Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session A40: Noisy Intermediate Scale Quantum Computers IFocus Recordings Available
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Sponsoring Units: DQI DCOMP Chair: Tim Menke, Harvard University Room: McCormick Place W-196B |
Monday, March 14, 2022 8:00AM - 8:12AM |
A40.00001: Reducing runtime and error in VQE using deeper and noisier quantum circuits Amara Katabarwa The rapid development of noisy intermediate-scale quantum (NISQ) devices has raised the question of whether or not these devices will find commercial use. |
Monday, March 14, 2022 8:12AM - 8:24AM |
A40.00002: An open quantum testbed based on trapped ions (QSCOUT) Susan M Clark At Sandia National Laboratories, we are developing and operating an open user testbed for quantum information protocols based on trapped ions, known as the Quantum Scientific Computing Open User Testbed, or QSCOUT. This testbed provides not only the opportunity to perform quantum algorithms and study noisy-intermediate-scale quantum (NISQ) systems, but unlike many commercial testbeds, it also allows users control of the system both at the quantum-circuit level and at the more fundamental pulse-control level to study alternate methods of gate construction and optimization. Here, we present our progress thus far in developing the testbed and running algorithms for our users, as well as some of the experimental and software challenges encountered in developing such a system. We also discuss the current and anticipated capabilities of QSCOUT. |
Monday, March 14, 2022 8:24AM - 8:36AM |
A40.00003: Experimental Benchmark of Randomized Estimators for Measurements of Quantum Hamiltonians Arkopal Dutt, William M Kirby, Antonio Mezzacapo, Charles Hadfield, Sarah Sheldon, Isaac L Chuang The Variational Quantum Eigensolver (VQE) is a quantum algorithm that has emerged as a leading candidate to address quantum ground-state problems on noisy-intermediate scale quantum devices. An important subroutine in VQE involves performing a large number of quantum measurements to construct estimates of the expectation value of a Hamiltonian. Recent efforts have been directed towards measurement procedures based on randomized estimators, with the goal of minimizing the number of measurements required to achieve a given error in the expectation value estimate, compared to non-randomized standard estimators. However, a comparison of the performance of these procedures on real noisy quantum hardware is lacking. To this end, we present benchmark results of recent randomized estimators proposed theoretically. We first carry out a finite-sample analysis of these procedures backed by numerical experiments on an ideal simulator. We then analyze their noise-resiliency through numerical experiments on a noisy simulator with different readout error mitigation techniques. Finally, we test their performance on an IBM Quantum device on a variety of molecular Hamiltonians of increasing size. Further, we propose a novel procedure based on derandomization with prior quantum state information. |
Monday, March 14, 2022 8:36AM - 8:48AM |
A40.00004: Interactive Test for Classically-Verifiable Quantum Advantage Daiwei Zhu, Greg Meyer, Laura Lewis, Crystal Noel, Or Katz, Bahaa Harraz, Qingfeng Wang, Andrew Risinger, Lei O Feng, Debopriyo Biswas, Laird Egan, Alexandru Gheorghiu, Yunseong Nam, Thomas Vidick, Umesh Vazirani, Norman Y Yao, Marko Cetina, Christopher Monroe
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Monday, March 14, 2022 8:48AM - 9:00AM |
A40.00005: Adaptive shot allocation for fast convergence in variational quantum algorithms Andi Gu, Angus Lowe, Pavel Dub, Patrick J Coles, Andrew T Arrasmith Variational Quantum Algorithms (VQAs) are a promising approach for practical applications like chemistry and materials science on near-term quantum computers as they typically reduce quantum resource requirements. However, in order to implement VQAs, an efficient classical optimization strategy is required. Here we present a new stochastic gradient descent method using an adaptive number of shots at each step, called the global Coupled Adaptive Number of Shots (gCANS) method, which improves on prior art in both the number of iterations as well as the number of shots required. These improvements reduce both the time and money required to run VQAs on current cloud platforms. We analytically prove that in a convex setting gCANS achieves geometric convergence to the optimum. Further, we numerically investigate the performance of gCANS on some chemical configuration problems. We also consider finding the ground state for an Ising model with different numbers of spins to examine the scaling of the method. We find that for these problems, gCANS compares favorably to all of the other optimizers we consider. |
