Jan 25 – 29, 2019
US/Pacific timezone

Faster classical sampling from distributions defined by quantum circuits

Jan 26, 2019, 11:30 AM
Building 66- Auditorium (LBL-Hill)

Building 66- Auditorium


Lawrence Berkeley National Lab Berkeley, California
Foundations of quantum computing Foundations of Quantum Computing


Igor Markov (University of Michigan)


The leading candidate task for benchmarking quantum computers against classical computers entails sampling from the output distribution defined by a random quantum circuit. We develop a massively-parallel simulation package that does not require inter-process communication (IPC) or proprietary hardware. We introduce two ways to trade circuit fidelity for computational speedups, so as to match the fidelity of a given quantum computer. Our software achieves massive speedups for the sampling task over prior software from Microsoft, IBM, Alibaba and Google, as well as supercomputer and GPU-based simulations. By using publicly available Google Cloud Computing, we price such simulations and enable comparisons by total cost across hardware platforms. We simulate approximate sampling from the output of a circuit with $7\times 8$ qubits and depth 1+40+1 by producing one million bitstring probabilities with fidelity 0.5 percent, at an estimated cost of USD 35184. Simulating circuits of depth to 1+48+1 would cost one million dollars.

Primary author

Igor Markov (University of Michigan)


Ms Aneeqa Fatima (Univ. of Michigan) Dr Sergei Isakov (Google Zurich) Dr Sergio Boixo (Google)

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