarsely optimized lowdepth VQE wavefunctions, we still obtain sub kcal/mol accuracy in the SAPT interaction energies. In SAPT(VQE), the quantum requirements, such as qubit count and circuit depth, are lowered by performing computations on the separate molecular systems. Furthermore, we benchmark SAPT(VQE) against a handful of small multireference dimer systems and the iron center containing human cancerrelevant protein lysinespecific demethylase 5 (KDM5A). 
Monday, March 14, 2022 12:30PM  12:42PM 
B01.00004: Peptide Design with Quantum Approximate Optimization Algorithm Alexey Galda, Vikram K Mulligan, Ian MacCormack, Gavin E Crooks, Hans Melo A protein is a long chain of amino acids that folds into a welldefined threedimensional structure. The amino acid sequence determines the folded structure, which in turn determines the protein's function. Computational tools allow the design of new amino acid sequences, giving rise to new folds and new functions, and permitting the creation of large and small proteins (peptides) with applications in industrial manufacturing, medicine, and agriculture. The protein design problem is NPcomplete, with the search space scaling exponentially with the number of amino acids. There is no known efficient classical algorithm able to find optimal sequences. In this work, we propose a formulation of the protein design problem amenable to the quantum approximate optimization algorithm (QAOA). By taking advantage of the qubitefficient mapping, we design several small peptides on a trappedion quantum processor, demonstrating the feasibility and favourable scalability of the approach. This work establishes the utility of nearterm universal quantum computers for protein design. 
Monday, March 14, 2022 12:42PM  12:54PM 
B01.00005: Designing new peptide and protein therapeutics using adiabatic quantum annealers Vikram K Mulligan, Hans Melo, Haley I Merritt, Michael Sawaya, Stewart Slocum, Brian Weitzner, Andrew Watkins, P. Douglas Renfrew, Craig Pelissier, Todd Yeates, Paramjit Arora, Richard Bonneau, Mohit Pandey, Alexey Galda The ability to design new peptide and protein sequences that can adopt folds not found in nature permits new functions to be engineered. Of particular interest is the rational design of peptide and protein therapeutics that are able to bind specifically to, and alter the function of, target biomolecules implicated in human disease. Over the last 20 years, major advancements have been made in classical computing approaches for peptide and protein design. Nevertheless, the design problem is NPcomplete: no known classical algorithm scales well as either the number of amino acids in the polypeptide chain grows large (as in the case of large proteins) or the number of buildingblocks from which one may choose grows large (as in the case of synthetic peptides that can be built from hundreds of artificial amino acid types). This limits the complexity of the design problems that can currently be solved classically. Here, we introduce approaches for designing peptides and proteins using adiabatic quantum annealers to solve the hard combinatorial problem of design, without simplification or reduction to a toy problem. Using the DWave Advantage system, we demonstrate application of our methods to nontrivial peptide design problems. 
Monday, March 14, 2022 12:54PM  1:30PM 
B01.00006: Applications of quantum annealers in chemistry and materials science Invited Speaker: Rosa DiFelice The promise of quantum computing is to provide new methods to unveil the physics of correlated manybody systems. Over the past few years, quantum annealers have grown in complexity to the point that useful applications are feasible. Whilst typical approaches use a quantum annealer to extract the solution of an optimization problem from the ground state of the Ising Hamiltonian, we pursue the application as a generator of structural models for disordered materials, where disorder appears from the competition between the different degrees of freedom. Starting from the representation of the crystal in terms of a network, we map the relevant interactions into Ising Hamiltonians and encode the disordered phases in the excitedstate spectrum of the target Ising Hamiltonian. The quantum annealer is thus used to explore the energy surfaces and to identify stable and metastable phases of prototypical disordered materials. Furthermore, the electronic structure problem can be mapped onto a quantum annealer, as an alternative to the widespread variational quantum eigensolver. We illustrate examples and discuss the possibility of including solvent effects in the molecular electronic structure. 
Monday, March 14, 2022 1:30PM  1:42PM 
B01.00007: Genetic Expression Programming for Quantum Chemistry on Quantum Computers Gonzalo Alvarez, Jacek Jakowski, Stephan Irle The tremendous human effort required to program computers hampers the advancement of science and the development of new technologies. This talk will therefore show how artificial intelligence can accelerate program development efforts: We are designing and implementing a scalable artificial intelligence software generator to construct and run accurate computer programs implementing a function of one or many variables, a function that is only known through a series of inputs and outputs. In order to showcase an application that is both valuable and timely, we apply this computer program generator to create optimal quantum circuits for quantum chemistry problems run on quantum computers. Our computer program relies on genetic expression programming [1], and a preliminary proof of concept implementation can be found at https:// github.com/g1257/evendim. [1] Ferreira, C., Gene Expression Programming, Mathematical Modeling by an Artificial Intelligence, 2nd Ed. SpringerVerlag, Berlin, Heidelberg, 2006. 