Monday, March 14, 2022 9:00AM - 9:12AM |
A40.00006: Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers Di Luo, Ryan Levy, Bryan K Clark Quantum process tomography is crucial for understanding quantum channels and characterizing properties of quantum devices. Inspired by recent advances using classical shadows in quantum state tomography [1], we have developed a method using classical shadows for quantum process tomography, ShadowQPT. We have proved rigorous measurement complexity for ShadowQPT and provided new post-processing techniques for improving the accuracy. Furthermore, our approach has been implemented on the IonQ trapped ion quantum computer; we benchmark reconstructions for unitary and non-unitary processes of a channel of up to n=4 qubits (equivalent to the complexity of n=8 qubits in state tomography) as well as determining input-output state overlaps. We show that ShadowQPT is efficient and provides new advancement on quantum process tomography in near-term and future quantum devices. |
Monday, March 14, 2022 9:12AM - 9:48AM |
A40.00007: Machine Learning of Noise-Resilient Quantum Circuits Invited Speaker: Lukasz Cincio Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers. We present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits. Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state. Given a task and a device model that captures information about the noise and connectivity of qubits in a device, NACL outputs an optimized circuit to accomplish this task in the presence of noise. It does so by minimizing a task-specific cost function over circuit depths and circuit structures. To demonstrate NACL, we construct circuits resilient to a fine-grained noise model derived from gate set tomography on a superconducting-circuit quantum device, for applications including quantum state overlap, quantum Fourier transform, and W-state preparation. |
Monday, March 14, 2022 9:48AM - 10:00AM |
A40.00008: Qubit assignment on NISQ hardware using Simulated Annealing and a Loschmidt Echo heuristic Evan Peters, Gabriel Perdue, Andy C. Y. Li, Prasanth Shyamsundar As the number of qubits available on noisy quantum computers grows, it becomes difficult to efficiently map logical qubits to a subset of physical qubits for use in a quantum computation. Evaluating the device performance using fidelity estimation introduces significant experimental overhead and may be infeasible for many applications. Furthermore, the number of possible mappings grows combinatorially in the number of qubits, motivating the use of heuristic optimization techniques. Here, we study this problem using simulated annealing with a cost function based on the Loschmidt Echo. We provide theoretical justification for this choice of cost function by demonstrating that the optimal qubit assignment coincides with the optimal mapping based on the fidelity function in the weak error limit, and we provide experimental justification using diagnostics performed on Google's superconducting qubit devices. We then establish the performance of simulated annealing for qubit assignment using classical simulations of noisy devices and optimization experiments performed on a quantum processor. Our technique provides a scalable and flexible approach to optimizing the performance of quantum programs executed on near-term hardware. |
Monday, March 14, 2022 10:00AM - 10:12AM |
A40.00009: Orqviz: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms Manuel S Rudolph, Sukin Sim, Asad Raza, Michał Stęchły, Jarrod McClean, Eric R Anschuetz, Alejandro Perdomo-Ortiz Variational Quantum Algorithms (VQAs) are promising candidates for finding practical applications of near to mid-term quantum computers. To best utilize available quantum resources, it is vitally important that we do not treat VQAs as black boxes. Towards this end, there has been an increasing effort to study the intricacies of VQAs, including the presence or absence of barren plateaus, the expressivity of circuit ansätze, and heuristics for parameter initialization. Many of these studies can be linked to the properties of the optimization loss landscape explored for each algorithm. In our work, we present a variety of techniques for visualizing and analyzing the high-dimensional loss landscapes of VQAs. We review and apply the techniques to three examples with diverse application domains: the Quantum Approximate Optimization Algorithm, the Quantum Circuit Born Machine, and the Variational Quantum Eigensolver. Additionally, we include an investigation on the impact of noise due to finite sampling in the estimation of the loss functions. This work is accompanied by the release of the open-source Python package "orqviz", which supports all the techniques discussed. Orqviz enables flexible visual analysis of high-dimensional VQA landscapes. |
