Controlled quantum search

Lacy, K. de; Noakes, Lyle; Twamley, J.; Wang, Jingbo

doi:10.1007/s11128-018-2031-6

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The foundation of QML targets to solve research foundation problems in mathematical analysis to generalize quantum to improve classical learning tasks with potential optimization to speed of execution. Some of the task's researchers are leveraging includes quantum techniques in Quantum Simulation [28], applied around nanotech, bio-medical imaging, physical chemistry, and with quantum systems tasks, such as search [29], which further provides polynomial speed, as compared to classical algorithms for other varied scenarios.…”

Section: Application Of Quantum Machine Learningmentioning

confidence: 99%

Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

Sengupta

Srivastava

2021

BMC Med Inform Decis Mak

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Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

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Section: Application Of Quantum Machine Learningmentioning

confidence: 99%

Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

Sengupta

Srivastava

2021

BMC Med Inform Decis Mak

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“…Since then, Childs and Young proposed their nonlinear protocol using the same Gross-Pitaevskii dynamics with a runtime scaling as O(min{1/g log(gN ), √ N }), achieving exponentially faster rates than previous results [8]. In [25], de Lacy also uses the Gross-Pitaevskii nonlinearity, performing an unstructured search on a complete graph in time O(N/(g m(N − m))), where m is the number of marked terms. Others have applied the concept of nonlinear quantum dynamics to quantum walks [26][27][28][29].…”

Section: Nonlinear Quantum Computingmentioning

confidence: 99%

Graph comparison via nonlinear quantum search

Chiew

Lacy

et al. 2019

Quantum Inf Process

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In this paper we present an efficiently scaling quantum algorithm which finds the size of the maximum common edge subgraph for a pair of arbitrary graphs and thus provides a meaningful measure of graph similarity. The algorithm makes use of a two-part quantum dynamic process: in the first part we obtain information crucial for the comparison of two graphs through linear quantum computation. However, this information is hidden in the quantum system with vanishingly small amplitude that even quantum algorithms such as Grover's search are not fast enough to distill the information efficiently. In order to extract the information we call upon techniques in nonlinear quantum computing to provide the speed-up necessary for an efficient algorithm. The linear quantum circuit requires O(n 3 log 3 (n) log log(n)) elementary quantum gates and the nonlinear evolution under the Gross-Pitaevskii equation has a time scaling of O( 1 g n 2 log 3 (n) log log(n)), where n is the number of vertices in each graph and g is the strength of the Gross-Pitaveskii non-linearity. Through this example, we demonstrate the power of nonlinear quantum search techniques to solve a subset of NP-hard problems.

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“…Nonlinear time evolution can be presented in standard quantum mechanics as an effective model, e.g., the Gross-Pitaevskii equation [3] which approximately describes the collective behavior of atoms in a Bose-Einstein condensate. Were it not approximate, the Gross-Pitaevskii equation would be applicable to solve the unstructured search problem with an exponential improvement over protocols based on standard quantum theory [2,4].…”

mentioning

confidence: 99%

Experimental orthogonalization of highly overlapping quantum states with single photons

et al. 2019

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We experimentally realize a nonlinear quantum protocol on single-photon qubits with linear optical elements and appropriate measurements. The quantum nonlinearity is induced by post-selecting the polarization qubit based on a measurement result obtained on the spatial degree of freedom of the single photon which plays the role of a second qubit. Initially, both qubits are prepared in the same quantum state and an appropriate two-qubit unitary transformation entangles them before the measurement on the spatial part. We analyze the result by quantum state tomography on the polarization degree of freedom. We then demonstrate the usefulness of the protocol for quantum state discrimination by iteratively applying it on either one of two slightly different quantum states which rapidly converge to different orthogonal states by the iterative dynamics. Our work opens the door to employ effective quantum nonlinear evolution for quantum information processing.

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Controlled quantum search

Cited by 5 publications

References 15 publications

Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

Graph comparison via nonlinear quantum search

Experimental orthogonalization of highly overlapping quantum states with single photons

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