Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session F34: Quantum Software and Compilers I - Program OptimizationsLive
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Sponsoring Units: DQI Chair: Ali Javadi |
Tuesday, March 16, 2021 11:30AM - 11:42AM Live |
F34.00001: Approximate quantum circuit synthesis using block encodings Daan Camps, Roel Van Beeumen One of the challenges in quantum computing is the synthesis of unitary operators into quantum circuits with polylogarithmic gate complexity. Exact synthesis of generic unitaries requires an exponential number of gates in general. We propose a novel approximate quantum circuit synthesis technique by relaxing the unitary constraints and interchanging them for ancilla qubits via block encodings. This approach combines smaller block encodings, which are easier to synthesize, into quantum circuits for larger operators. Due to the use of block encodings, our technique is not limited to unitary operators and can be applied for the synthesis of arbitrary operators. We show that operators which can be approximated by a canonical polyadic expression with a polylogarithmic number of terms can be synthesized with polylogarithmic gate complexity with respect to the matrix dimension. |
Tuesday, March 16, 2021 11:42AM - 11:54AM Live |
F34.00002: QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space Ed Younis, Wim Lavrijsen, Koushik Sen, Katherine Yelick, Costin Iancu We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice. Our contributions are: 1) a novel representation of circuits able to encode placement and topology; 2) a hierarchical approach with an iterative refinement formulation that combines "coarse-grained" fast optimization during circuit structure search with a good, but slower, optimization stage only in the final circuit instantiation. When compared against state-of-the-art techniques, although not always optimal, QFAST can reduce circuits for "time-dependent evolution" algorithms, as used by domain scientists, by 60x in depth. On typical circuits, it provides 4x better depth reduction than the widely used Qiskit and UniversalQ compilers. We also show the composability and tunability of our formulation in terms of circuit depth and running time. For example, we show how to generate shorter circuits by plugging in the best available third party synthesis algorithm at a given hierarchy level. Composability enables portability across chip architectures, which is missing from similar approaches. |
Tuesday, March 16, 2021 11:54AM - 12:06PM Live |
F34.00003: LEAP: Scaling Numerical Optimization Based Synthesis Using an Incremental Approach Ethan Smith, Marc Davis, Jeffrey Larson, Costin Iancu Synthesis provides a valuable tool for quantum circuit optimization.The best techniques combine numerical optimization with search over structures, but face scalability challenges due to: 1) large number of of parameters; 2) exponential search space; and 3) complex objective function. |
Tuesday, March 16, 2021 12:06PM - 12:18PM Live |
F34.00004: Scalable Quantum Circuit Optimization Using Automated Synthesis Xin-Chuan Wu, Costin Iancu, Fred Chong
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Tuesday, March 16, 2021 12:18PM - 12:30PM Live |
F34.00005: Circuit Optimization for Simulations of Quantum Systems Kaiwen Gui, Teague Tomesh, Pranav Gokhale, Yunong Shi, Margaret Martonosi, Martin Suchara, Fred Chong Simulating the time evolution of a quantum mechanical system is one of the most promising near-term applications of quantum computing, potentially offering an exponential advantage compared to classical simulation. However, as the desired accuracy of these calculations increases, the required quantum resources do not scale favorably. The quantum evolution circuits are represented by tensor products of Pauli matrices obtained from the second quantization form using transformation methods such as Jordan-Wigner or Bravyi-Kitaev. The required number of quantum gates scales as O(N4), thus leading to the accumulation of error due to the presence of physical gate errors. In addition, algorithmic errors due to Trotterization are also present. Prior work focused on reducing either of these error types in isolation. We demonstrate a new technique that simultaneously reduces (i) Trotterization errors by reordering terms in the Hamiltonian, and (ii) gate error accumulation by achieving gate cancellation. We map the problem to a graph-theoretic formulation and use the clique cover and traveling salesperson heuristics to optimize the order of the terms. Our simulations demonstrate at least 5% overall fidelity improvement. |
Tuesday, March 16, 2021 12:30PM - 12:42PM Live |
F34.00006: Automatic Shuttling Sequence Generation for a Linear Ion Trap Computer Jonathan Durandau, Charles-Antoine Brunet, Ulrich Poschinger, Frederic Mailhot, Yves Bérubé-Lauzière
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Tuesday, March 16, 2021 12:42PM - 12:54PM Live |
F34.00007: Discontinuous Galerkin method with Voronoi partitioning for Quantum Simulation of Chemistry Fabian Faulstich, Xiaojie Wu, Lin Lin Molecular orbitals are arguably the most employed discretization in quantum chemistry simulations, both on quantum and classical devices. To circumvent a potentially dense electron repulsion integral tensor (ERI) and obtain lower asymptotic costs for quantum simulations of chemistry, the discontinuous Galerkin (DG) procedure with rectangular partitioning for quasi 1D-systems was recently piloted [1]. The DG approach interpolates in a controllable way between a compact description of the ERI through molecular orbitals and a diagonal characterization through primitive basis sets. Moreover, it gives rise to a block-diagonal representation of the ERI with a reduced number of nonzero terms, which reduces the cost of quantum simulations. |
Tuesday, March 16, 2021 12:54PM - 1:06PM Live |
