22 research outputs found
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
Benchmarking treewidth as a practical component of tensor-network--based quantum simulation
Tensor networks are powerful factorization techniques which reduce resource
requirements for numerically simulating principal quantum many-body systems and
algorithms. The computational complexity of a tensor network simulation depends
on the tensor ranks and the order in which they are contracted. Unfortunately,
computing optimal contraction sequences (orderings) in general is known to be a
computationally difficult (NP-complete) task. In 2005, Markov and Shi showed
that optimal contraction sequences correspond to optimal (minimum width) tree
decompositions of a tensor network's line graph, relating the contraction
sequence problem to a rich literature in structural graph theory. While
treewidth-based methods have largely been ignored in favor of dataset-specific
algorithms in the prior tensor networks literature, we demonstrate their
practical relevance for problems arising from two distinct methods used in
quantum simulation: multi-scale entanglement renormalization ansatz (MERA)
datasets and quantum circuits generated by the quantum approximate optimization
algorithm (QAOA). We exhibit multiple regimes where treewidth-based algorithms
outperform domain-specific algorithms, while demonstrating that the optimal
choice of algorithm has a complex dependence on the network density, expected
contraction complexity, and user run time requirements. We further provide an
open source software framework designed with an emphasis on accessibility and
extendability, enabling replicable experimental evaluations and future
exploration of competing methods by practitioners.Comment: Open source code availabl
Digital-Analog Quantum Simulations Using The Cross-Resonance Effect
Digital-analog quantum computation aims to reduce the currently infeasible
resource requirements needed for near-term quantum information processing by
replacing sequences of one- and two-qubit gates with a unitary transformation
generated by the systems' underlying Hamiltonian. Inspired by this paradigm, we
consider superconducting architectures and extend the cross-resonance effect,
up to first order in perturbation theory, from a two-qubit interaction to an
analog Hamiltonian acting on 1D chains and 2D square lattices which, in an
appropriate reference frame, results in a purely two-local Hamiltonian. By
augmenting the analog Hamiltonian dynamics with single-qubit gates we show how
one may generate a larger variety of distinct analog Hamiltonians. We then
synthesize unitary sequences, in which we toggle between the various analog
Hamiltonians as needed, simulating the dynamics of Ising, , and Heisenberg
spin models. Our dynamics simulations are Trotter error-free for the Ising and
models in 1D. We also show that the Trotter errors for 2D and 1D
Heisenberg chains are reduced, with respect to a digital decomposition, by a
constant factor. In order to realize these important near-term speedups, we
discuss the practical considerations needed to accurately characterize and
calibrate our analog Hamiltonians for use in quantum simulations. We conclude
with a discussion of how the Hamiltonian toggling techniques could be extended
to derive new analog Hamiltonians which may be of use in more complex
digital-analog quantum simulations for various models of interacting spins