Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session R09: Algorithms and Architecture for Quantum Information II |
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Sponsoring Units: DQI Chair: Masoud Mohseni, Google Inc. Room: 106 |
Thursday, March 5, 2020 8:00AM - 8:12AM |
R09.00001: Randomized benchmarking for qudit Clifford gates Barry Sanders, Mahnaz Jafarzadeh, Yadong Wu We introduce unitary-gate randomized benchmarking (URB) for qudit gates by extending single- and multi-qubit URB to single- and multi-qudit gates. Specifically, we develop a qudit URB procedure that exploits unitary 2-designs. Furthermore, we show that our URB procedure is not simply extracted from the multi-qubit case by equating qudit URB to URB of the symmetric multi-qubit subspace. Our qudit URB is elucidated by using pseudocode, which facilitates incorporating into benchmarking applications. |
Thursday, March 5, 2020 8:12AM - 8:24AM |
R09.00002: Quantum-Computing Architecture based on Large-Scale Multi-Dimensional Continuous-Variable Cluster States in a Scalable Photonic Platform Bo-Han Wu, Rafael Alexander, Zheshen Zhang Quantum computing is a disruptive paradigm widely believed to be capable of solving classically intractable problems, but the route toward full-scale quantum computers is impeded by immense challenges associated with the scalability of the platform and the required fidelity of various components. One-way quantum computing is an appealing approach that shifts the burden from high-fidelity quantum gates and quantum memories to the generation of high-quality entangled resource states and high fidelity measurements. Here, we bridge two fields—Kerr microcombs and continuous-variable (CV) quantum information—to formulate a one-way quantum computing architecture based on programmable large-scale CV cluster states. The architecture accommodate hundreds of simultaneously addressable entangled optical modes multiplexed in frequency and an unlimited number of sequentially addressable entangled modes in time. One-, two-, three-dimensional CV cluster states can be deterministically produced, which allows for fault-tolerant one-way quantum computing with known error-correction strategies. This architecture opens a promising avenue for quantum computing at a large scale. |
Thursday, March 5, 2020 8:24AM - 8:36AM |
R09.00003: Proposed Universal Deutsch Gate Circuitry Using GaAs/InAs Quantum Dots Paul Bailey, Jean-Francois Van Huele The Deutsch gate is a universal quantum logic gate, meaning that any quantum computing task can be completed using a combination of Deutsch gates. Although a proposal exists to build a Deutsch gate using Rydberg atoms [X-F Shi, Phys. Rev. Applied 9, 051001], to our knowledge no Deutsch gate has been experimentally realized so far. We propose to combine two GaAs/InAs quantum dots with photon polarization in a larger circuit comprised of linear optical elements to create a spin-spin-photon polarization three qubit Deutsch Gate similar to a proposal for a CNOT gate [Bouwmeester et al., Phys. Rev. Lett. 104, 160503]. We display the circuit and discuss the intricacies of designing this gate and reflect on the general feasibility of gate design. |
Thursday, March 5, 2020 8:36AM - 8:48AM |
R09.00004: Quantum-classical transition in analog quantum supremacy subject to Markovian decoherence Razieh Mohseninia, Milad Marvian, Daniel A Lidar Instantaneous Quantum Polynomial time circuits are a promising way to demonstrate quantum supremacy. We study the robustness of quantum supremacy in an analog Hamiltonian version of such circuits in the presence of a Markovian environment whose noise operators commute with the system Hamiltonian. We find a transition from a regime of quantum supremacy to classical simulability that occurs at a finite critical decoherence rate, that depends on the system size. |
Thursday, March 5, 2020 8:48AM - 9:00AM |
R09.00005: Optimal recognition of exact free-fermion solutions for spin models Adrian Chapman, Steven Flammia Finding exact solutions to spin models is a fundamental problem of many-body physics. A workhorse technique for many such exact solution methods is mapping to an effective description by noninteracting particles. The paradigmatic example of this method is the exact solution for the one dimensional XY model by mapping to free-fermions via the Jordan-Wigner transformation. We connect the general problem of recognizing models which can be exactly solved in this way to the graph-theoretic problem of recognizing line graphs, which has been solved optimally. Our solution method captures an entire class of spin models which are as exactly solvable as the Kitaev honeycomb model. We give an example of a previously unsolved spin model and demonstrate its exact solution by our method. We close by showing how these techniques can be used to design new fermion-to-qubit mappings. |
Thursday, March 5, 2020 9:00AM - 9:12AM |
