Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Q36: Quantum Software and Compilers: Simulation and ControlFocus Recordings Available
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Sponsoring Units: DQI Chair: Emery Doucet, University of Massachusetts-Lowell Room: McCormick Place W-194A |
Wednesday, March 16, 2022 3:00PM - 3:12PM |
Q36.00001: Parallel Tensor Network Simulator QTensor Danylo Lykov, Angela Chen, Huaxuan Chen, Kristopher Keipert, Zheng Zhang, Tom Gibbs, Yuri Alexeev We present a parallel quantum circuit simulator QTensor* for running on large supercomputers with the eventual goal to run at scale on exa-scale supercomputers Aurora and Frontier. The simulator is based on the tensor network representation of quantum circuits and designed to run efficiently on both CPUs and GPUs. We implemented NumPy, PyTorch, and CuPy backends and benchmarked the codes to find the optimal allocation of tensor simulations to either a CPU or a GPU. We also present a dynamic mixed backend to achieve optimal performance. To demonstrate the performance, we simulate QAOA circuits for computing the MaxCut energy expectation. Our method achieves 176 times speedup on a GPU over the NumPy baseline on a CPU for the benchmarked QAOA circuits to solve MaxCut problem on a 3-regular graph of size 30 with depth p=4. |
Wednesday, March 16, 2022 3:12PM - 3:24PM |
Q36.00002: cuQuantum: Accelerating Quantum Circuit Simulation on GPUs Sam Stanwyck, Harun Bayraktar, Tim Costa Quantum circuit simulation plays an important role in quantum computing, as it enables algorithm research at a scale and performance level not possible on today's quantum computers. We present cuQuantum, a set of libraries to accelerate quantum circuit simulations on GPUs. cuQuantum contains libraries for state vector and tensor network simulation, and has already been integrated as a backend into several leading frameworks. We benchmark cuQuantum against current state-of-the-art CPU and GPU-based simulation solutions. Using cuQuantum on NVIDIA's Selene supercomputer, we are also able to simulate a variational quantum algorithm circuit to solve a MaxCut problem with thousands of qubits, an order of magnitude increase over the previous largest solution. We expect and hope that cuQuantum will open the door for novel algorithms research, at scales not previously possible. |
Wednesday, March 16, 2022 3:24PM - 3:36PM |
Q36.00003: HybridQ: A Hybrid Simulator for Quantum Circuits Salvatore Mandra, Jeffrey S Marshall, Eleanor G Rieffel, Rupak Biswas Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. To support a unified and optimized use of multiple techniques across platforms, we developed HybridQ, a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The powerful tools developed in HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques. |
Wednesday, March 16, 2022 3:36PM - 4:12PM |
Q36.00004: Enabling Deeper Quantum Compiler Optimizations at High Level Invited Speaker: Gushu Li A quantum compiler is one essential and critical component in a quantum computing system to deploy and optimize the quantum programs onto the underlying physical quantum hardware platforms. Yet, today’s quantum compilers are still far from optimal. One reason is that most optimizations in today’s quantum compilers are local program transformations over very few qubits and gates. In general, it is highly non-trivial for a compiler that runs on a classical computer to automatically derive large-scale program optimizations at the gate-level. |
Wednesday, March 16, 2022 4:12PM - 4:24PM |
Q36.00005: Modular Multi-formalism Mixed-signal Simulator of Quantum Hardware Stefan Krastanov, Dirk Englund There is an unmet need for multi-physics modeling of quantum information processors at the system scale, quantitatively coupled to experimental measurements of subsystem components. Similarly to mixed-signal classical electronics simulators, we need to address the "analog" dynamics of noise (e.g., a Lindblad master equation), while simultaneously modeling the discrete digital infrastructure built on top of the low level analog model. Tools derived from both SPICE and Verilog do so in the classical domain. We showcase a tool addressing many of the problems faced by such mixed-signal simulations in the quantum domain. To limit the exponential cost of low-level quantum simulations, we introduce a multi-formalism "quantum state allocator" and "garbage collector" that ensures the simulation tracks only the minimally sized Hilbert space of interest. The allocator is agnostic to the formalism used, permitting on-the-fly transitions between Lindblad Master Equation, Noisy Stabilizer Tableaux, and other models, depending on how fast the problem grows. The analog simulator is integrated with a discrete event simulator, permitting easy simulation of quantum circuits and quantum networks with arbitrary classical feedback. We demonstrate a case study where we design and optimize a small color-center-based cluster-state quantum computer. |
