2 research outputs found
Symmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet
accurate, force fields for atomistic simulations. This approach has recently
been extended to incorporate quantum computational methods, making use of
variational quantum learning models to predict potential energy surfaces and
atomic forces from ab initio training data. However, the trainability and
scalability of such models are still limited, due to both theoretical and
practical barriers. Inspired by recent developments in geometric classical and
quantum machine learning, here we design quantum neural networks that
explicitly incorporate, as a data-inspired prior, an extensive set of
physically relevant symmetries. We find that our invariant quantum learning
models outperform their more generic counterparts on individual molecules of
growing complexity. Furthermore, we study a water dimer as a minimal example of
a system with multiple components, showcasing the versatility of our proposed
approach and opening the way towards larger simulations. Our results suggest
that molecular force fields generation can significantly profit from leveraging
the framework of geometric quantum machine learning, and that chemical systems
represent, in fact, an interesting and rich playground for the development and
application of advanced quantum machine learning tools.Comment: 12 pages, 8 figure
Analytic Filter-Function Derivatives for Quantum Optimal Control
Auto-correlated noise appears in many solid state qubit systems and hence
needs to be taken into account when developing gate operations for quantum
information processing. However, explicitly simulating this kind of noise is
often less efficient than approximate methods. Here, we focus on the filter
function formalism, which allows the computation of gate fidelities in the
presence of auto-correlated classical noise. Hence, this formalism can be
combined with optimal control algorithms to design control pulses, which
optimally implement quantum gates. To enable the use of gradient-based
algorithms with fast convergence, we present analytically derived filter
function gradients with respect to control pulse amplitudes, and analyze the
computational complexity of our results. When comparing pulse optimization
using our derivatives to a gradient-free approach, we find that the
gradient-based method is roughly two orders of magnitude faster for our test
cases. We also provide a modular computational implementation compatible with
quantum optimal control packages.Comment: Revised arguments in section 7, results unchanged. 13 pages, 7
figures. Open-source software available at
https://github.com/qutech/filter_function