R09.00006: Quantum Algorithms for Approximate Dynamic Programming Pooya Ronagh We present a quantum algorithm for finding approximate solutions to dynamic programming problems using the multiplicative weight update method. Up to polylogarithmic factors, our algorithm provides a quadratic quantum advantage in terms of the number of states of a given dynamic programming problem, at the expense of the appearance of other polynomial factors representative of the number of actions of the dynamic programming problem, the maximum value of the instantaneous reward, and the time horizon of the problem. We also prove lower bounds for the query complexity of quantum algorithms and classical randomized algorithms for solving dynamic programming problems, and show that no greater-than-quadratic speedup in either the number of states or number of actions can be achieved for solving dynamic programming problems using quantum algorithms. |
Thursday, March 5, 2020 9:12AM - 9:24AM |
R09.00007: A Novel Tensor Network Algorithm for Simulating Large Quantum Circuits Justin Reyes, Lei Zhang, Stefanos Kourtis, Claudio Chamon, Andrei E Ruckenstein, Eduardo R Mucciolo Simulations of quantum circuits often involve an exponential number of |
Thursday, March 5, 2020 9:24AM - 9:36AM |
R09.00008: Gate(s)-wise Optimization for Variational Quantum Eigensolvers Lucas Slattery, Benjamin Villalonga, Bryan Clark The variational quantum eigensolver (VQE) is a promising hybrid framework for solving chemistry and physics problems on noisy intermediate-scale quantum (NISQ) computers of the near future. VQE uses a NISQ device to prepare classically intractable parameterized quantum states. These states include ground states of chemistry and condensed matter models, as well as the solution to other optimization problems. A VQE method combines sampling from the NISQ device with a classical optimization routine to find target states. To optimize, most VQE algorithms use quantum circuits to measure gradients of a cost function in order to perform a gradient descent step. We provide and benchmark an alternative to gradient descent VQE methods with the potential for avoiding local minima and faster convergence. We demonstrate proof-of-concept results for local hamiltonians by optimizing one gate at a time. |
Thursday, March 5, 2020 9:36AM - 9:48AM |
R09.00009: The resilience of quantum random access memory to generic noise Connor Hann, Gideon Lee, Steven Girvin, Liang Jiang Quantum random access memory (qRAM)—a memory which stores classical data but allows queries to be performed in superposition—is required for the implementation of numerous quantum algorithms. While naive implementations of qRAM are highly susceptible to decoherence and thus not scalable, it has been argued that the bucket brigade qRAM scheme [Phys. Rev. Lett. 100 160501 (2008)] possesses a remarkable resilience to noise. In this scheme, the infidelity of a memory call scales only logarithmically with the size of the memory. In prior analyses of the scheme, however, this favorable scaling followed directly from the use of restricted noise models, thus leaving open the question of whether experimental implementations would actually enjoy the purported scaling advantage. We prove that, quite surprisingly, the favorable scaling holds for general noise models (including depolarization) and hence should be achievable in realistically noisy devices. As a corollary, we show that the benefits of the bucket-brigade scheme persist even when quantum error correction is used, in which case the scheme offers improved resilience to logical errors and better hardware efficiency. |
Thursday, March 5, 2020 9:48AM - 10:00AM |
R09.00010: QuSP: The Quantum Simulator Package Matthew Jones, Lincoln Carr With the advent and recent popularity of quantum computing (QC) architectural design, there has never been greater focus on quantum simulators and how they might inform the associated design decisions. There are many libraries and tools that hope to bridge the gap between optimal design patterns and the intuition that informs those patterns, but there are very few platforms that streamline this iterative process. With the guidance of the Science Gateways Community Institute, and backed by OpenMPS, a highly generalizable matrix product state simulation toolkit, we will address the aforementioned gap with QuSP, the Quantum Simulator Package. By building a clean, hosted graphical interface for performing quantum simulator simulations, leveraging both the high performance computing resources of the Colorado School of Mines and XSEDE, we will bridge this gap. QuSP provides a guided user experience to defining a system, its evolution, and any metrics a user might want to track through that evolution. |
Thursday, March 5, 2020 10:00AM - 10:12AM |