Wednesday, March 16, 2022 4:24PM - 4:36PM |
Q36.00006: Quantum processor crosstalk mitigation using QubiC controller aided by NERSC HPC Jan Balewski, Gang Huang, Adam Winick, Yilun Xu, David I Santiago, Irfan Siddiqi, Ravi K Naik
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Wednesday, March 16, 2022 4:36PM - 4:48PM |
Q36.00007: TimeStitch: Exploiting Slack to Mitigate Decoherence in Quantum Circuits Kaitlin N Smith, Gokul Subramanian Ravi, Prakash Murali, Jonathan M Baker, Nathan Earnest, Ali Javadi-Abhari, Frederic T Chong
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Wednesday, March 16, 2022 4:48PM - 5:00PM |
Q36.00008: Using classical logic to design quantum circuits for compression of quantum data Abhinav Anand, Jakob Kottmann, Alán Aspuru-Guzik The use of near term quantum devices for compression of information is an exciting prospect which can enable the use of quantum resources for complex tasks. To this end, different compression algorithms, including the quantum autoencoder, have been proposed. These algorithms rely on trained parameterized quantum circuits to perform the compression. The success of the training depends on the structure of the employed circuit, whose design can be difficult to generalize. In this work we propose a novel strategy to design quantum circuits using an evolutionary algorithm, with a restricted gate set based on classical logic operations. The use of the limited gate set enables efficient simulation of the quantum circuit. We show initial applications for compression of different family of states, including single particle states, two particle states, random states, prime states, among others. This opens a new path for using near term quantum devices for compressing quantum data and facilitating efficient quantum simulations for various tasks. |
Wednesday, March 16, 2022 5:00PM - 5:12PM |
Q36.00009: QCover: QAOA-based Conbinational Optimization Solver Mike Hu We have entered the era of Noisy Intermedia-scale Quantum (NISQ), that is, quantum chips can contain tens to hundreds of qubits, but the large error rate of quantum logic gate operations affected by noise results in extremely limited circuit depth that can be effectively executed. Demonstrate quantum advantages in certain applications has become the most urgent thing at present. |
Wednesday, March 16, 2022 5:12PM - 5:24PM |
Q36.00010: Quantify-scheduler: An open-source hybrid compiler for operating quantum computers in the NISQ era Damien Crielaard, Damaz De Jong, Jordy Gloudemans, Rahul Vyas, Victor Negirneac, Diogo Valada, Calin Sindile, Callum Attryde, Adam Lawrence, Thomas Reynders, Viacheslav Ostroukh, Kelvin Loh, Michiel Adriaan Rol, Cornelis Christiaan Bultink Operating a quantum computer in the NISQ era is a daunting challenge. Abstraction is needed to manage the increasing complexity of control parameters, measured signals, and classical logic at the hardware level. However, this typically also reduces the number of available operations by only allowing expressing experiments as either a series of classical pulses or variants of QASM. To overcome this difficulty, we present the Quantify-scheduler*, a robust and extensively-documented open-source python package that translates high-level instructions to low-level hardware-executable code. It features a hybrid gate/pulse control model with explicit timing control for writing quantum programs with parameterized expressions. The control model allows combining quantum gate- and pulse-level descriptions in a hardware-agnostic way, currently exemplified by supporting two control hardware platforms from different suppliers. User-friendly visualization and debugging tools are provided for both QASM and high-level pulse expressions as well as at the level of simulated analog signals at the quantum chip. This opens up new avenues for efficient execution of calibration routines as well as quantum algorithms. |
Wednesday, March 16, 2022 5:24PM - 5:36PM |
Q36.00011: Aggregated Control of Quantum Computations: When Stacked Architectures Are Too Good to Be Practical Soon Alexandru Paler Google’s quantum supremacy demonstration pitted the world’s largest supercomputer against a single quantum chip. A path to further scale up may involve a large supercomputer working together with quantum chips, instead of in competition with them. The debate about universal large-scale quantum computers never being built is not a novelty. If the hardware qubits were (almost) perfect, available quantum machines would be practically useful. But this is not the case: qubits are error-prone. Quantum error-correction has to be implemented, and millions of hardware qubits are necessary to execute quantum computations of practical interest. The main concern is that the control of quantum computers, comprising of millions of qubits, is practically impossible for scalability reasons. |
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