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DOE OSTI2023

Quantum multi-programming for Grover’s search

Quantum multi-programming is a method utilizing contemporary noisy intermediate-scale quantum computers by executing multiple quantum circuits concurrently. Despite early research on it, the research remains on quantum gates or small-size quantum algorithms without correlation. In this paper, we propose a quantum multi-programming (QMP algorithm for Grover's search. Our algorithm decomposes Grover's algorithm by the partial diffusion operator and executes the decomposed circuits in parallel by QMP. We proved that this new algorithm increases the rotation angle of the Grover operator which, as a result, increases the success probability. The new algorithm is implemented on IBM quantum computers and compared with the canonical Grover's algorithm and other variations of Grover's algorithms. So, the empirical tests validate that our new algorithm outperforms other variations of Grover's algorithms as well as the canonical Grover's algorithm.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

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. Here, in this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a timeseries 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.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC), a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs-–multi-programming (MP), crosstalk mitigation (CM)—as well as a combination of their optimization goals (i.e., a combined MP-CM).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Kinetic Coupling of Water Splitting and Photoreforming on SrTiO 3 -Based Photocatalysts

Coupling the anodic half-reactions of overall water splitting and oxygenate photoreforming (i.e., proton reduction and oxygenate oxidations) on Al-doped SrTiO3 decorated with a co-catalyst enables efficient photocatalytic H2 generation along with oxygenate conversion without accumulating undesired intermediates such as formaldehyde. The net H2-evolution rates result from the interplay between water oxidation, oxygenate oxidation, and the back-reaction of H2 and O2 to water. When the latter pathway is quantitatively suppressed (e.g., on RhCrOx co-catalyst or in excess of oxygenated hydrocarbons), the initial H2-evolution rates are independent of the oxygenate nature and concentration. This is a consequence of the reduction equivalents for H2-evolution provided by water oxidation compensating changes in the rates of oxygenate conversion. Thus, under conditions of suppressed back-reaction, water and oxygenate oxidations have equal quantum efficiencies. The selectivities to water and oxygenate oxidation depend on oxygenate nature and concentration. Transformations mediated by indirect hole transfer dominate as a result of the water oxidation at the anode and the associated intermediates generated in O2-evolution catalysis (e.g. ·OH, ·O and ·OOH). On the undecorated semiconductor, the O2 produced during overall water splitting is reductively activated to participate in glycerol oxidation without consuming evolved H2. Acknowledgements The authors would like to thank ESRF in Grenoble, France, for providing beam time at the ID26 station for XAFS experiments. K.E.S. gratefully acknowledges financial support by the Fond der Chemischen Industrie (FCI). J.A.L. and O.Y.G. acknowledge support for his contribution by the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy. The authors thank Xaver Hecht for BET measurements, Martin Neukamm for SEM and AAS measurements and Dr. Udishnu Sanyal for TEM imaging. Christine Schwarz is acknowledged for technical assistance in NMR experiments.

Sanwald, Kai E.↗