Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session K36: Quantum Digital and Analog AlgorithmsFocus Recordings Available

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Sponsoring Units: DQI Chair: Daniel Weiss, Northwestern University Room: McCormick Place W194A 
Tuesday, March 15, 2022 3:00PM  3:12PM 
K36.00001: Say NO to Optimization: A NonOrthogonal Quantum Eigensolver Unpil Baek, Diptarka Hait, James Shee, Oskar Leimkuhler, William J Huggins, Martin P HeadGordon, Birgitta Whaley A balanced description of both static and dynamic correlations in electronic systems with nearly degenerate lowlying states presents a challenge for multiconfigurational methods on classical computers. A recent quantum protocol, known as the NonOrthogonal Variational Quantum Eigensolver (NOVQE), combines nonorthogonal configurational interaction (NOCI) with the hybrid quantumclassical variational quantum eigensolver (VQE), to simulate strong correlations in systems such as the πsystem of Hexatriene. However, NOVQE has been found to require a formidable number of optimization steps and associated measurements to describe energetics within chemical accuracy. We present a novel protocol that drastically reduces the required number of measurements and provides a nonvariational counterpart denoted as NOQE. Given an efficient ansatz parametrization inspired by classical techniques, NOQE succeeds in capturing accurate electronic correlation while reducing both the classical and quantum computational costs. We demonstrate the success of our approach with two chemical systems: a stretched hydrogen molecule and a square configuration of H_{4}. 
Tuesday, March 15, 2022 3:12PM  3:24PM 
K36.00002: A quantum optimal control perspective on variational quantum algorithms Alicia B Magann, Christian Arenz, Matthew Grace, TakSan Ho, Robert Kosut, Jarrod McClean, Herschel Rabitz, Mohan Sarovar The availability of noisy quantum processors has sparked a flurry of research into the development of variational quantum algorithms (VQAs). However, progress is needed to improve the utility of VQAs in practice. In this talk, I will explore a variety of ways that the theory of quantum optimal control could be used to enable advances in VQA research. I will begin by identifying VQAs and quantum optimal control as formulations of variational optimization at the circuit level and pulse level, respectively. I will then discuss ways that these different levels of abstraction may be connected, in order to facilitate the application of quantum control theory to VQA challenges associated with ansatz selection, optimization landscapes, noise, and robustness. Examples of recent work at the intersection of quantum control and VQAs will be given, and the talk will conclude with a discussion of open questions and a look to the future. This work was supported by DOE SC ASCR through the Quantum Computing Application Teams program. Sandia National Labs is a multimission laboratory managed and operated by NTESS, LLC, a wholly owned subsidiary of Honeywell International Inc., for DOE’s NNSA under contract DENA0003525. 
Tuesday, March 15, 2022 3:24PM  3:36PM 
K36.00003: Benchmarking digitalanalog quantum algorithms against their digital version Vicente Pina Canelles, Hermanni Heimonen, Mario Ponce, Jami Rönkkö, Martin Leib, Ines de Vega, Adrian Auer The digitalanalog quantum computation (DAQC) paradigm combines local unitaries with the analog evolution of a device. This potentially increases the robustness of quantum computations with respect to digital quantum computation (DQC) in noisy intermediatescale quantum (NISQ) devices. In this work, we analyze this by presenting simulations of the ideal evolution of a DAQC algorithm on a star topology chip. Then, we include in the simulations some of the main error sources that exist in NISQ devices. For our simulations, we use our recently developed software framework for designing digitalanalog quantum algorithms that can be adapted to specific quantum architectures. 
Tuesday, March 15, 2022 3:36PM  4:12PM 
K36.00004: Classical verification of quantum computational advantage Invited Speaker: Gregory D KahanamokuMeyer An important milestone on the path to applicationready quantum computing is the demonstration of quantum computational advantage: solving some problem faster on a quantum computer than would be possible on any classical computer. Excitingly, several experiments have already performed sampling problems which are believed to be intractable for even the world's top supercomputers. But a challenge arises in the verification of these experiments: checking the quantum computer's output requires exponential classical resources, so correctness can be verified for only moderate problem sizes. Here we present protocols for efficientlyverifiable quantum advantage, through cryptographic "proofs of quantumness." These protocols have the combined advantages of polynomialtime classical verification, and security against even adversarial classical impostors via wellstudied cryptographic hardness assumptions. After discussion of the protocols we present progress toward their implementation on nearterm quantum devices. 
