reminder that this talk is starting in a little under an hour.
On Tue, Sep 17, 2019 at 10:49 AM Aram Harrow <aram(a)mit.edu> wrote:
This will be in 6-310 and we will stream it from
Chicago. This is part of
our EPIQC collaboration but anyone is welcome to attend.
title: Bayesian Network Knowledge Compilation for Quantum Circuit
Simulation
speaker: Yipeng Huang
Abstract: The problem of simulating quantum circuits that appear in
variational algorithms offers distinct opportunities, when compared to
simulating other types of quantum circuits. We focus on exploiting three
characteristics common in variational algorithm quantum circuits. These
properties are 1. circuits in variational algorithms such as VQE and QAOA
are often wide but shallow, 2. the algorithms run circuits with the same
topology many times, with only differences in gate parameters between runs,
3. the variational algorithm expects the quantum computer or simulator to
return samples from a multi-modal wavefunction, and not the full
high-dimensional wavefunction. These traits make this type of circuit
simulation a distinctive workload compared to simulating Shor's, Grover's,
or even random circuit sampling workloads.
In this ongoing work, we use knowledge compilation on Bayesian networks to
aid simulating variational quantum algorithms. Knowledge compilation is a
set of techniques we borrow from classical AI inference, which had been
useful for repeated inference on AI models with different parameters, and
for efficient stochastic simulation of those inference models. Those tricks
may be useful here for quantum circuit simulation.
The toolchain begins by converting quantum circuits to complex-valued
Bayesian networks. This transformation has been established by prior work
that use probabilistic graphical models and tensor network contraction for
quantum circuit simulation. From this transformation, our work strikes out
on a new pathway to do simulation. We convert the Bayesian networks to
conjunctive normal forms, and finally to arithmetic circuits for inference.
These minimized arithmetic circuits can be reused across multiple
simulations with different parameters. Furthermore, the arithmetic circuits
allow for more efficient sampling of measurement outcomes from the final
wavefunction.
We experimentally validate our simulation toolchain using Google Cirq for
its QAOA benchmark, its simulator test harnesses, and its native simulator
as a baseline. We are able to beat the native simulator for QAOA problem
sizes beyond 16 qubits, where the native simulator starts to struggle with
longer wavefunction vectors. Our proposed approach appears to be limited in
terms of depth to about 33 steps, where the size of the compiled topology
becomes a limiting factor. In all, the approach may be useful in simulating
this wide but shallow range of quantum circuits.
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