On April 29, 2025, researchers Debarshi Kundu, Avimita Chatterjee, Archisman Ghosh, and Swaroop Ghosh published a study titled Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit. Their work introduces QSDC, a hardware-agnostic, learning-driven framework enabling non-destructive quantum state snapshots. This innovation addresses limitations posed by the no-cloning theorem and destructive measurements, validated with high fidelity in simulations and real devices.

Researchers introduced QSDC (Snapshot with Dynamic Circuit), a hardware-agnostic framework enabling non-destructive state estimation during quantum circuits. By uses a guess-and-check method with classical models trained via neural networks or evolutionary strategies QSDC reconstruct unknown states using SWAP test fidelity as feedback. Validated in simulations and on real devices, the approach achieves high fidelities (up to 0.999) in noiseless conditions and accurately reconstructs single-qubit states within three optimization steps. This method supports mid-circuit state reconstruction, addressing key quantum system introspection and debugging challenges.

Quantum computing is poised to revolutionise industries from cryptography to drug discovery. Yet, its promise hinges on overcoming technical hurdles, particularly managing fragile quantum states and optimising computations.

Quantum Snapshot with Dynamic Circuit (QSDC)

This paper introduces Quantum Snapshot with Dynamic Circuit (QSDC), a new approach for capturing and storing information about quantum states during quantum computation without destroying those states. This addresses a fundamental challenge in quantum computing: typically, when you measure a quantum state to learn about it, you irreversibly change it, losing the very information you’re trying to preserve.

The researchers propose a method that allows for “quantum snapshots” – non-destructive estimates of quantum states at different points within a quantum circuit. These snapshots can be stored classically and later reconstructed. This capability is crucial for debugging quantum programs, understanding what’s happening inside quantum circuits, and creating a form of quantum memory.

QSDC works through a guess-and-check approach. A classical model (either a neural network or evolutionary algorithm) attempts to reconstruct an unknown quantum state. The model’s guess is compared to the actual quantum state using a quantum operation called a SWAP test, which provides a measurement of similarity (fidelity) without destroying the original state. This feedback helps the model improve its guess iteratively.

A key innovation of QSDC is that it can work with just a single copy of a quantum state, unlike traditional quantum state tomography which requires many identical copies. The approach requires quantum hardware with dynamic circuit capabilities – the ability to adjust quantum operations based on measurement results during execution – and sufficient coherence time for the quantum system to remain stable during the process.

The researchers validated their approach both in computer simulations and on actual IBM quantum hardware. In ideal, noise-free simulations, their models achieved very high accuracy (average fidelity up to 0.999) across 100 random test states. Even on real quantum devices with their inherent noise and imperfections, they could accurately reconstruct known single-qubit states within just three optimization steps.

The paper outlines a vision for quantum-centric supercomputing architecture where classical and quantum processors work together closely. The classical processor needs to process measurement results and update the model quickly, all within the coherence time of the qubits. While current hardware has limitations that make full implementation challenging, the researchers demonstrated key components of their approach and laid groundwork for future development.

QSDC opens possibilities for richer forms of quantum program analysis, debugging, and memory. By enabling non-destructive access to quantum information during computation and storing it classically, this approach could help overcome some of the fundamental limitations in current quantum technologies while supporting the development of more sophisticated quantum applications.