Monday, March 14, 2022 1:42PM  1:54PM 
B01.00008: Approximation of Free Energies with Fluctuation Relations on Quantum Hardware Diyi Liu, Katherine Klymko, Lindsay Bassman, Norm M Tubman, Wibe(Bert) A de Jong Fluctuation relations allow for the computation of equilibrium properties, like free energy differences, from an ensemble of nonequilibrium dynamics simulations. Computing them for quantum systems is exponentially hard on classical computers because it requires taking the exponential of an exponentially scaling Hamiltonian. Given that quantum computers can alleviate this hurdle, we propose an algorithm utilizing a fluctuation relation known as the Jarzynski equality to approximate free energy differences of quantum systems on a quantum computer. We prove that the approximation is rigorously an upper bound for the free energy difference and that the computation is exact when the inverse temperature goes to zero or infinity in adiabatic regimes. Additionally, a rigorous bound is given for the nonadiabatic regime. Furthermore, we successfully demonstrate a proofofconcept of our algorithm using the transverse field Ising model on quantum hardware. Since free energies play a critical role in any equilibrium property, our algorithm may eventually serve as a valuable tool in a wide range of applications including the construction of phase diagrams, prediction of transport properties and reaction constants, and computeraided drug design in the future. 
Monday, March 14, 2022 1:54PM  2:06PM 
B01.00009: Theory of Real Time Krylov Subspace Diagonalization for Quantum Computing Algorithms Yizhi Shen, Norm M Tubman, Katherine Klymko, James Sud Here we explore the theoretical and numerical aspects of a generalized Krylov subspace method where the underlying Krylov subspace is built through parameterized real time evolution. We establish an exponential bound on the convergence of the spectral approximation for a gapped target operator. Even in the single step limit, we find that the overlap of approximated lowlying eigenstates with the high energy sector of the operator spectrum undergoes a signature suppression due to favorable cancellation of time evolved phases. As the number of steps increases, phase cancellation gives rise to persistently growing features in the overlap and we show that the convergence of our spectral approximation has a native dependence on the size of spectral gap. For implementation efficiency, we compare the performances of real time evolution with fixed and updated references separately, where optimal time parametrization is examined and analyzed. We also consider the performance when stochasticity gets cast to our target operator via the form of spectral statistics. To demonstrate the practicality of such real time evolution, we discuss application of the scheme to fundamental problems in quantum computation such as unstructured searches and electronic structure predictions for strongly correlated systems. 
Monday, March 14, 2022 2:06PM  2:18PM 
B01.00010: Benchmarking the performance of variational quantum eigensolvers (VQE) applied to the HCN molecule. Goran Wendin, Andrew Tranter, David Muñoz Ramo, Ross Duncan, Phalgun Lolur, Mårten Skogh, Martin Rahm We compute the ground state energy of HCN within several representations of VQE/UCCSD using Qiskit [1,2] and EUMEN [3] to find the minimum number of qubits, variational parameters, and gates needed to perform chemically accurate simulation in a STO6G basis. We compare several different avenues for optimization: fermiontoqubit transformations (JW, BK, parity, paraparticle [4]); imposing existing symmetries (no symmetries; Z2 symmetries with and without explicit molecular orbital symmetrisation); qubit tapering; level of compilation. Using tapering without symmetrised orbitals needs 15 qubits, and 284 variational parameters. Imposing symmetry in PySCF on the initial state results in 14 qubits, and 166 parameters. Finally, applying highlevel tketoptimisation [3] results in a CNOT gate count of about 3300 (total gate count about 4700). The lowest gate count was obtained with EUMEN and tket using a paraparticle approach [4]: 14 qubits, 166 parameters, 2589 CNOT gates, total gate count 3897 for Ansatz preparation, potentially within reach of nearfuture NISQ processors. 
Monday, March 14, 2022 2:18PM  2:30PM 
B01.00011: Polaritonic Unitary Coupled Cluster for Quantum Computations Fabijan Pavosevic, Johannes Flick In the field of polaritonic chemistry, strong lightmatter interactions are used to alter a chemical reaction inside an optical cavity. To explain and understand these processes, the development of reliable theoretical models is essential. While traditional methods have to balance accuracy and system size, new developments in quantum computing, in particular the Variational Quantum Eigensolver (VQE), offer a path for an accurate solution of the electronic Schrödinger equation with the promise of polynomial scaling and eventual quasiexact solutions on currently available quantum devices. In this work, we combine these two fields. In particular, we introduce the quantum electrodynamics unitary coupled cluster (QEDUCC) method combined with the VQE algorithm, as well as the quantum electrodynamics equationofmotion (QEDEOM) method formulated in the qubit basis that allows an accurate calculation of the groundstate and the excitedstate properties of strongly coupled lightmatter systems on a quantum computer. The accuracy and performance of the developed methods is tested for a H$_4$ molecule inside an optical cavity in a regime where strong electronic correlations become significant. For the first time, we explicitly include two photon effects from first principles. We show that the developed methods are in excellent agreement with the exact reference results and can outperform their traditional counterparts. The work presented here sets the stage for future developments of polaritonic quantum chemistry methods suitable for both classical and quantum computers. 
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