Quantum-Classical Multiple Kernel Learning

As quantum computers become increasingly practical, so does the prospect of using quantum computation to improve upon traditional algorithms. Kernel methods in machine learning is one area where such improvements could be realized in the near future. Paired with kernel methods like support-vector machines, small and noisy quantum computers can evaluate classically-hard quantum kernels that capture unique notions of similarity in data. Taking inspiration from techniques in classical machine learning, this work investigates simulated quantum kernels in the context of multiple kernel learning (MKL). We consider pairwise combinations of several classical-classical, quantum-quantum, and quantum-classical kernels in an empirical investigation of their classification performance with support-vector machines. We also introduce a novel approach, which we call QCC-net (quantum-classical-convex neural network), for optimizing the weights of base kernels together with any kernel parameters. We show this approach to be effective for enhancing various performance metrics in an MKL setting. Looking at data with an increasing number of features (up to 13 dimensions), we find parameter training to be important for successfully weighting kernels in some combinations. Using the optimal kernel weights as indicators of relative utility, we find growing contributions from trainable quantum kernels in quantum-classical kernel combinations as the number of features increases. We observe the opposite trend for combinations containing simpler, non-parametric quantum kernels.Comment: 15 pages, Supplementary Information on page 15, 6 main figures, 1 supplementary figur

Thermal Conductivity of GaAs Nanowire Arrays Measured by the 3ω Method

Vertical nanowire (NW) arrays are the basis for a variety of nanoscale devices. Understanding heat transport in these devices is an important concern, especially for prospective thermoelectric applications. To facilitate thermal conductivity measurements on as-grown NW arrays, a common NW-composite device architecture was adapted for use with the 3ω method. We describe the application of this technique to obtain thermal conductivity measurements on two GaAs NW arrays featuring ~130 nm diameter NWs with a twinning superlattice (TSL) and a polytypic (zincblende/wurtzite) crystal structure, respectively. Our results indicate NW thermal conductivities of 5.2 ± 1.0 W/m-K and 8.4 ± 1.6 W/m-K in the two samples, respectively, showing a significant reduction in the former, which is the first such measurements on TSL NWs. Nearly an order of magnitude difference from the bulk thermal conductivity (~50 W/m-K) is observed for the TSL NW sample, one of the lowest values measured to date for GaAs NWs

Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification

Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. In this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a time-series Hamiltonian kernel (TSHK). A TSHK is constructed with a sum of inner products generated by quantum states evolved using a parameterized time evolution operator. This sum is then optimally weighted using techniques derived from multiple kernel learning. Because we treat the kernel weighting step as a differentiable convex optimization problem, our method can be regarded as an end-to-end learnable hybrid quantum-classical-convex neural network, or QCC-net, whose output is a data set-generalized kernel function suitable for use in any kernelized machine learning technique such as the support vector machine (SVM). Using our TSHK as input to a SVM, we classify univariate and multivariate time-series using quantum circuit simulators and demonstrate the efficient parallel deployment of the algorithm to 127-qubit superconducting quantum processors using quantum multi-programming.Comment: 23 pages, 10 figures, 1 table and 1 code snippe

AgnostiqHQ/covalent: v0.228.0-rc.0

[0.228.0-rc.0] - 2023-08-3