Monday, March 14, 2022 10:12AM - 10:24AM |
A40.00010: Stochastic Gradient Line Bayesian Optimization: Reducing Measurement Shots in Variational Quantum Algorithms Shiro Tamiya, Hayata Yamasaki Optimization of parameterized quantum circuits is indispensable for the application of near-term quantum devices to computational tasks using variational quantum algorithms (VQAs). However, existing optimization algorithms require an excessive number of quantum measurement shots, and their cost is a critical obstacle for practical use. In this work, we propose an efficient framework, stochastic gradient line Bayesian optimization (SGLBO), for circuit optimization in VQAs with fewer measurement shots. The key idea of SGLBO is to estimate the appropriate update direction of parameters based on stochastic gradient descent (SGD), and further utilize Bayesian optimization (BO) to estimate the optimal step size at each iteration in SGD. We combined this idea with an adaptive measurement shot strategy and suffix averaging techniques to achieve efficient optimization while reducing the effects of statistical and hardware noise. Numerical simulations show that SGLBO augmented with these techniques can significantly reduce the required number of measurement shots and the robustness against hardware noise compared to other state-of-the-art optimizers in representative tasks of VQAs. |
Monday, March 14, 2022 10:24AM - 10:36AM |
A40.00011: Optimization of a virtual two-qubit gate Akhil P Singh, Kosuke Mitarai, Yasunari Suzuki, Kentaro Heya, Yutaka Tabuchi, Keisuke Fujii, Yasunobu Nakamura Noisy intermediate scale quantum (NISQ) devices seem to be on a promising path to realize the capabilities of quantum computers which could prove to be more fast, secure, and efficient than their classical counterparts. However, presently NISQ devices are with their two biggest challenges: scalability and limited coherence times, still proving to be the testbeds of many promising quantum algorithms. One of the techniques addressing the former challenge in certain situations was proposed by [1], which constructs a general two-qubit gate from only single-qubit operations or referred here as a "virtual two-qubit" gate. This virtual two-qubit gate allows us to, for example, simulate a quantum circuit of 2N qubits by using only N physical qubits with sampling overhead when the goal of the quantum circuit is expectation-value estimation. Hence, it enables us to expand the computing capabilities of NISQ devices in certain algorithms. Here, we present the construction of a "virtual" controlled-NOT (CNOT) gate, an essential component in the construction of gate-based quantum computers. We construct the process matrix for the "virtual CNOT" gate and compare it with the ideal matrix. Further, we also optimize the proposed theoretical decomposition of the virtual two-qubit gates taking into account the imperfections of local operations arising from the real implementation. This technique helps us to obtain expectation values in "scaled-up" quantum circuits, which are used in many quantum algorithms such as variational quantum eigensolver and others. |
Monday, March 14, 2022 10:36AM - 10:48AM |
A40.00012: Quantum average-case distances Filip B Maciejewski, Michał Oszmaniec, Zbigniew Puchała The commonly used distance measures, such as trace distance or diamond norm, quantify the maximal statistical distinguishability of protocols utilizing objects of interest. Here we propose new measures of distance that quantify average-case statistical distinguishability via random quantum circuits. |
Monday, March 14, 2022 10:48AM - 11:00AM |
A40.00013: Obtaining effective noisy systems by simulating noiseless systems on NISQ hardware. Nicolas F Vogt, Keith R Fratus, Juha Leppäkangas, Jan-Michael Reiner, Sebastian Zanker, Michael Marthaler Quantum Computers promise to revolutionize the simulation of large quantum systems. In the NISQ era of quantum computing, the simulation of "perfect" coherent model systems without noise or disorder is hindered by errors introduced in the simulation by the imperfection of the NISQ device. Typically these errors can either be mitigated with error mitigation schemes or simulating the system must wait for the availability of fully error corrected quantum computers. |
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