Supervised learning with quantum enhanced feature spaces

Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous for pattern recognition, with support vector machines (SVMs) being the most well-known method for classification problems. However, there are limitations to the successful solution to such problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element to computational speed-ups afforded by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here, we propose and experimentally implement two novel methods on a superconducting processor. Both methods represent the feature space of a classification problem by a quantum state, taking advantage of the large dimensionality of quantum Hilbert space to obtain an enhanced solution. One method, the quantum variational classifier builds on [1,2] and operates through using a variational quantum circuit to classify a training set in direct analogy to conventional SVMs. In the second, a quantum kernel estimator, we estimate the kernel function and optimize the classifier directly. The two methods present a new class of tools for exploring the applications of noisy intermediate scale quantum computers [3] to machine learning.Comment: Fixed typos, added figures and discussion about quantum error mitigatio

Normal reference values of strength in pelvic floor muscle of women: a descriptive and inferential study

Background: To describe the clinical, functional and quality of life characteristics in women with Stress Urinary Incontinence (SUI). In addition, to analyse the relationship between the variables reported by the patients and those informed by the clinicians, and the relationship between instrumented variables and the manual pelvic floor strength assessment.Methods: Two hundred and eighteen women participated in this observational, analytical study. An interview about Urinary Incontinence and the quality of life questionnaires (EuroQoL-5D and SF-12) were developed as outcomes reported by the patients. Manual muscle testing and perineometry as outcomes informed by the clinician were assessed. Descriptive and correlation analysis were carried out.Results: The average age of the subjects was (39.93 ± 12.27 years), (24.49 ± 3.54 BMI). The strength evaluated by manual testing of the right levator ani muscles was 7.79 ± 2.88, the strength of left levator ani muscles was 7.51 ± 2.91 and the strength assessed with the perineometer was 7.64 ± 2.55. A positive correlation was found between manual muscle testing and perineometry of the pelvic floor muscles (p < .001). No correlation was found between outcomes of quality of life reported by the patients and outcomes of functional capacity informed by the physiotherapist.Conclusion: A stratification of the strength of pelvic floor muscles in a normal distribution of a large sample of women with SUI was done, which provided the clinic with a baseline. There is a relationship between the strength of the pelvic muscles assessed manually and that obtained by a perineometer in women with SUI. There was no relationship between these values of strength and quality of life perceived

Matching and maximum likelihood decoding of a multi-round subsystem quantum error correction experiment

Quantum error correction offers a promising path for performing quantum computations with low errors. Although a fully fault-tolerant execution of a quantum algorithm remains unrealized, recent experimental developments, along with improvements in control electronics, are enabling increasingly advanced demonstrations of the necessary operations for applying quantum error correction. Here, we perform quantum error correction on superconducting qubits connected in a heavy-hexagon lattice. The full processor can encode a logical qubit with distance three and perform several rounds of fault-tolerant syndrome measurements that allow the correction of any single fault in the circuitry. Furthermore, by using dynamic circuits and classical computation as part of our syndrome extraction protocols, we can exploit real-time feedback to reduce the impact of energy relaxation error in the syndrome and flag qubits. We show that the logical error varies depending on the use of a perfect matching decoder compared to a maximum likelihood decoder. We observe a logical error per syndrome measurement round as low as $\sim0.04$ for the matching decoder and as low as $\sim0.03$ for the maximum likelihood decoder. Our results suggest that more significant improvements to decoders are likely on the horizon as quantum hardware has reached a new stage of development towards fully fault-tolerant operations.Comment: 15 pages, 6 figures, 5 table

Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning"

Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning", npj Quantum Information 3, Article number: 16 (2017)