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Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases
Quantum Physics · Submitted 13 Sep 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
Quantum computing crucially relies on the ability to efficiently characterize the quantum states output by quantum hardware. Conventional methods which probe these states through direct measurements and classically computed correlations become computationally expensive when increasing the system size. 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. Here, we realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological (SPT) phases of a spin model characterized by a non-zero string order parameter. We benchmark the performance of the QCNN based on approximate ground states of a family of cluster-Ising Hamiltonians which we prepare using a hardware-efficient, low-depth state preparation circuit. 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Quantum computing crucially relies on the ability to efficiently characterize the quantum states output by quantum hardware. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Conventional methods which probe these states through direct measurements and classically computed correlations become computationally expensive when increasing the system size. | background 0.89 | 0.06 | 0.02 | asserted 1.00 |
| s2 | 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. | background 0.98 | 0.06 | 0.05 | hedged possibility 0.93 |
| s3 | Here, we realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological (SPT) phases of a spin model characterized by a non-zero string order parameter. | contribution 0.97 | 0.97 | 0.17 | asserted 1.00 |
| s4 | We benchmark the performance of the QCNN based on approximate ground states of a family of cluster-Ising Hamiltonians which we prepare using a hardware-efficient, low-depth state preparation circuit. | method 0.98 | 0.96 | 0.05 | asserted 0.90 |
| s5 | 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. | result 1.00 | 0.97 | 0.82 | asserted 1.00 |
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The paper
Original abstract
Quantum computing crucially relies on the ability to efficiently characterize the quantum states output by quantum hardware. Conventional methods which probe these states through direct measurements and classically computed correlations become computationally expensive when increasing the system size. 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. Here, we realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological (SPT) phases of a spin model characterized by a non-zero string order parameter. We benchmark the performance of the QCNN based on approximate ground states of a family of cluster-Ising Hamiltonians which we prepare using a hardware-efficient, low-depth state preparation circuit. 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.
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How the literature uses it
7 arXiv papers cite this in 10 sentences
Papers citing it · 5 sentences without a confident label
Cites it as support 1 paper
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This feature enables the immediate implementation of our approach in experiments, as already done in two different quantum simulation platforms [ref] , prior to our rigorous theoretical understanding.
Exact Quantum Algorithms for Quantum Phase Recognition: Renormalization Group and Error Correction · Discussion and implications · 0.98
Cites it as background 3 papers
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In recent years there has been a proliferation of proposals for quantum algorithms and circuits dubbed “quantum neural networks” (QNNs) [ref] .
Evaluating the performance of sigmoid quantum perceptrons in quantum neural networks · Introduction · 1.00
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The first experimentally realized quantum neural networks are made of 7 [ref] to 40 qubits [ref] , each connected to two or four nearest neighbors.
Quantum reservoir neural network implementation on coherently coupled quantum oscillators · Introduction · 0.81
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Prior works have proposed quantum versions of perceptrons [ref] , support vector machines [ref] , Boltzmann machines [ref] , autoencoders [ref] , and convolutional neural networks [ref] .
Realization of a quantum neural network using repeat-until-success circuits in a superconducting quantum processor · Introduction · 0.99
What it cites · 5 sentences without a confident label
Uses the method or tool of 1 paper
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To make the state preparation circuit less susceptible to$T_1$errors, we then compute an equivalent set of rotational angles$θ̃_opt$yielding the same$U(θ̃_opt)=U(θ_opt)$ [ref] , but keeping the individual qubits preferentially in their respective ground state in the beginning of the state preparation sequence, see Appendix [ref] for details.
Non-trivial symmetries in quantum landscapes and their resilience to quantum noise · Introduction · 0.99
Compares with 1 paper
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Compared to previous experiments on superconducting quantum hardware, in which an exact matrix product state representation [ref] was used to prepare ground states of $H$ for specific combinations ${h_1,h_2}$, the use of a variational circuit allows us to prepare approximate ground states for any parameter set ${h_1,h_2}$.
Crossing a topological phase transition with a quantum computer · Introduction · 0.85
Cites as background 5 papers
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Furthermore, topological states have recently been prepared on quantum hardware and analyzed by measuring characteristic observables [ref] such as string order parameters.
Crossing a topological phase transition with a quantum computer · Introduction · 0.86
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Furthermore, topological states have recently been prepared on quantum hardware and analyzed by measuring characteristic observables [ref] such as string order parameters.
Identification of symmetry-protected topological states on noisy quantum computers · Introduction · 0.86
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Introduction – Remarkable progress in building quantum hardware [ref] has fueled the search for potential applications of both near-term and future error-corrected quantum computers [ref] , particularly in the simulation of quantum many-body systems [ref] and in machine learning [ref] .
Quantum computational advantage using photons · Introduction · 0.97
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