3,226 research outputs found
Programming Quantum Computers Using Design Automation
Recent developments in quantum hardware indicate that systems featuring more
than 50 physical qubits are within reach. At this scale, classical simulation
will no longer be feasible and there is a possibility that such quantum devices
may outperform even classical supercomputers at certain tasks. With the rapid
growth of qubit numbers and coherence times comes the increasingly difficult
challenge of quantum program compilation. This entails the translation of a
high-level description of a quantum algorithm to hardware-specific low-level
operations which can be carried out by the quantum device. Some parts of the
calculation may still be performed manually due to the lack of efficient
methods. This, in turn, may lead to a design gap, which will prevent the
programming of a quantum computer. In this paper, we discuss the challenges in
fully-automatic quantum compilation. We motivate directions for future research
to tackle these challenges. Yet, with the algorithms and approaches that exist
today, we demonstrate how to automatically perform the quantum programming flow
from algorithm to a physical quantum computer for a simple algorithmic
benchmark, namely the hidden shift problem. We present and use two tool flows
which invoke RevKit. One which is based on ProjectQ and which targets the IBM
Quantum Experience or a local simulator, and one which is based on Microsoft's
quantum programming language Q.Comment: 10 pages, 10 figures. To appear in: Proceedings of Design, Automation
and Test in Europe (DATE 2018
2D Qubit Placement of Quantum Circuits using LONGPATH
In order to achieve speedup over conventional classical computing for finding
solution of computationally hard problems, quantum computing was introduced.
Quantum algorithms can be simulated in a pseudo quantum environment, but
implementation involves realization of quantum circuits through physical
synthesis of quantum gates. This requires decomposition of complex quantum
gates into a cascade of simple one qubit and two qubit gates. The
methodological framework for physical synthesis imposes a constraint regarding
placement of operands (qubits) and operators. If physical qubits can be placed
on a grid, where each node of the grid represents a qubit then quantum gates
can only be operated on adjacent qubits, otherwise SWAP gates must be inserted
to convert non-Linear Nearest Neighbor architecture to Linear Nearest Neighbor
architecture. Insertion of SWAP gates should be made optimal to reduce
cumulative cost of physical implementation. A schedule layout generation is
required for placement and routing apriori to actual implementation. In this
paper, two algorithms are proposed to optimize the number of SWAP gates in any
arbitrary quantum circuit. The first algorithm is intended to start with
generation of an interaction graph followed by finding the longest path
starting from the node with maximum degree. The second algorithm optimizes the
number of SWAP gates between any pair of non-neighbouring qubits. Our proposed
approach has a significant reduction in number of SWAP gates in 1D and 2D NTC
architecture.Comment: Advanced Computing and Systems for Security, SpringerLink, Volume 1
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Compiler Optimization for Quantum Computing Using Reinforcement Learning
Any quantum computing application, once encoded as a quantum circuit, must be
compiled before being executable on a quantum computer. Similar to classical
compilation, quantum compilation is a sequential process with many compilation
steps and numerous possible optimization passes. Despite the similarities, the
development of compilers for quantum computing is still in its infancy-lacking
mutual consolidation on the best sequence of passes, compatibility,
adaptability, and flexibility. In this work, we take advantage of decades of
classical compiler optimization and propose a reinforcement learning framework
for developing optimized quantum circuit compilation flows. Through distinct
constraints and a unifying interface, the framework supports the combination of
techniques from different compilers and optimization tools in a single
compilation flow. Experimental evaluations show that the proposed framework-set
up with a selection of compilation passes from IBM's Qiskit and Quantinuum's
TKET-significantly outperforms both individual compilers in over 70% of cases
regarding the expected fidelity. The framework is available on GitHub
(https://github.com/cda-tum/MQTPredictor).Comment: 6 pages, 3 figure
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