23 research outputs found
Distributionally Robust Variational Quantum Algorithms with Shifted Noise
Given their potential to demonstrate near-term quantum advantage, variational
quantum algorithms (VQAs) have been extensively studied. Although numerous
techniques have been developed for VQA parameter optimization, it remains a
significant challenge. A practical issue is the high sensitivity of quantum
noise to environmental changes, and its propensity to shift in real time. This
presents a critical problem as an optimized VQA ansatz may not perform
effectively under a different noise environment. For the first time, we explore
how to optimize VQA parameters to be robust against unknown shifted noise. We
model the noise level as a random variable with an unknown probability density
function (PDF), and we assume that the PDF may shift within an uncertainty set.
This assumption guides us to formulate a distributionally robust optimization
problem, with the goal of finding parameters that maintain effectiveness under
shifted noise. We utilize a distributionally robust Bayesian optimization
solver for our proposed formulation. This provides numerical evidence in both
the Quantum Approximate Optimization Algorithm (QAOA) and the Variational
Quantum Eigensolver (VQE) with hardware-efficient ansatz, indicating that we
can identify parameters that perform more robustly under shifted noise. We
regard this work as the first step towards improving the reliability of VQAs
influenced by real-time noise.Comment: 11 pages, 8 figure
Distributionally Robust Circuit Design Optimization under Variation Shifts
Due to the significant process variations, designers have to optimize the
statistical performance distribution of nano-scale IC design in most cases.
This problem has been investigated for decades under the formulation of
stochastic optimization, which minimizes the expected value of a performance
metric while assuming that the distribution of process variation is exactly
given. This paper rethinks the variation-aware circuit design optimization from
a new perspective. First, we discuss the variation shift problem, which means
that the actual density function of process variations almost always differs
from the given model and is often unknown. Consequently, we propose to
formulate the variation-aware circuit design optimization as a distributionally
robust optimization problem, which does not require the exact distribution of
process variations. By selecting an appropriate uncertainty set for the
probability density function of process variations, we solve the shift-aware
circuit optimization problem using distributionally robust Bayesian
optimization. This method is validated with both a photonic IC and an
electronics IC. Our optimized circuits show excellent robustness against
variation shifts: the optimized circuit has excellent performance under many
possible distributions of process variations that differ from the given
statistical model. This work has the potential to enable a new research
direction and inspire subsequent research at different levels of the EDA flow
under the setting of variation shift.Comment: accepted by ICCAD 2023, 8 page