1,122 research outputs found
Full-Stack, Real-System Quantum Computer Studies: Architectural Comparisons and Design Insights
In recent years, Quantum Computing (QC) has progressed to the point where
small working prototypes are available for use. Termed Noisy Intermediate-Scale
Quantum (NISQ) computers, these prototypes are too small for large benchmarks
or even for Quantum Error Correction, but they do have sufficient resources to
run small benchmarks, particularly if compiled with optimizations to make use
of scarce qubits and limited operation counts and coherence times. QC has not
yet, however, settled on a particular preferred device implementation
technology, and indeed different NISQ prototypes implement qubits with very
different physical approaches and therefore widely-varying device and machine
characteristics.
Our work performs a full-stack, benchmark-driven hardware-software analysis
of QC systems. We evaluate QC architectural possibilities, software-visible
gates, and software optimizations to tackle fundamental design questions about
gate set choices, communication topology, the factors affecting benchmark
performance and compiler optimizations. In order to answer key cross-technology
and cross-platform design questions, our work has built the first top-to-bottom
toolflow to target different qubit device technologies, including
superconducting and trapped ion qubits which are the current QC front-runners.
We use our toolflow, TriQ, to conduct {\em real-system} measurements on 7
running QC prototypes from 3 different groups, IBM, Rigetti, and University of
Maryland. From these real-system experiences at QC's hardware-software
interface, we make observations about native and software-visible gates for
different QC technologies, communication topologies, and the value of
noise-aware compilation even on lower-noise platforms. This is the largest
cross-platform real-system QC study performed thus far; its results have the
potential to inform both QC device and compiler design going forward.Comment: Preprint of a publication in ISCA 201
Validating multi-photon quantum interference with finite data
Multi-particle interference is a key resource for quantum information
processing, as exemplified by Boson Sampling. Hence, given its fragile nature,
an essential desideratum is a solid and reliable framework for its validation.
However, while several protocols have been introduced to this end, the approach
is still fragmented and fails to build a big picture for future developments.
In this work, we propose an operational approach to validation that encompasses
and strengthens the state of the art for these protocols. To this end, we
consider the Bayesian hypothesis testing and the statistical benchmark as most
favorable protocols for small- and large-scale applications, respectively. We
numerically investigate their operation with finite sample size, extending
previous tests to larger dimensions, and against two adversarial algorithms for
classical simulation: the Mean-Field sampler and the Metropolized Independent
Sampler. To evidence the actual need for refined validation techniques, we show
how the assessment of numerically simulated data depends on the available
sample size, as well as on the internal hyper-parameters and other practically
relevant constraints. Our analyses provide general insights into the challenge
of validation, and can inspire the design of algorithms with a measurable
quantum advantage.Comment: 10 pages, 7 figure
Approximation of Quantum States Using Decision Diagrams
The computational power of quantum computers poses major challenges to new
design tools since representing pure quantum states typically requires
exponentially large memory. As shown previously, decision diagrams can reduce
these memory requirements by exploiting redundancies. In this work, we
demonstrate further reductions by allowing for small inaccuracies in the
quantum state representation. Such inaccuracies are legitimate since quantum
computers themselves experience gate and measurement errors and since quantum
algorithms are somewhat resistant to errors (even without error correction). We
develop four dedicated schemes that exploit these observations and effectively
approximate quantum states represented by decision diagrams. We empirically
show that the proposed schemes reduce the size of decision diagrams by up to
several orders of magnitude while controlling the fidelity of approximate
quantum state representations
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