F34.00008: Minimizing estimation runtime on noisy quantum computers Peter Johnson, Guoming Wang, Dax Enshan Koh, Yudong Cao There is no evidence that NISQ algorithms such as VQE will outperform classical computers for problems of interest. This unfortunate fact urges us to ask: what new insights may be needed to achieve quantum advantage? Towards answering this question we attempt to reconcile two contrasting features of quantum computation: the power of deep-circuit quantum amplification and the reality of imperfect or noisy operating conditions. We present an estimation algorithm tailored to practical implementation by incorporating noise models into its design and optimizing for minimal runtime. We show simulations which demonstrate its advantage over existing techniques. Finally, we demonstrate the long-term impact of this technique for converting device improvement into algorithm performance. As the field of applied quantum computing navigates the evolving hardware, software, and algorithm landscape, these insights may help to find the shortest path to quantum advantage. |
Tuesday, March 16, 2021 1:06PM - 1:42PM Not Participating |
F34.00009: Nomination for invited speaker for DQI: Isaac Chuang Invited Speaker: Isaac Chuang Nominated by Ali Javadi, Raphael Pooser, Krysta Svore for DQI session organized by Michelle Simmons. Ike would discuss the grand unification of quantum algorithms and implications about this for quantum software & architecture. Ike is an expert in both quantum hardware, software, and their interfaces. |
Tuesday, March 16, 2021 1:42PM - 1:54PM Live |
F34.00010: Reinforcement learning for quantum circuit optimization Thomas Foesel, Murphy Yuezhen Niu, Florian Marquardt, Li Li One of the central bottlenecks for quantum computers is noise originating from their inevitable interaction with the environment, which threatens to corrupt the result of the quantum computation. The detrimental effect of noise can be mitigated by quantum circuit optimization, i.e., to search for circuits with less operations and a shorter runtime. In the recent years, powerful approaches [1, 2] have been developed focused on optimizing the global circuit structure. However, these approaches do not consider and thus cannot optimize for hardware specifics of the quantum architecture, which is especially important for near-term applications. To address this point, we propose a novel approach to quantum circuit optimization based on reinforcement learning, a powerful machine-learning technique which has already helped, for example, to achieve super-human performance in the board game Go [3]. |
Tuesday, March 16, 2021 1:54PM - 2:06PM Live |
F34.00011: Towards Constant-Depth Circuits for Dynamic Simulations of Materials on Quantum Computers Lindsay Bassman, Roel Van Beeumen, Ethan Smith, Ed Younis, Wibe A De Jong, Costin Iancu Dynamic simulations of materials are one of the most promising applications for noisy intermediate-scale quantum (NISQ) computers. The difficulty in carrying out such simulations is that a quantum circuit must be executed for each time-step, and these circuits tend to grow in size with increasing time-step. NISQ computers, however, can only produce high-fidelity results for circuits up to a given size due to gate error rates and qubit decoherence times, limiting the duration of simulations that can be performed. Here, we present work towards developing constant-depth circuits for dynamic simulations under special classes of Hamiltonians. Specifically, we show that simulations of one-dimensional, time-dependent, N-spin transverse-field Ising models require quantum circuits with only N(N-1) CNOT gates for all time-steps, providing orders of magnitude depth reduction when compared with previous circuit generation methods. Such constant-depth circuits allow for simulations of arbitrary duration, enabling simulations of long-time dynamics which are often required to observe interesting and important atomic-level events. |
Tuesday, March 16, 2021 2:06PM - 2:18PM On Demand |
F34.00012: Error-robust controls in quantum algorithms Anurag Mishra, Harrison Ball, Michael Hush, Michael Biercuk Current commercial quantum computers are prone to various kinds of noise processes, such as leakage and dephasing, which degrade the performance of quantum algorithms. These errors can be dynamically suppressed by designing quantum controls that are robust to the underlying noise processes. In this talk, we will focus on the impact of using such validated control techniques on the performance of the variational quantum eigensolver (VQE). VQE is a NISQ-era quantum algorithm which has been used successfully to find the ground state energy of small molecules. This particular algorithm is thought to exhibit some inherent resistance to noise; however, it is not clear how such algorithms are impacted by errors which are correlated in space and time across quantum gates. In this talk, we discuss numerical simulation of the impact of common correlated noise processes on these algorithms. We shall demonstrate how optimally designed robust quantum controls can reduce the impact of various noise sources and improve the performance of quantum algorithms on commercial devices. |
Tuesday, March 16, 2021 2:18PM - 2:30PM On Demand |
F34.00013: Optimized quantum solutions for vehicle routing problems Alireza Shabani, Christopher Bentley, Andre Carvalho, Michael Biercuk, Michael Hush Vehicle routing and scheduling are examples of transportation-network operational tasks that can be cast as optimization problems. Solving these problems becomes increasingly challenging with more vehicles or larger networks, as well as when constraints such as vehicle capacity are considered. Existing literature shows that quantum algorithms like QAOA can be used to solve certain transport problems, suggesting that quantum computers may be capable of tackling these applications in the future. However, gate errors and decoherence processes in today’s quantum hardware severely limit the performance of even small-scale demonstrations. We address this issue by constructing algorithms with tailored gates that are robust against typical hardware imperfections. We perform simulations of QAOA for the Mobility as a Service (MaaS) problem with varying network sizes using a pulse-level description of gates and realistic noise models. We probe performance bounds in the presence of different errors including incoherent T1 processes, coherent over-rotation errors, and coherent dephasing, as are common on superconducting quantum computers. Finally, we discuss the results from the standard and robust MaaS QAOA algorithms and compare their performance for each type of error. |
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