Abstract
We present a composable design scheme for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. Our object-oriented approach is based on constructing an expressive set of common data structures and methods that enable programming of a broad variety of complex hybrid quantum simulation applications. The abstract core of our scheme is distilled from the analysis of the current quantum simulation algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library. To validate our implementation, we test and show its utility on commercial quantum processors from IBM and Rigetti, running some prototypical quantum simulations.
Highlights
Quantum simulation is an important use case of quantum computing for scientific computing applications
There, we show the energy as a function of the quantum circuits used in the learning process with a budget of 200 function evaluations with 5000 shots per evaluation by using the QCOR’s variational quantum eigensolver (VQE) module, Quantum Simulation Modeling (QuaSiMo).getWorkflow(’vqe’), and by using the Qiskit class aqua.algorithms.VQE [19]
To compute the ground-state energy of an arbitrary Hamiltonian, in addition to VQE as we have demonstrated in 3.2, there is another algorithm, so-called Quantum Imaginary Time Evolution (QITE) [18, 50], which does not require the use of an ansatz nor an optimizer
Summary
Quantum simulation is an important use case of quantum computing for scientific computing applications. A common, reusable and extensible programming workflow for quantum simulation would enable broader adoption of these applications and support more robust testing by the quantum computing community. Our approach constructs common data structures and methods to program varying quantum simulation applications, and we leverage the hardware-agnostic language QCOR and programming framework XACC to implement these ideas. We demonstrate these methods with example applications from materials science and chemistry, and we discuss how to extend these workflows to experimental validation of quantum computation advantage, in which numerical simulations can benchmark programs for small-sized models [12, 23,24,25,26]. Rather than requiring new extensions being imported, QuaSiMo puts forward a common interface for all of its extension points [29], and enables the development of portable user applications w.r.t. the underlying library implementation
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