Logical Abstractions for Noisy Variational Quantum Algorithm Simulation

TL;DR

This paper introduces a logical abstraction-based quantum circuit simulation toolchain optimized for variational algorithms, effectively handling noise, repeated parameterized circuits, and sampling, outperforming existing simulators especially for near-term quantum devices.

Contribution

The paper presents a novel simulation framework that encodes quantum amplitudes and noise in a probabilistic graphical model, enabling efficient repeated simulation and sampling for variational quantum algorithms.

Findings

Outperforms state-of-the-art simulators for noisy circuits with 8-20 qubits.

Achieves 66x reduction in sampling cost for noise-free shallow circuits with 32 qubits.

Efficiently supports repeated simulations with different parameters.

Abstract

Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms, which are among the leading candidates for useful quantum computation. Existing quantum circuit simulators do not address the common traits of variational algorithms, namely: 1) their ability to work with noisy qubits and operations, 2) their repeated execution of the same circuits but with different parameters, and 3) the fact that they sample from circuit final wavefunctions to drive a classical optimization routine. We present a quantum circuit simulation toolchain based on logical abstractions targeted for simulating variational algorithms. Our proposed toolchain encodes quantum amplitudes and noise probabilities in a probabilistic graphical model, and it compiles the circuits to logical formulas that support efficient repeated simulation of and sampling from quantum circuits for different parameters. Compared to state-of-the-art state vector and density matrix quantum circuit simulators, our simulation approach offers greater performance when sampling from noisy circuits with at least eight to 20 qubits and with around 12 operations on each qubit, making the approach ideal for simulating near-term variational quantum algorithms. And for simulating noise-free shallow quantum circuits with 32 qubits, our simulation approach offers a $66\times$ reduction in sampling cost versus quantum circuit simulation techniques based on tensor network contraction.