59 research outputs found
A Language and Hardware Independent Approach to Quantum-Classical Computing
Heterogeneous high-performance computing (HPC) systems offer novel
architectures which accelerate specific workloads through judicious use of
specialized coprocessors. A promising architectural approach for future
scientific computations is provided by heterogeneous HPC systems integrating
quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale
ACCelerator) --- a programming model and software framework that enables
quantum acceleration within standard or HPC software workflows. XACC follows a
coprocessor machine model that is independent of the underlying quantum
computing hardware, thereby enabling quantum programs to be defined and
executed on a variety of QPUs types through a unified application programming
interface. Moreover, XACC defines a polymorphic low-level intermediate
representation, and an extensible compiler frontend that enables language
independent quantum programming, thus promoting integration and
interoperability across the quantum programming landscape. In this work we
define the software architecture enabling our hardware and language independent
approach, and demonstrate its usefulness across a range of quantum computing
models through illustrative examples involving the compilation and execution of
gate and annealing-based quantum programs
Validating Quantum-Classical Programming Models with Tensor Network Simulations
The exploration of hybrid quantum-classical algorithms and programming models
on noisy near-term quantum hardware has begun. As hybrid programs scale towards
classical intractability, validation and benchmarking are critical to
understanding the utility of the hybrid computational model. In this paper, we
demonstrate a newly developed quantum circuit simulator based on tensor network
theory that enables intermediate-scale verification and validation of hybrid
quantum-classical computing frameworks and programming models. We present our
tensor-network quantum virtual machine (TNQVM) simulator which stores a
multi-qubit wavefunction in a compressed (factorized) form as a matrix product
state, thus enabling single-node simulations of larger qubit registers, as
compared to brute-force state-vector simulators. Our simulator is designed to
be extensible in both the tensor network form and the classical hardware used
to run the simulation (multicore, GPU, distributed). The extensibility of the
TNQVM simulator with respect to the simulation hardware type is achieved via a
pluggable interface for different numerical backends (e.g., ITensor and
ExaTENSOR numerical libraries). We demonstrate the utility of our TNQVM quantum
circuit simulator through the verification of randomized quantum circuits and
the variational quantum eigensolver algorithm, both expressed within the
eXtreme-scale ACCelerator (XACC) programming model
Progressive join algorithms considering user preference
Progressive query processing is a new attractive paradigm for exploratory data analysis. This paper considers the case where users want to receive results ordered according to their preference, and specifically focuses on the design of join algorithms. We investigate the use of contour lines in progressive algorithms with user preferences, and propose ContourJoin to reduce sorting overhead of progressive preference-aware joins. Experimental results show that compared with the na ̈ıve blocking algorithm and the top-k RankJoin algorithm, ContourJoin has superior performance in both early result generation and total result computation
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