There is a steady growth in research and demand for real quan- tum hardware in the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing. This prompts many third-party cloud providers to set up quantum hardware as a service that includes a wide range of qubit technologies and architectures to maximize performance at minimal cost. Quantum circuits submitted to these services are the fruits of the efforts invested by the designers. But these third-party cloud compilers (who offer more efficient alter- natives to established compilers) can make user circuits/programs vulnerable to various security breaches. Communication channels for transmitting quantum information also need to be developed such that there is end to end encryption. There is little to no visibil- ity on where the execution of the circuit is actually taking place. The success of the user program is highly reliant on the backend that was used for execution. Besides, the third-party provider and/or tools (e.g., hardware mapper and allocator) may be untrustworthy and execute the quantum circuits on less efficient and more error- prone hardware to conserve resources and maximize profit. These vulnerabilities in the quantum system could leak confidential data, cause loss of intellectual property rights of designers, cause loss of access control, corrupt user programs et cetera. Thus, it is imper- ative to identify these security vulnerabilities and create defense strategies to protect quantum programs, thus helping to maintain the integrity of quantum information across the software stack. My thesis aims to help address these vulnerabilities and opacities and address them.

The qubit technologies, basis gate set, noise behavior, speed and coupling architecture are among the various factors that vary among various backends. Although third-party cloud providers offering quantum hardware as a service offer lower cost and flexibility to the users to choose from several qubit technologies, quantum hardware, and coupling maps; the actual execution of the program is not clearly visible to the customer. The success of the user program, in addition to various other metadata such as cost, performance, & number of iterations to converge, depends on the error rate of the backend used. Moreover, the third-party provider and/or tools (e.g., hardware allocator and mapper) may hold insider/outsider adversarial agents to conserve resources and maximize profit by running the quantum circuits on error-prone hardware. Thus it is important to gain visibility of the backend from various perspectives of the computing process e.g., execution, transpilation and outcomes. In this paper, we estimate the error rate of the backend from the original and transpiled circuit. Although many quantum services providers publish the error rates of their backends, we assume that such information may not be accurate and/or correspond to the actual hardware allocated to the program. For the forensics we propose two complementary approaches. First, we exploit the fact that qubit mapping and routing steps of the transpilation process select qubits and qubit pairs with low gate errors to minimize overall error accumulation. We leverage this to rank qubit links into bins and compare with publicly available data we are able to assign a bin rank within a difference of 2 with respect to the actual bin for upto$\mathbf{8 3. 5 \%}$of the qubit links in IBM Sherbrooke and$\mathbf{8 0 \%}$in IBM Brisbane, 127 qubit IBM backends. Second, we derive the error rates of the backends from a pool of programs by solving fidelity equations using numerical nonlinear optimizer. We achieve upto$92.7 \%(97.3 \%)$accuracy for single qubit (2 qubit) gate error rates.