9,064 research outputs found
Circuit Transformations for Quantum Architectures
Quantum computer architectures impose restrictions on qubit interactions. We propose efficient circuit transformations that modify a given quantum circuit to fit an architecture, allowing for any initial and final mapping of circuit qubits to architecture qubits. To achieve this, we first consider the qubit movement subproblem and use the ROUTING VIA MATCHINGS framework to prove tighter bounds on parallel routing. In practice, we only need to perform partial permutations, so we generalize ROUTING VIA MATCHINGS to that setting. We give new routing procedures for common architecture graphs and for the generalized hierarchical product of graphs, which produces subgraphs of the Cartesian product. Secondly, for serial routing, we consider the TOKEN SWAPPING framework and extend a 4-approximation algorithm for general graphs to support partial permutations. We apply these routing procedures to give several circuit transformations, using various heuristic qubit placement subroutines. We implement these transformations in software and compare their performance for large quantum circuits on grid and modular architectures, identifying strategies that work well in practice
Benchmarking integrated photonic architectures
Photonic platforms represent a promising technology for the realization of
several quantum communication protocols and for experiments of quantum
simulation. Moreover, large-scale integrated interferometers have recently
gained a relevant role for restricted models of quantum computing, specifically
with Boson Sampling devices. Indeed, various linear optical schemes have been
proposed for the implementation of unitary transformations, each one suitable
for a specific task. Notwithstanding, so far a comprehensive analysis of the
state of the art under broader and realistic conditions is still lacking. In
the present work we address this gap, providing in a unified framework a
quantitative comparison of the three main photonic architectures, namely the
ones with triangular and square designs and the so-called fast transformations.
All layouts have been analyzed in presence of losses and imperfect control over
the reflectivities and phases of the inner structure. Our results represent a
further step ahead towards the implementation of quantum information protocols
on large-scale integrated photonic devices.Comment: 10 pages, 6 figures + 2 pages Supplementary Informatio
Optimization of Circuits for IBM's five-qubit Quantum Computers
IBM has made several quantum computers available to researchers around the
world via cloud services. Two architectures with five qubits, one with 16, and
one with 20 qubits are available to run experiments. The IBM architectures
implement gates from the Clifford+T gate library. However, each architecture
only implements a subset of the possible CNOT gates. In this paper, we show how
Clifford+T circuits can efficiently be mapped into the two IBM quantum
computers with 5 qubits. We further present an algorithm and a set of circuit
identities that may be used to optimize the Clifford+T circuits in terms of
gate count and number of levels. It is further shown that the optimized
circuits can considerably reduce the gate count and number of levels and thus
produce results with better fidelity
Continuous-variable quantum neural networks
We introduce a general method for building neural networks on quantum
computers. The quantum neural network is a variational quantum circuit built in
the continuous-variable (CV) architecture, which encodes quantum information in
continuous degrees of freedom such as the amplitudes of the electromagnetic
field. This circuit contains a layered structure of continuously parameterized
gates which is universal for CV quantum computation. Affine transformations and
nonlinear activation functions, two key elements in neural networks, are
enacted in the quantum network using Gaussian and non-Gaussian gates,
respectively. The non-Gaussian gates provide both the nonlinearity and the
universality of the model. Due to the structure of the CV model, the CV quantum
neural network can encode highly nonlinear transformations while remaining
completely unitary. We show how a classical network can be embedded into the
quantum formalism and propose quantum versions of various specialized model
such as convolutional, recurrent, and residual networks. Finally, we present
numerous modeling experiments built with the Strawberry Fields software
library. These experiments, including a classifier for fraud detection, a
network which generates Tetris images, and a hybrid classical-quantum
autoencoder, demonstrate the capability and adaptability of CV quantum neural
networks
Graph-theoretic simplification of quantum circuits with the ZX-calculus
We present a completely new approach to quantum circuit optimisation, based on the ZX-calculus. We first interpret quantum circuits as ZX-diagrams, which provide a flexible, lower-level language for describing quantum computations graphically. Then, using the rules of the ZX-calculus, we give a simplification strategy for ZX-diagrams based on the two graph transformations of local complementation and pivoting and show that the resulting reduced diagram can be transformed back into a quantum circuit. While little is known about extracting circuits from arbitrary ZX-diagrams, we show that the underlying graph of our simplified ZX-diagram always has a graph-theoretic property called generalised flow, which in turn yields a deterministic circuit extraction procedure. For Clifford circuits, this extraction procedure yields a new normal form that is both asymptotically optimal in size and gives a new, smaller upper bound on gate depth for nearest-neighbour architectures. For Clifford+T and more general circuits, our technique enables us to to `see around' gates that obstruct the Clifford structure and produce smaller circuits than naĂŻve `cut-and-resynthesise' methods
A Language and Hardware Independent Approach to Quantum-Classical Computing
Heterogeneous high-performance computing (HPC) systems offer novel
architectures which accelerate specific workloads through judicious use of
specialized coprocessors. A promising architectural approach for future
scientific computations is provided by heterogeneous HPC systems integrating
quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale
ACCelerator) --- a programming model and software framework that enables
quantum acceleration within standard or HPC software workflows. XACC follows a
coprocessor machine model that is independent of the underlying quantum
computing hardware, thereby enabling quantum programs to be defined and
executed on a variety of QPUs types through a unified application programming
interface. Moreover, XACC defines a polymorphic low-level intermediate
representation, and an extensible compiler frontend that enables language
independent quantum programming, thus promoting integration and
interoperability across the quantum programming landscape. In this work we
define the software architecture enabling our hardware and language independent
approach, and demonstrate its usefulness across a range of quantum computing
models through illustrative examples involving the compilation and execution of
gate and annealing-based quantum programs
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