Practical applications of quantum computing depend on fault-tolerant devices that use error correction. A promising quantum error correcting code for large-scale quantum computing is the surface code. For this code, Fault-Tolerant Quantum Computing (FTQC) can be performed via lattice surgery, i.e. merging and splitting of encoded qubit patches on a 2D grid. Lattice surgery related operations result in space-time patterns of activity that are defined in this work as {\em access traces}. This work demonstrates that the {\em access traces} reveal when, where, and how logical qubits interact. Leveraging this formulation, this work further introduces TraceQ, a trace-based reconstruction framework that is able to reconstruct the quantum circuit dataflow just by observing the patch activity at each trace entry. The framework is supported by heuristics for handling inherent ambiguity in the traces, and demonstrates its effectiveness on a range of synthetic fault-tolerant quantum benchmarks. The {\em access traces} can have applications in a wide range of scenarios, enabling analysis and profiling of execution of quantum programs and the hardware they run on. As one example use of TraceQ, this work investigates whether such traces, even in a form that contains the least information (only whether a patch is active or not at a given time) can act as a side channel through which an observer can recover the circuit’s logical dataflow and identify known subroutines in a larger program, or even whole large programs. The findings show that indeed the minimal {\em access traces} can be used to recover subroutines or even whole quantum programs with very high accuracy. Only a single trace per program execution is needed and the processing can be done fully offline. Along with the custom heuristics, advanced subgraph matching algorithms used in this work enable a high rate of locating the subroutines while executing in minimal time.