NFNet: Non-interacting Fermion Network for Efficient Simulation of Large-scale Quantum Systems

TL;DR

NFNet is a PyTorch-based framework that efficiently simulates large-scale, continuously controlled quantum systems using non-interacting Fermionic formalism, enabling scalable quantum system analysis and learning tasks.

Contribution

Introduces NFNet, a novel software framework that extends matchgate-based Fermionic simulation to arbitrary two-Fermion interactions, supporting large-scale quantum system simulation.

Findings

Supports simulation of 512+ qubits

Enables quantum system benchmarking and learning tasks

Links quantum simulation to neural network concepts

Abstract

We present NFNet, a PyTorch-based framework for polynomial-time simulation of large-scale, continuously controlled quantum systems, supporting parallel matrix computation and auto-differentiation of network parameters. It is based on the non-interacting Fermionic formalism that relates the Matchgates by Valiant to a physical analogy of non-interacting Fermions in one dimension as introduced by Terhal and DiVincenzo. Given an input bit string $\boldsymbol{x}$, NFNet computes the probability $p(\boldsymbol{y}|\boldsymbol{x})=\langle x|U_{\theta}^\dagger \Pi_y U_\theta |x\rangle$ of observing the bit string $\boldsymbol{y}$, which can be a sub or full-system measurement on the evolved quantum state $U_{\mathbf{\theta}}|x\rangle$, where $\mathbf{\theta}$ is the set of continuous rotation parameters, and the unitary $U_{\mathbf{\theta}}$'s underlying Hamiltonians are not restricted to nearest-neighbor interactions. We first review the mathematical formulation of the Matchgate to Fermionic mapping with additional matrix decomposition derivations, and then show that on top of the pair-wise circuit gates documented in Terhal and DiVincenzo, the Fermionic formalism can also simulate evolutions whose Hamiltonians are sums of arbitrary two-Fermion-mode interactions. We then document the design philosophy of NFNet, its software structure, and demonstrate its usage in various quantum system simulation, benchmarking, and quantum learning tasks involving 512+ qubits. As NFNet is both an efficient large-scale quantum simulator, and a quantum-inspired classical computing network structure, many more exciting topics are worth exploring, such as its connection to recurrent neural networks, discrete generative learning and discrete normalizing flow. NFNet source code can be found at https://github.com/BILLYZZ/NFNet.