148 research outputs found
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
A massive gap exists between current quantum computing (QC) prototypes, and
the size and scale required for many proposed QC algorithms. Current QC
implementations are prone to noise and variability which affect their
reliability, and yet with less than 80 quantum bits (qubits) total, they are
too resource-constrained to implement error correction. The term Noisy
Intermediate-Scale Quantum (NISQ) refers to these current and near-term systems
of 1000 qubits or less. Given NISQ's severe resource constraints, low
reliability, and high variability in physical characteristics such as coherence
time or error rates, it is of pressing importance to map computations onto them
in ways that use resources efficiently and maximize the likelihood of
successful runs.
This paper proposes and evaluates backend compiler approaches to map and
optimize high-level QC programs to execute with high reliability on NISQ
systems with diverse hardware characteristics. Our techniques all start from an
LLVM intermediate representation of the quantum program (such as would be
generated from high-level QC languages like Scaffold) and generate QC
executables runnable on the IBM Q public QC machine. We then use this framework
to implement and evaluate several optimal and heuristic mapping methods. These
methods vary in how they account for the availability of dynamic machine
calibration data, the relative importance of various noise parameters, the
different possible routing strategies, and the relative importance of
compile-time scalability versus runtime success. Using real-system
measurements, we show that fine grained spatial and temporal variations in
hardware parameters can be exploited to obtain an average x (and up to
x) improvement in program success rate over the industry standard IBM
Qiskit compiler.Comment: To appear in ASPLOS'1
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
BQA: A High-performance Quantum Circuits Scheduling Strategy Based on Heuristic Search
Currently, quantum computing is developing at a high speed because its high
parallelism and high computing power bring new solutions to many fields.
However, due to chip process technology, it is difficult to achieve full
coupling of all qubits on a quantum chip, so when compiling a quantum circuit
onto a physical chip, it is necessary to ensure that the two-qubit gate acts on
a pair of coupled qubits by inserting swap gates. It will cause great
additional cost when a large number of swap gates are inserted, leading to the
execution time of quantum circuits longer. In this paper, we designed a way
based on the business to insert swap gates BQA(Busy Qubits Avoid). We exploit
the imbalance of the number of gates on qubits, trying to hide the overhead of
swap gates. At the same time, we also expect swap gates to make as little
negative impact on subsequent two-qubit gates as possible. We have designed a
heuristic function that can take into account both of these points. Compared
with qiskit, the execution time of the circuit optimized by our proposed method
is only 0.5 times that of the qiskit compiled circuit. And when the number of
two-qubit gates is large, it will achieve higher level than general conditions.
This implies higher execution efficiency and lower decoherence error rate.Comment: 9 pages, 8 figure
Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications
There is no unique way to encode a quantum algorithm into a quantum circuit.
With limited qubit counts, connectivities, and coherence times, circuit
optimization is essential to make the best use of near-term quantum devices. We
introduce two separate ideas for circuit optimization and combine them in a
multi-tiered quantum circuit optimization protocol called AQCEL. The first
ingredient is a technique to recognize repeated patterns of quantum gates,
opening up the possibility of future hardware co-optimization. The second
ingredient is an approach to reduce circuit complexity by identifying zero- or
low-amplitude computational basis states and redundant gates. As a
demonstration, AQCEL is deployed on an iterative and efficient quantum
algorithm designed to model final state radiation in high energy physics. For
this algorithm, our optimization scheme brings a significant reduction in the
gate count without losing any accuracy compared to the original circuit.
Additionally, we have investigated whether this can be demonstrated on a
quantum computer using polynomial resources. Our technique is generic and can
be useful for a wide variety of quantum algorithms.Comment: 22 pages, 16 figure
- …