439 research outputs found
Exploiting Quantum Teleportation in Quantum Circuit Mapping
Quantum computers are constantly growing in their number of qubits, but
continue to suffer from restrictions such as the limited pairs of qubits that
may interact with each other. Thus far, this problem is addressed by mapping
and moving qubits to suitable positions for the interaction (known as quantum
circuit mapping). However, this movement requires additional gates to be
incorporated into the circuit, whose number should be kept as small as possible
since each gate increases the likelihood of errors and decoherence.
State-of-the-art mapping methods utilize swapping and bridging to move the
qubits along the static paths of the coupling map---solving this problem
without exploiting all means the quantum domain has to offer. In this paper, we
propose to additionally exploit quantum teleportation as a possible
complementary method. Quantum teleportation conceptually allows to move the
state of a qubit over arbitrary long distances with constant
overhead---providing the potential of determining cheaper mappings. The
potential is demonstrated by a case study on the IBM Q Tokyo architecture which
already shows promising improvements. With the emergence of larger quantum
computing architectures, quantum teleportation will become more effective in
generating cheaper mappings.Comment: To appear in ASP-DAC 202
Deep neural networks for quantum circuit mapping
AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy
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