  The Job shop scheduling problem (JSSP) plays a pivotal role in industrial
applications, such as signal processing (SP) and steel manufacturing, involving
sequencing machines and jobs to maximize scheduling efficiency. Before, JSSP
was solved using manually defined circuits by variational quantum algorithm
(VQA). Finding a good circuit architecture is task-specific and time-consuming.
Differentiable quantum architecture search (DQAS) is a gradient-based framework
that can automatically design circuits. However, DQAS is only tested on quantum
approximate optimization algorithm (QAOA) and error mitigation tasks. Whether
DQAS applies to JSSP based on a more flexible algorithm, such as variational
quantum eigensolver (VQE), is still open for optimization problems. In this
work, we redefine the operation pool and extend DQAS to a framework JSSP-DQAS
by evaluating circuits to generate circuits for JSSP automatically. The
experiments conclude that JSSP-DQAS can automatically find noise-resilient
circuit architectures that perform much better than manually designed circuits.
It helps to improve the efficiency of solving JSSP.
