Simulation of quantum algorithms using classical probabilistic bits and circuits

TL;DR

This paper presents a classical probabilistic simulation method for quantum algorithms by mapping qubits to an 8-dimensional probability space, enabling the simulation of quantum gates and algorithms like Deutsch-Jozsa and Quantum Fourier Transform.

Contribution

It introduces a novel non-linear, affine mapping of quantum states to classical probability spaces, allowing efficient simulation of quantum algorithms with polynomial resources.

Findings

Simulation of Deutsch-Jozsa algorithm demonstrated

Quantum Fourier Transform implemented in classical probability space

Simulation requires polynomial resources, O(n) bits and O(n^2) operations

Abstract

We discuss a new approach to simulate quantum algorithms using classical probabilistic bits and circuits. Each qubit (a two-level quantum system) is initially mapped to a vector in an eight dimensional probability space (equivalently, to a classical random variable with eight probabilistic outcomes). The key idea in this mapping is to store both the amplitude and phase information of the complex coefficients that describe the qubit state in the probabilities. Due to the identical tensor product structure of combining multiple quantum systems as well as multiple probability spaces, $n$ qubits are then mapped to a tensor product of $n$ 8-dimensional probabilistic vectors (i.e., the Hilbert space of dimension $2^n$ is mapped to a probability space of dimension $8^n$). After this initial mapping, we show how to implement the analogs of single-qubit and two-qubit gates in the probability space using correlation-inducing operations on these classical random variables. The key defining feature of both the mapping to the probability space and the transformations in this space (i.e., operations on the random variables) is that they are not linear, but instead affine. Using this architecture, the evolution of the $2^n$ complex coefficients of the quantum system can be tracked in the joint fully-correlated probabilities of the polynomial number of random variables. We then give specific procedures for implementing (1) the Deutsch-Jozsa algorithm, and (2) the Quantum Fourier Transform in the probability space. Identical to the Quantum case, simulating the Quantum Fourier Transform in the probability space requires $O(n)$ probabilistic bits and $O(n^2)$ (i.e., quadratic in the number of quantum bits) operations.