R09.00011: Cost of Classical Strong Simulation of the T-Gate Magic State Lucas Kocia, Mohan Sarovar The stabilizer rank of qubit T-gate magic state has been postulated to grow slowest with increasing number of qubits, suggesting that the T-gate is in this sense the most efficient state outside the Clifford subtheory that can be simulated by classical strong simulation that nevertheless extends this subtheory to quantum universality. Unfortunately, the T-gate magic state’s stabilizer rank scaling is not formally known and has only been found numerically for up to seven qubits. We examine this problem from the perspective of the cost of strong classical simulation of discrete Wigner functions in systems with odd dimension and compare with the known results for qubits. To accomplish this, we develop and exploit relationships between the number of critical points of quantum states’ Wigner functions in a periodized stationary phase approximation and spanning decompositions of stabilizer states that are closely related to the stabilizer rank. We report on the trends we observe. |
Thursday, March 5, 2020 10:12AM - 10:24AM |
R09.00012: Alibaba Cloud Quantum Development Platform: Large-Scale Classical Simulation of Quantum Circuits Fang Zhang, Cupjin Huang, Michael Newman, Junjie Cai, Huanjun Yu, Zhengxiong Tian, Bo Yuan, Haihong Xu, Junyin Wu, Xun Gao, Jianxin Chen, Mario Szegedy, Yaoyun Shi We report our work on the Alibaba Cloud Quantum Development Platform(AC-QDP). AC-QDP provides a set of tools for aiding the development of both quantum computing algorithms and quantum processors, and is powered by a large-scale classical simulator deployed on Alibaba Cloud. In this note, we report the computational experiments demonstrating the classical simulation capability of AC-QDP. We use as a benchmark the random quantum circuits designed for Google's Bristlecone QPU {\cite{GRCS}}. We simulate Bristlecone-70 circuits with depth 1+32+1 in 0.43 second per amplitude, using 1449 Alibaba Cloud Elastic Computing Service (ECS) instances, each with 88 Intel Xeon(Skylake) Platinum 8163 vCPU cores @ 2.5 GHz and 160 gigabytes of memory. By comparison, the previously best reported results for the same tasks are 104 and 135 seconds, using NASA's HPC Pleiades and Electra systems, respectively ({arXiv:1811.09599}). Furthermore, we report simulations of Bristlecone-70 with depth 1+36+1 and depth 1+40+1 in 5.6 and 580.7 seconds per amplitude, respectively. To the best of our knowledge, these are the first successful simulations of instances at these depths. |
Thursday, March 5, 2020 10:24AM - 10:36AM |
R09.00013: Alibaba Cloud Quantum Development Platform: Applications to Quantum Algorithm Design Cupjin Huang, Mario Szegedy, Fang Zhang, Jianxin Chen, Xun Gao, Yaoyun Shi
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Thursday, March 5, 2020 10:36AM - 10:48AM |
R09.00014: Quantum linear system solver based on time-optimal adiabatic quantum computing and quantum approximate optimization algorithm Dong An, Lin Lin The condition number dependence is a crucial aspect of quantum linear system solvers. We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can solve a quantum linear system problem with O(κ/ε) runtime, where κ is the condition number, and ε is the target accuracy. This achieves at least quadratic speedup over the standard vanilla AQC and reaches the complexity lower bound with respect to κ. The success of the time-optimal AQC implies that the quantum approximate optimization algorithm (QAOA) can also achieve the O(κ) complexity with respect to κ. Our method is applicable to general non-Hermitian matrices (possibly dense), but the efficiency can be improved when restricted to Hermitian matrices, and further to Hermitian positive definite matrices. Numerical results indicate that QAOA can yield the lowest runtime compared to the time-optimal AQC, vanilla AQC, and the recently proposed randomization method. The runtime of QAOA is observed numerically to be only O(κ*poly(log(1/ε))). |
Thursday, March 5, 2020 10:48AM - 11:00AM |
R09.00015: Quantum optimal algorithm for general polynomials Keren Li, Pan Gao Gradient descent algorithms, which is a first-order iterative algorithm, is widely used for optimization problems. To obtain the local minimum of a objective function, one caculates the gradients at the current point in a $d$-dimension space and then move the function along the negative gradient. This procedure requires $O(poly(d))$ calculation operations, which hinders the performance of the algorithm in a high dimensional optimization situation. Here, by learning a framework of a quantum iterative optimization algorithm and introducing an adapted encoding scheme, we develop the quantum gradient operation for an in-homogenous polynomial objective function with a unit norm constraint. Under certain circumstances, the calculation operations required for the algorithm decreases and is linear with the $O(poly(log(d)))$. Besides, we experimentally demonstrate it on a 4-qubit Nuclear Magnetic Resonance(NMR) quantum system and show the feasibility in polynomial optimization. Since the potential value in high dimensional optimization problems, this algorithm is supposed to influence the interdisciplinary research including Quantum Information and Artificial Intelligence, such as Support Vector Machine, Logistic regression, (quantum) Neural network |
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