Abstract
Here we present qFlex, a flexible tensor network-based quantum circuit simulator. qFlex can compute both the exact amplitudes, essential for the verification of the quantum hardware, as well as low-fidelity amplitudes, to mimic sampling from Noisy Intermediate-Scale Quantum (NISQ) devices. In this work, we focus on random quantum circuits (RQCs) in the range of sizes expected for supremacy experiments. Fidelity f simulations are performed at a cost that is 1/f lower than perfect fidelity ones. We also present a technique to eliminate the overhead introduced by rejection sampling in most tensor network approaches. We benchmark the simulation of square lattices and Google’s Bristlecone QPU. Our analysis is supported by extensive simulations on NASA HPC clusters Pleiades and Electra. For our most computationally demanding simulation, the two clusters combined reached a peak of 20 Peta Floating Point Operations per Second (PFLOPS) (single precision), i.e., 64% of their maximum achievable performance, which represents the largest numerical computation in terms of sustained FLOPs and the number of nodes utilized ever run on NASA HPC clusters. Finally, we introduce a novel multithreaded, cache-efficient tensor index permutation algorithm of general application.
Highlights
Building a universal, noise-resistant quantum computer is to date a long-term goal driven by the strong evidence that such a machine will provide large amounts of computational power, beyond classical capabilities.[1,2,3,4,5,6,7,8,9] An imminent milestone in that direction is represented by Noisy Intermediate-Scale Quantum (NISQ) devices[10] of about 50–100 qubits
By expanding on a technique introduced in ref. 28, including introducing fine-grained “cuts” that enable us to judiciously balance memory requirements with the number of independent computations that can be done in parallel, our simulator can output 1/f amplitudes with a target fidelity f at the same computational cost to compute a single perfect-fidelity amplitude; we present an alternative technique to simulate random quantum circuits (RQCs) sampling with target fidelity f with the generate RQCs
In “Fast sampling of bit-strings from low delity RQCs”, we discuss two methods to classically sample from an RQC mimicking the fidelity f of the output of a real device, while achieving a speedup in performance of a factor of 1/f; in addition, we present a method to speed up the classical sampling by a factor of about 10× that, under reasonable assumptions, is well suited to tensor network-based simulators
Summary
Noise-resistant quantum computer is to date a long-term goal driven by the strong evidence that such a machine will provide large amounts of computational power, beyond classical capabilities.[1,2,3,4,5,6,7,8,9] An imminent milestone in that direction is represented by Noisy Intermediate-Scale Quantum (NISQ) devices[10] of about 50–100 qubits. As part of their study, the authors the two systems combined reached a peak of 20 PFLOPS (single propose for the first time a technique to “match” the target fidelity precision), i.e., 64% of their maximum achievable performance, f of the NISQ device, which reduces the classical computation cost by a factor f. As we show here, the Google Bristlecone QPU implements circuit topologies with a large diameter, which increases the run time exponentially In both cases, one could mitigate the memory run on the quantum computer as well as to compute perfect fidelity amplitudes for the verification of the experiments—and discussion are presented in their respective sections. The hardness of the revised RQCs motivates, in part, our simulator’s approach, which is explained in “Overview of the simulator,” where both conceptual and implementation details are discussed; here we introduce a novel, cache-efficient npj Quantum Information (2019) 86
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
