1,347 research outputs found
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy
intermediate-scale quantum computers for practical applications. Expanding the
portfolio of such techniques, we propose a quantum circuit learning algorithm
that can be used to assist the characterization of quantum devices and to train
shallow circuits for generative tasks. The procedure leverages quantum hardware
capabilities to its fullest extent by using native gates and their qubit
connectivity. We demonstrate that our approach can learn an optimal preparation
of the Greenberger-Horne-Zeilinger states, also known as "cat states". We
further demonstrate that our approach can efficiently prepare approximate
representations of coherent thermal states, wave functions that encode
Boltzmann probabilities in their amplitudes. Finally, complementing proposals
to characterize the power or usefulness of near-term quantum devices, such as
IBM's quantum volume, we provide a new hardware-independent metric called the
qBAS score. It is based on the performance yield in a specific sampling task on
one of the canonical machine learning data sets known as Bars and Stripes. We
show how entanglement is a key ingredient in encoding the patterns of this data
set; an ideal benchmark for testing hardware starting at four qubits and up. We
provide experimental results and evaluation of this metric to probe the trade
off between several architectural circuit designs and circuit depths on an
ion-trap quantum computer.Comment: 16 pages, 9 figures. Minor revisions. As published in npj Quantum
Informatio
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
Resource Optimized Quantum Architectures for Surface Code Implementations of Magic-State Distillation
Quantum computers capable of solving classically intractable problems are
under construction, and intermediate-scale devices are approaching completion.
Current efforts to design large-scale devices require allocating immense
resources to error correction, with the majority dedicated to the production of
high-fidelity ancillary states known as magic-states. Leading techniques focus
on dedicating a large, contiguous region of the processor as a single
"magic-state distillation factory" responsible for meeting the magic-state
demands of applications. In this work we design and analyze a set of optimized
factory architectural layouts that divide a single factory into spatially
distributed factories located throughout the processor. We find that
distributed factory architectures minimize the space-time volume overhead
imposed by distillation. Additionally, we find that the number of distributed
components in each optimal configuration is sensitive to application
characteristics and underlying physical device error rates. More specifically,
we find that the rate at which T-gates are demanded by an application has a
significant impact on the optimal distillation architecture. We develop an
optimization procedure that discovers the optimal number of factory
distillation rounds and number of output magic states per factory, as well as
an overall system architecture that interacts with the factories. This yields
between a 10x and 20x resource reduction compared to commonly accepted single
factory designs. Performance is analyzed across representative application
classes such as quantum simulation and quantum chemistry.Comment: 16 pages, 14 figure
Quantum Transpiler Optimization: On the Development, Implementation, and Use of a Quantum Research Testbed
Quantum computing research is at the cusp of a paradigm shift. As the complexity of quantum systems increases, so does the complexity of research procedures for creating and testing layers of the quantum software stack. However, the tools used to perform these tasks have not experienced the increase in capability required to effectively handle the development burdens involved. This case is made particularly clear in the context of IBM QX Transpiler optimization algorithms and functions. IBM QX systems use the Qiskit library to create, transform, and execute quantum circuits. As coherence times and hardware qubit counts increase and qubit topologies become more complex, so does orchestration of qubit mapping and qubit state movement across these topologies. The transpiler framework used to create and test improved algorithms has not kept pace. A testbed is proposed to provide abstractions to create and test transpiler routines. The development process is analyzed and implemented, from design principles through requirements analysis and verification testing. Additionally, limitations of existing transpiler algorithms are identified and initial results are provided that suggest more effective algorithms for qubit mapping and state movement
On the Effect of Quantum Interaction Distance on Quantum Addition Circuits
We investigate the theoretical limits of the effect of the quantum
interaction distance on the speed of exact quantum addition circuits. For this
study, we exploit graph embedding for quantum circuit analysis. We study a
logical mapping of qubits and gates of any -depth quantum adder
circuit for two -qubit registers onto a practical architecture, which limits
interaction distance to the nearest neighbors only and supports only one- and
two-qubit logical gates. Unfortunately, on the chosen -dimensional practical
architecture, we prove that the depth lower bound of any exact quantum addition
circuits is no longer , but . This
result, the first application of graph embedding to quantum circuits and
devices, provides a new tool for compiler development, emphasizes the impact of
quantum computer architecture on performance, and acts as a cautionary note
when evaluating the time performance of quantum algorithms.Comment: accepted for ACM Journal on Emerging Technologies in Computing
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