Tuesday, March 15, 2022 4:12PM  4:24PM 
K36.00005: Benchmark Study of Quantum Algorithms for Combinatorial Optimization: Unitary versus Dissipative Krishanu R Sankar, Artur Scherer, Satoshi Kako, Sam Reifenstein, Navid Ghadermarzy, Willem B Krayenhoff, Yoshitaka Inui, Edwin Ng, Tatsuhiro Onodera, Pooya Ronagh, Yoshihisa Yamamoto

Tuesday, March 15, 2022 4:24PM  4:36PM 
K36.00006: FiniteSize Scaling on a Digital Quantum Simulator using Quantum Restricted Boltzmann Machines Bilal Khalid, Shree Hari Sureshbabu, Arnab Banerjee, Sabre Kais The critical point and the critical exponents for a quantum phase transition can be determined using the finitesize scaling (FSS) analysis. However, this method is computationally expensive for manybody systems in which the size of the Hilbert space grows exponentially with the size of the system. In recent years, digital quantum simulators (DQS) have emerged as a promising platform to carry out classically intractable calculations for quantum manybody systems. An important question that arises is whether the FSS method has an efficient implementation on a DQS. In this work, we take a step towards addressing this question. We perform a proofofprinciple demonstration of FSS for the phase transition in Quantum Rabi Model using the Quantum Restricted Boltzmann Machine algorithm. Using the quantum algorithm, we work out the critical coupling strength beyond which the system enters a superradiant phase. Our work opens up a new direction in the study of Phase Transitions on DQS. 
Tuesday, March 15, 2022 4:36PM  4:48PM 
K36.00007: Quantifying the Impact of Precision Errors on Quantum Approximate Optimization Algorithms Gregory Quiroz, Paraj Titum, Phillip C Lotshaw, Pavel Lougovski, Kevin Schultz, Eugen Dumitrescu, Itay Hen The quantum approximate optimization algorithm (QAOA) is a hybrid quantumclassical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between intervals of controlled quantum evolution. Here, we examine the effect of analog precision errors on QAOA performance both from the perspective of algorithmic training and canonical state and observabledependent QAOArelevant metrics. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. We show using both analytical techniques and numerical simulations that errors in the analog implementation of QAOA circuits hinder its performance as an optimization algorithm. In particular, we find that any fixed precision implementation of QAOA will be subject to an exponential degradation in performance dependent upon the number of optimal QAOA layers and magnitude of the precision error. Despite this significant reduction, we show that it is possible to mitigate precision errors in QAOA via digitization of the variational parameters, therefore at the cost of increasing circuit depth. We illustrate our results via numerical simulations and analytic and empirical error bounds as a comparison. While focused on precision errors, our approach naturally lends itself to more general noise scenarios and the calculation of error bounds on QAOA performance and broader classes of variational quantum algorithms. 
Tuesday, March 15, 2022 4:48PM  5:00PM 
K36.00008: QCHOP: Quantum Constrained Hamiltonian OPtimization Pranav Gokhale We introduce, QCHOP (Quantum Constrained Hamiltonian OPtimization), a method for solving constrained optimization problems with a quantum computer. QCHOP extends a recently discovered adiabatic quantum algorithm to support arbitrary constraints and objective functions. The method yields highquality solutions with short evolution times by confining evolution to occur within the degenerate subspace of feasible solutions. We demonstrate QCHOP for constrained optimization problems like Maximum Weighted Independent Set, which push the limits of classical solvers. QCHOP on a small graph configuration problem achieves 90% success probability, a >15x advantage over random guessing. Moreover, this result is attained with modest quantum evolution time of T = N^{2}, discretized over 40 Trotter steps. 
