Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit
- URL: http://arxiv.org/abs/2504.21250v1
- Date: Wed, 30 Apr 2025 01:18:21 GMT
- Title: Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit
- Authors: Debarshi Kundu, Avimita Chatterjee, Archisman Ghosh, Swaroop Ghosh,
- Abstract summary: Quantum Snapshot with Dynamic Circuit (QSDC) is a hardware-agnostic, learning-driven framework for capturing quantum snapshots.<n>We introduce a guess-and-check methodology in which a classical model is trained to reconstruct an unknown quantum state.<n>Our approach supports single-copy, mid-circuit state reconstruction, assuming hardware with dynamic circuit support.
- Score: 1.912429179274357
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We propose Quantum Snapshot with Dynamic Circuit (QSDC), a hardware-agnostic, learning-driven framework for capturing quantum snapshots: non-destructive estimates of quantum states at arbitrary points within a quantum circuit, which can then be classically stored and later reconstructed. This functionality is vital for introspection, debugging, and memory in quantum systems, yet remains fundamentally constrained by the no-cloning theorem and the destructive nature of measurement. QSDC introduces a guess-and-check methodology in which a classical model, powered by either gradient-based neural networks or gradient-free evolutionary strategie, is trained to reconstruct an unknown quantum state using fidelity from the SWAP test as the sole feedback signal. Our approach supports single-copy, mid-circuit state reconstruction, assuming hardware with dynamic circuit support and sufficient coherence time. We validate core components of QSDC both in simulation and on IBM quantum hardware. In noiseless settings, our models achieve average fidelity up to 0.999 across 100 random quantum states; on real devices, we accurately reconstruct known single-qubit states (e.g., Hadamard) within three optimization steps.
Related papers
- Guess, SWAP, Repeat : Capturing Quantum Snapshots in Classical Memory [2.089191490381739]
We introduce a novel technique that enables observation of quantum states without direct measurement, preserving them for reuse.<n>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.<n>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.
arXiv Detail & Related papers (2025-04-20T02:39:30Z) - Quantum Compressive Sensing Meets Quantum Noise: A Practical Exploration [8.260432715157027]
We present a practical implementation of Quantum Compressive Sensing (QCS) on Amazon Braket.
QCS is a quantum data-driven approach to compressive sensing where the state of the tensor network is represented by a quantum state over a set of entangled qubits.
We discuss potential long-term directions aimed at unlocking the full potential of quantum compressive sensing for applications such as signal recovery and image processing.
arXiv Detail & Related papers (2025-01-21T18:10:03Z) - Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers [37.69303106863453]
We propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers.
Our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average.
arXiv Detail & Related papers (2024-06-17T14:10:10Z) - Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
We present a model for parallelizing simulation of quantum circuit executions.
The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
arXiv Detail & Related papers (2024-06-05T17:16:07Z) - Single-Round Proofs of Quantumness from Knowledge Assumptions [41.94295877935867]
A proof of quantumness is an efficiently verifiable interactive test that an efficient quantum computer can pass.
Existing single-round protocols require large quantum circuits, whereas multi-round ones use smaller circuits but require experimentally challenging mid-circuit measurements.
We construct efficient single-round proofs of quantumness based on existing knowledge assumptions.
arXiv Detail & Related papers (2024-05-24T17:33:10Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Mapping quantum circuits to shallow-depth measurement patterns based on
graph states [0.0]
We create a hybrid simulation technique for measurement-based quantum computing.
We show that groups of fully commuting operators can be implemented using fully-parallel, i.e., non-adaptive, measurements.
We discuss how such circuits can be implemented in constant quantum depths by employing quantum teleportation.
arXiv Detail & Related papers (2023-11-27T19:00:00Z) - Quantum Imitation Learning [74.15588381240795]
We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL.
We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL)
Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
arXiv Detail & Related papers (2023-04-04T12:47:35Z) - Improved variational quantum eigensolver via quasi-dynamical evolution [0.0]
The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for current and near-term quantum devices.
There are problems with VQE that forbid a favourable scaling towards quantum advantage.
We propose and extensively test a quantum annealing inspired algorithm that supplements VQE.
The improved VQE avoids barren plateaus, exits local minima, and works with low-depth circuits.
arXiv Detail & Related papers (2022-02-21T11:21:44Z) - Realizing Quantum Convolutional Neural Networks on a Superconducting
Quantum Processor to Recognize Quantum Phases [2.1465372441653354]
Quantum neural networks tailored to recognize specific features of quantum states by combining unitary operations, measurements and feedforward promise to require fewer measurements and to tolerate errors.
We realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological phases of a spin model characterized by a non-zero string order parameter.
We find that, despite being composed of finite-fidelity gates itself, the QCNN recognizes the topological phase with higher fidelity than direct measurements of the string order parameter for the prepared states.
arXiv Detail & Related papers (2021-09-13T12:32:57Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.