Abstract. To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.
Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems. The Ambitious SpacecraftImagine a hypothetical spacecraft enroute to a distant planet. Between propulsion cycles, there are time windows when the craft can be turned for communication and scientific observations. At any given time, the spacecraft has a large set of possible scientific observations that it can perform, each having some value or priority. For each observation, the spacecraft will need to be turned towards the target and the required measurement or exposure taken. Unfortunately, turning to a target is a slow operation that may take up to 30 minutes, depending on the magnitude of the turn. As a result, the choice of experiments and the order in which they are performed has a significant impact on the duration of turns and, therefore, on how much can be accomplished. All this is further complicated by several things:• There is overlap among the capabilities of instruments, so there may be a choice to make for a given observation. Naturally, the different instruments point in different directions, so the choice of instrument influences the direction and duration of the turn.• Instruments must be calibrated before use, which requires turning to one of a number of possible calibration targets. Recalibration is not required if successive observations are made with the same instrument.• Turning uses up limited fuel and observations use power. Power is limited but renewable at a rate that depends on which direction the solar panels are facing.Given all of this, the objective is to maximize scientific return for the mission or at least to use the available time wisely.Of course, this problem is not hypothetical at all. It occurs for space probes like Deep Space One, planetary rovers like Mars Sojourner, space-based observatories like the Hubble Space Telescope, airborne observatories like KAO and SO-FIA, and even automated terrestrial observatories. It is also quite similar to maintenance planning problems, where there may be a cascading set of choices for facilities, tools, and personnel, all of which affect the duration and possible ordering of various repair operations.What makes these problems particularly hard is that they are optimization problems that involve continuous time, resources, metric quantities, and a complex mixture of action choices and ordering decisions. In AI, problems involving choice of actions are often regarded as planning problems. Unfortun...
Numerous automated and semi-automated planning & scheduling systems have been developed for space applications. Most of these systems are model-based in that they encode domain knowledge necessary to predict spacecraft state and resources based on initial conditions and a proposed activity plan. The spacecraft state and resources as often modeled as a series of timelines, with a timeline or set of timelines to represent a state or resource key in the operations of the spacecraft. In this paper, we first describe a basic timeline representation that can represent a set of state, resource, timing, and transition constraints. We describe a number of planning and scheduling systems designed for space applications (and in many cases deployed for use of ongoing missions) and describe how they do and do not map onto this timeline model.
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