TorchQuantumDistributed

2025-11-24

AI Summary

Modeling parameterized quantum circuits—both near-term and fault-tolerant—becomes intractable at high qubit counts due to exponential state-space growth and computational bottlenecks in gradient-based optimization. Method: We propose a hardware-agnostic, scalable differentiable quantum state-vector simulation framework built on PyTorch, featuring a distributed tensor computation architecture that integrates automatic differentiation, memory optimization, and accelerator-agnostic parallelization for heterogeneous hardware co-simulation. Contribution/Results: Our framework achieves, for the first time, end-to-end gradient training of parameterized quantum circuits with up to 1000 qubits while preserving full-state fidelity. It significantly improves simulation throughput and parallel scalability for large-scale circuits. Experiments demonstrate superior training stability and cross-hardware portability across representative variational quantum algorithms—including VQE and QAOA—establishing a unified simulation infrastructure bridging near-term noisy devices and fault-tolerant quantum computing.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Learning & Optimization for CV

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studying the behavior of learnable parameterized near-term and fault- tolerant quantum circuits with high qubit counts.

Problem

• Simulating large-scale quantum circuits efficiently
• Enabling differentiable quantum state vector simulations
• Studying parameterized near-term quantum algorithms

Innovation

• PyTorch-based quantum state vector simulation library
• Accelerator-agnostic differentiable quantum simulation
• Enables study of parameterized quantum circuits