1,193 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
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
The next few years will be exciting as prototype universal quantum processors
emerge, enabling implementation of a wider variety of algorithms. Of particular
interest are quantum heuristics, which require experimentation on quantum
hardware for their evaluation, and which have the potential to significantly
expand the breadth of quantum computing applications. A leading candidate is
Farhi et al.'s Quantum Approximate Optimization Algorithm, which alternates
between applying a cost-function-based Hamiltonian and a mixing Hamiltonian.
Here, we extend this framework to allow alternation between more general
families of operators. The essence of this extension, the Quantum Alternating
Operator Ansatz, is the consideration of general parametrized families of
unitaries rather than only those corresponding to the time-evolution under a
fixed local Hamiltonian for a time specified by the parameter. This ansatz
supports the representation of a larger, and potentially more useful, set of
states than the original formulation, with potential long-term impact on a
broad array of application areas. For cases that call for mixing only within a
desired subspace, refocusing on unitaries rather than Hamiltonians enables more
efficiently implementable mixers than was possible in the original framework.
Such mixers are particularly useful for optimization problems with hard
constraints that must always be satisfied, defining a feasible subspace, and
soft constraints whose violation we wish to minimize. More efficient
implementation enables earlier experimental exploration of an alternating
operator approach to a wide variety of approximate optimization, exact
optimization, and sampling problems. Here, we introduce the Quantum Alternating
Operator Ansatz, lay out design criteria for mixing operators, detail mappings
for eight problems, and provide brief descriptions of mappings for diverse
problems.Comment: 51 pages, 2 figures. Revised to match journal pape
Reducing the Compilation Time of Quantum Circuits Using Pre-Compilation on the Gate Level
In order to implement a quantum computing application, problem instances must
be encoded into a quantum circuit and then compiled for a specific platform.
The lengthy compilation process is a key bottleneck in this workflow,
especially for problems that arise repeatedly with a similar yet distinct
structure (each of which requires a new compilation run thus far). In this
paper, we aim to overcome this bottleneck by proposing a comprehensive
pre-compilation technique that tries to minimize the time spent for compiling
recurring problems while preserving the solution quality as much as possible.
The following concepts underpin the proposed approach: Beginning with a problem
class and a corresponding quantum algorithm, a predictive encoding scheme is
applied to encode a representative problem instance into a general-purpose
quantum circuit for that problem class. Once the real problem instance is
known, the previously constructed circuit only needs to be adjusted -- with
(nearly) no compilation necessary. Experimental evaluations on QAOA for the
MaxCut problem as well as a case study involving a satellite mission planning
problem show that the proposed approach significantly reduces the compilation
time by several orders of magnitude compared to Qiskit's compilation schemes
while maintaining comparable compiled circuit quality. All implementations are
available on GitHub (https://github.com/cda-tum/mqt-problemsolver) as part of
the Munich Quantum Toolkit (MQT).Comment: 11 pages, 8 Figures, minor changes, to be published at International
Conference on Quantum Computing and Engineering (QCE), 202
Phase Transitions in Planning Problems: Design and Analysis of Parameterized Families of Hard Planning Problems
There are two common ways to evaluate algorithms: performance on benchmark problems derived from real applications and analysis of performance on parametrized families of problems. The two approaches complement each other, each having its advantages and disadvantages. The planning community has concentrated on the first approach, with few ways of generating parametrized families of hard problems known prior to this work. Our group's main interest is in comparing approaches to solving planning problems using a novel type of computational device - a quantum annealer - to existing state-of-the-art planning algorithms. Because only small-scale quantum annealers are available, we must compare on small problem sizes. Small problems are primarily useful for comparison only if they are instances of parametrized families of problems for which scaling analysis can be done. In this technical report, we discuss our approach to the generation of hard planning problems from classes of well-studied NP-complete problems that map naturally to planning problems or to aspects of planning problems that many practical planning problems share. These problem classes exhibit a phase transition between easy-to-solve and easy-to-show-unsolvable planning problems. The parametrized families of hard planning problems lie at the phase transition. The exponential scaling of hardness with problem size is apparent in these families even at very small problem sizes, thus enabling us to characterize even very small problems as hard. The families we developed will prove generally useful to the planning community in analyzing the performance of planning algorithms, providing a complementary approach to existing evaluation methods. We illustrate the hardness of these problems and their scaling with results on four state-of-the-art planners, observing significant differences between these planners on these problem families. Finally, we describe two general, and quite different, mappings of planning problems to QUBOs, the form of input required for a quantum annealing machine such as the D-Wave II
Quantum Circuit Transformation Based on Simulated Annealing and Heuristic Search
IEEE Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom and arbitrarily long coherence time. In Noisy Intermediate-Scale Quantum (NISQ) devices, however, the number of qubits is highly limited and quantum operation error and qubit coherence are not negligible. Besides, the connectivity of physical qubits in a quantum processing unit (QPU) is also strictly constrained. Thereby, additional operations like SWAP gates have to be inserted to satisfy this constraint while preserving the functionality of the original circuit. This process is known as quantum circuit transformation. Adding additional gates will increase both the size and depth of a quantum circuit and therefore cause further decay of the performance of a quantum circuit. Thus it is crucial to minimize the number of added gates. In this paper, we propose an efficient method to solve this problem. We first choose by using simulated annealing an initial mapping which fits well with the input circuit and then, with the help of a heuristic cost function, stepwise apply the best selected SWAP gates until all quantum gates in the circuit can be executed. Our algorithm runs in time polynomial in all parameters including the size and the qubit number of the input circuit, and the qubit number in the QPU. Its space complexity is quadratic to the number of edges in the QPU. Experimental results on extensive realistic circuits confirm that the proposed method is efficient and the number of added gates of our algorithm is, on average, only 57% of that of state-of-the-art algorithms on IBM Q20 (Tokyo), the most recent IBM quantum device
Quantum Computing Techniques for Multi-Knapsack Problems
Optimization problems are ubiquitous in various industrial settings, and
multi-knapsack optimization is one recurrent task faced daily by several
industries. The advent of quantum computing has opened a new paradigm for
computationally intensive tasks, with promises of delivering better and faster
solutions for specific classes of problems. This work presents a comprehensive
study of quantum computing approaches for multi-knapsack problems, by
investigating some of the most prominent and state-of-the-art quantum
algorithms using different quantum software and hardware tools. The performance
of the quantum approaches is compared for varying hyperparameters. We consider
several gate-based quantum algorithms, such as QAOA and VQE, as well as quantum
annealing, and present an exhaustive study of the solutions and the estimation
of runtimes. Additionally, we analyze the impact of warm-starting QAOA to
understand the reasons for the better performance of this approach. We discuss
the implications of our results in view of utilizing quantum optimization for
industrial applications in the future. In addition to the high demand for
better quantum hardware, our results also emphasize the necessity of more and
better quantum optimization algorithms, especially for multi-knapsack problems.Comment: 20 page
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