188 research outputs found
Interconnection Networks for Scalable Quantum Computers
We show that the problem of communication in a quantum computer reduces to
constructing reliable quantum channels by distributing high-fidelity EPR pairs.
We develop analytical models of the latency, bandwidth, error rate and resource
utilization of such channels, and show that 100s of qubits must be distributed
to accommodate a single data communication. Next, we show that a grid of
teleportation nodes forms a good substrate on which to distribute EPR pairs. We
also explore the control requirements for such a network. Finally, we propose a
specific routing architecture and simulate the communication patterns of the
Quantum Fourier Transform to demonstrate the impact of resource contention.Comment: To appear in International Symposium on Computer Architecture 2006
(ISCA 2006
Anatomy of a message in the Alewife multiprocessor
Shared-memory provides a uniform and attractive mechanism for communication. For efficiency, it is often implemented with a layer of interpretive hardware on top of a message-passing communications network. This interpretive layer is responsible for data location, data movement, and cache coherence. It uses patterns of communication that benefit common programming styles, but which are only heuristics. This suggests that certain styles of communication may benefit from direct access to the underlying communications substrate. The Alewife machine, a shared-memory multiprocessor being built at MIT, provides such an interface. The interface is an integral part of the shared memory implementation and affords direct, user-level access to the network queues, supports an efficient DMA mechanism, and includes fast trap handling for message reception. This paper discusses the design and implementation of the Alewife message-passing interface and addresses the issues and advantages of using such an interface to complement hardware-synthesized shared memory.National Science Foundation (U.S.) (Grant MIP-9012773)United States. Defense Advanced Research Projects Agency (Contract N00014-87-K-0825
Comparing the Overhead of Topological and Concatenated Quantum Error Correction
This work compares the overhead of quantum error correction with concatenated
and topological quantum error-correcting codes. To perform a numerical
analysis, we use the Quantum Resource Estimator Toolbox (QuRE) that we recently
developed. We use QuRE to estimate the number of qubits, quantum gates, and
amount of time needed to factor a 1024-bit number on several candidate quantum
technologies that differ in their clock speed and reliability. We make several
interesting observations. First, topological quantum error correction requires
fewer resources when physical gate error rates are high, white concatenated
codes have smaller overhead for physical gate error rates below approximately
10E-7. Consequently, we show that different error-correcting codes should be
chosen for two of the studied physical quantum technologies - ion traps and
superconducting qubits. Second, we observe that the composition of the
elementary gate types occurring in a typical logical circuit, a fault-tolerant
circuit protected by the surface code, and a fault-tolerant circuit protected
by a concatenated code all differ. This also suggests that choosing the most
appropriate error correction technique depends on the ability of the future
technology to perform specific gates efficiently
Integrated shared-memory and message-passing communication in the Alewife multiprocessor
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 237-246) and index.by John David Kubiatowicz.Ph.D
Improving Quantum Circuit Synthesis with Machine Learning
In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations
of quantum algorithms that minimize the number of expensive and error prone
multi-qubit gates is vital to ensure computations produce meaningful outputs.
Unitary synthesis, the process of finding a quantum circuit that implements
some target unitary matrix, is able to solve this problem optimally in many
cases. However, current bottom-up unitary synthesis algorithms are limited by
their exponentially growing run times. We show how applying machine learning to
unitary datasets permits drastic speedups for synthesis algorithms. This paper
presents QSeed, a seeded synthesis algorithm that employs a learned model to
quickly propose resource efficient circuit implementations of unitaries. QSeed
maintains low gate counts and offers a speedup of in synthesis time
over the state of the art for a 64 qubit modular exponentiation circuit, a core
component in Shor's factoring algorithm. QSeed's performance improvements also
generalize to families of circuits not seen during the training process.Comment: 11 pages, 10 figure
FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity
The Robot Operating System (ROS2) is the most widely used software platform
for building robotics applications. FogROS2 extends ROS2 to allow robots to
access cloud computing on demand. However, ROS2 and FogROS2 assume that all
robots are locally connected and that each robot has full access and control of
the other robots. With applications like distributed multi-robot systems,
remote robot control, and mobile robots, robotics increasingly involves the
global Internet and complex trust management. Existing approaches for
connecting disjoint ROS2 networks lack key features such as security,
compatibility, efficiency, and ease of use. We introduce FogROS2-SGC, an
extension of FogROS2 that can effectively connect robot systems across
different physical locations, networks, and Data Distribution Services (DDS).
With globally unique and location-independent identifiers, FogROS2-SGC securely
and efficiently routes data between robotics components around the globe.
FogROS2-SGC is agnostic to the ROS2 distribution and configuration, is
compatible with non-ROS2 software, and seamlessly extends existing ROS2
applications without any code modification. Experiments suggest FogROS2-SGC is
19x faster than rosbridge (a ROS2 package with comparable features, but lacking
security). We also apply FogROS2-SGC to 4 robots and compute nodes that are
3600km apart. Videos and code are available on the project website
https://sites.google.com/view/fogros2-sgc.Comment: 9 pages, 8 figure
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