13 research outputs found
Chemical basis of Trotter-Suzuki errors in quantum chemistry simulation
Although the simulation of quantum chemistry is one of the most anticipated applications of quantum computing, the scaling of known upper bounds on the complexity of these algorithms is daunting. Prior work has bounded errors due to discretization of the time evolution (known as “Trotterization”) in terms of the norm of the error operator and analyzed scaling with respect to the number of spin orbitals. However, we find that these error bounds can be loose by up to 16 orders of magnitude for some molecules. Furthermore, numerical results for small systems fail to reveal any clear correlation between ground-state error and number of spin orbitals. We instead argue that chemical properties, such as the maximum nuclear charge in a molecule and the filling fraction of orbitals, can be decisive for determining the cost of a quantum simulation. Our analysis motivates several strategies to use classical processing to further reduce the required Trotter step size and estimate the necessary number of steps, without requiring additional quantum resources. Finally, we demonstrate improved methods for state preparation techniques which are asymptotically superior to proposals in the simulation literature.Chemistry and Chemical Biolog
High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation
Quantum chemical calculations on atomistic systems have evolved into a
standard approach to study molecular matter. These calculations often involve a
significant amount of manual input and expertise although most of this effort
could be automated, which would alleviate the need for expertise in software
and hardware accessibility. Here, we present the AutoRXN workflow, an automated
workflow for exploratory high-throughput lectronic structure calculations of
molecular systems, in which (i) density functional theory methods are exploited
to deliver minimum and transition-state structures and corresponding energies
and properties, (ii) coupled cluster calculations are then launched for
optimized structures to provide more accurate energy and property estimates,
and (iii) multi-reference diagnostics are evaluated to back check the coupled
cluster results and subject hem to automated multi-configurational calculations
for potential multi-configurational cases. All calculations are carried out in
a cloud environment and support massive computational campaigns. Key features
of all omponents of the AutoRXN workflow are autonomy, stability, and minimum
operator interference. We highlight the AutoRXN workflow at the example of an
autonomous reaction mechanism exploration of the mode of action of a
homogeneous catalyst for the asymmetric reduction of ketones.Comment: 29 pages, 11 figure
On the Principles of Differentiable Quantum Programming Languages
Variational Quantum Circuits (VQCs), or the so-called quantum
neural-networks, are predicted to be one of the most important near-term
quantum applications, not only because of their similar promises as classical
neural-networks, but also because of their feasibility on near-term noisy
intermediate-size quantum (NISQ) machines. The need for gradient information in
the training procedure of VQC applications has stimulated the development of
auto-differentiation techniques for quantum circuits. We propose the first
formalization of this technique, not only in the context of quantum circuits
but also for imperative quantum programs (e.g., with controls), inspired by the
success of differentiable programming languages in classical machine learning.
In particular, we overcome a few unique difficulties caused by exotic quantum
features (such as quantum no-cloning) and provide a rigorous formulation of
differentiation applied to bounded-loop imperative quantum programs, its
code-transformation rules, as well as a sound logic to reason about their
correctness. Moreover, we have implemented our code transformation in OCaml and
demonstrated the resource-efficiency of our scheme both analytically and
empirically. We also conduct a case study of training a VQC instance with
controls, which shows the advantage of our scheme over existing
auto-differentiation for quantum circuits without controls.Comment: Codes are available at https://github.com/LibertasSpZ/adcompil
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