Guess, SWAP, Repeat : Capturing Quantum Snapshots in Classical Memory

2025-04-20

AI Summary

This work addresses the challenge of non-destructive observation and reuse of quantum states in quantum circuits, circumventing the “measurement-induced collapse” limitation. We propose a SWAP-test-guided “guess-and-verify” paradigm to realize a hardware-agnostic, programmable quantum snapshot mechanism—enabling non-destructive sampling, classical storage, and on-demand reconstruction of quantum states at arbitrary circuit nodes. Our method integrates SWAP testing, fidelity optimization, gradient-descent-based deep neural networks, and gradient-free evolutionary strategies, reconstructing states solely from fidelity feedback. On IBM’s real quantum hardware, we achieve ≈1.0 fidelity for canonical states (e.g., Hadamard states); in simulation, the average fidelity over 100 random single-qubit states reaches 0.999. This is the first demonstration of a modular, non-destructive quantum workflow, establishing a novel paradigm for pipelined quantum computation and quantum state reuse.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Non-convex Optimization
• Intelligent Robots: State Estimation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We introduce a novel technique that enables observation of quantum states without direct measurement, preserving them for reuse. Our method allows multiple quantum states to be observed at different points within a single circuit, one at a time, and saved into classical memory without destruction. These saved states can be accessed on demand by downstream applications, introducing a dynamic and programmable notion of quantum memory that supports modular, non-destructive quantum workflows. We propose a hardware-agnostic, machine learning-driven framework to capture non-destructive estimates, or"snapshots,"of quantum states at arbitrary points within a circuit, enabling classical storage and later reconstruction, similar to memory operations in classical computing. This capability is essential for debugging, introspection, and persistent memory in quantum systems, yet remains difficult due to the no-cloning theorem and destructive measurements. Our guess-and-check approach uses fidelity estimation via the SWAP test to guide state reconstruction. We explore both gradient-based deep neural networks and gradient-free evolutionary strategies to estimate quantum states using only fidelity as the learning signal. We demonstrate a key component of our framework on IBM quantum hardware, achieving high-fidelity (approximately 1.0) reconstructions for Hadamard and other known states. In simulation, our models achieve an average fidelity of 0.999 across 100 random quantum states. This provides a pathway toward non-volatile quantum memory, enabling long-term storage and reuse of quantum information, and laying groundwork for future quantum memory architectures.

Problem

• Observing quantum states without direct measurement
• Storing multiple quantum states in classical memory
• Reconstructing quantum states for reuse and debugging

Innovation

• Non-destructive quantum state observation via SWAP test
• Machine learning-driven quantum state reconstruction framework
• Classical storage and on-demand quantum state retrieval