Tuesday, March 15, 2022 5:00PM  5:12PM 
K36.00009: Geometric quantum adiabatic path for molecular eigensystems Hongye Yu, Deyu Lu, Qin Wu, TzuChieh Wei We propose a quantum algorithm based on adiabatic evolution to obtain molecular eigenstates and eigenenergies, which exploits slow stretching of bonding lengths and possibly angles. We refer to this scheme as geometric quantum adiabatic evolution (GeoQAE). In a previous work, we used a Hamiltonian path connecting to the final molecular Hamiltonian from the associated maximally commuting one, but this McQAE approach encounters small energy gaps and level crossing at large molecular distances. Our new GeoQAE approach solves this problem and we simulate the quantum evolution and the final energy in several examples, including H_{2}O, CH_{2}, and a chemical reaction study on H_{2}+D_{2} →2HD. 
Tuesday, March 15, 2022 5:12PM  5:24PM 
K36.00010: Hybrid QuantumClassical Algorithms for Loan Collection Optimization with Loan Loss Provisions Jirawat Tangpanitanon, Jirawat Saiphet, Pantita Palittapongarnpim, Poompong Chaiwongkhot, Pinn Prugsanapan, Nuntanut Raksasri, Yarnvith Raksri, Thiparat Chotibut Banks are required to set aside funds in their income statement, known as a loan loss provision (LLP), to account for potential loan defaults and expenses. By treating the LLP as a global constraint, we propose a hybrid quantumclassical algorithm to solve Quadratic Constrained Binary Optimization (QCBO) models for loan collection optimization. The objective is to find a set of optimal loan collection actions that maximizes the expected net profit presented to the bank as well as the financial welfare in the financial network of debtors, while keeping the LLP at its minimum. Our algorithm consists of three parts: a classical divideandconquer algorithm to enable a largescale optimization, a quantum alternating operator ansatz (QAOA) algorithm to maximize the objective function, and a classical sampling algorithm to handle the LLP. We apply the algorithm to a realworld dataset with 600 debtors and 5 possible collection actions. The QAOA is performed using up to 35 qubits on a classical computer. We show that the presence of the QAOA can improve the expected net profit by approximately 70%, compared to when the QAOA is absent from the hybrid algorithm. Our work illustrates the use of nearterm quantum devices to tackle realworld optimization problems. 
Tuesday, March 15, 2022 5:24PM  5:36PM 
K36.00011: NISQHHL: Portfolio Optimization for NearTerm Quantum Hardware Romina Yalovetzky, Pierre Minssen, Dylan Herman, Marco Pistoia Portfolio optimization is an essential use case in finance, but its computational complexity forces financial institutions to resort to approximated solutions, which are still time consuming. Thus, the scientific community is looking at how Quantum Computing can be used for efficient and accurate portfolio optimization. 
Tuesday, March 15, 2022 5:36PM  5:48PM 
K36.00012: Reflectionbased adiabatic groundstate preparation Artur Scherer, Jessica Lemieux, Pooya Ronagh

Tuesday, March 15, 2022 5:48PM  6:00PM 
K36.00013: Subspace variational quantum simulator on superconducting qubit system Heya Kentaro, Nakanishi M Ken, Mitarai Kosuke, Zhiguang Yan, Zuo Kun, Suzuki Yasunari, Sugiyama Takanori, Tamate Shuhei, Tabuchi Yutaka, Fujii Keisuke, Nakamura Yasunobu Quantum simulation is one of the key applications of quantum computing, which accelerates research and development in the fields such as chemistry and material science. The recent development of noisy intermediatescale quantum (NISQ) devices urges the exploration of applications without the necessity of quantum error correction. Here, we propose an efficient method to simulate quantum dynamics driven by a static Hamiltonian on NISQ devices, named subspace variational quantum simulator (SVQS). SVQS employs the subspacesearch variational eigensolver (SSVQE) to find a lowlying subspace and extends it to simulate dynamics within the subspace with lower overhead compared to the existing schemes. We successfully simulate the time evolution of a quantum state in a hydrogen molecule with the subspace process fidelity from 0.90 to 0.99 using superconducting qubits. 
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