The ability to implement high-fidelity quantum circuit analysis rests on both the accurate identification of noise sources and the appropriate choice of noise model. Ideally, the various noise processes contributing to decoherence for different qubit systems can be adapted to a system-agnostic representation. The purpose of this workshop is to discuss noise processes in superconducting, quantum dot, and ion trap qubits with the goal of identifying a Universal Abstract Representation (UAR) that captures the relevant noise parameters common to these three architectures. The parameters of the UAR should be estimable from experimental measurements or high-fidelity numerical simulations of the underlying physical device. Furthermore, the UAR should be designed such that it can be used in a higher-level abstract circuit simulator that can estimate the performance of quantum gate sequences across multiple qubits.

To capture the dynamics of sequence of quantum operations or multi-scale circuit designs, one would like to compute the operation of just the physical gates that are required to implement a universal gate set for quantum computing (e.g. Clifford + T), as well as other operations such as state preparation, measurement, move, swap, etc., and store descriptions of these quantum processes for further analysis. One example of a UAR that captures the performance of a specific time-evolution of a quantum device is a process or chi matrix. Process-matrices are a useful abstraction that can be used for further circuit level analysis. Unfortunately, this procedure of computing process-matrices for just the individual gates needed to compose a universal set does not capture information about all relevant noise sources. One example is the omission of spatial and temporal noise correlations from individual gate-level process matrices, even though these correlations are known to be present in circuits at all scales.

With that background the purpose of this workshop is twofold. First, we wish to identify the prominent noise sources present in superconducting qubits, quantum dots, and ion trap qubits. Second, we wish to identify a UAR that is common to these three devices that can accurately and compactly capture the identified noise sources and be used in a higher-level circuit simulator for analysis of multi-scale quantum error correcting circuits.

In the classical computing arena, efficient techniques for analysis of circuits using model reduction techniques are well-known and used with great success for optimization and verification. In contrast, efficient simulation of arbitrary quantum circuits is not possible on a classical computer, which would seem to preclude the corresponding quantum circuit analysis.

Certain approximations and model reduction strategies do, however, suggest that analysis of a quantum circuits through classical error modeling is, in fact, possible. An example is the well-known Gottesman-Knill theorem that proves one can efficiently simulate quantum stabilizer circuits on a classical computer. This allows one to analyze the efficiency of error correcting codes for simple noise models. In the name of efficiency, however, most methods for analyzing quantum circuits make many approximations that may not be valid in real-world systems. One such example is the omission of spatially and temporally correlated noise. It is known that noise sources with correlated statistics exist in real world systems, and furthermore that such correlations can have degrading effect on error correction techniques, possibly even to the level of preventing fault-tolerant operation. Thus, it would seem that at a minimum correlated noise statistics must be included in a robust circuit analysis scheme. In addition only certain noise models are compatible with stabilizer based simulation techniques and these may not be compatible with realistic noise sources.The purpose of this workshop then is to examine model reduction and error analysis strategies that allow one to go from a low-level, high fidelity physical model of a particular device to a high-level abstract noise model that can be used to efficiently analyze errors in multi-scale quantum circuits. In light of this goal, we will look to discuss methods to accurately simulate quantum circuits on multiple scales with realistic noise models at each scale and the development of reliability metrics for such circuits. Emphasis in both cases will be placed on techniques to quantify the losses in model fidelity as one makes the required approximations to simulate quantum circuits on a classical computer. The ultimate goal of the workshop is to explore the potential for classical error analysis of multi-scale quantum circuits taking into account realistic noise sources and to derive reliability metrics as well as bounds on the accuracy of these metrics.