Currently ongoing

Designing a quantum algorithm is only half the battle; the real challenge lies in execution. While it might seem straightforward, translating a generic quantum algorithm—represented mathematically as a massive unitary matrix—into a sequence of operations that hardware can actually perform is a complex task. This process is known as Quantum Circuit Synthesis, a critical and active area of research. My current work focuses on a specific subset of circuits called CNOT circuits, crucial for building efficient encoders and decoders for Quantum Error Correcting codes.

Once we have successfully synthesized a quantum circuit, the next major hurdle is preparing it for execution on a physical quantum processor. This involves Quantum Mapping and Routing, the process of adapting the abstract, logical circuit to the physical constraints of the hardware. Physical architectures often restrict which qubits can interact directly (connectivity limitations). Mapping involves assigning logical qubits to available physical qubits, while routing inserts necessary SWAP gates to move quantum information around the processor, ensuring that interacting qubits are always physically adjacent. This problem is an NP-hard optimization challenge, as the addition of SWAP gates significantly increases the circuit depth and execution time, directly impacting the final algorithm's fidelity. My line of research is that of tackling these tasks exploiting graph theory and concepts coming from theoretical CS.

Quantum information is incredibly fragile, making it highly susceptible to decoherence and external noise. This necessitates the use of Quantum Error Correction (QEC), a critical field focused on protecting quantum computations from environmental errors. Unlike classical error correction, which uses redundancy to find and flip bad bits, QEC must protect fragile superpositions. It achieves this by encoding one logical qubit into an entangled state distributed across several physical qubits (syndrome measurement). My work involves studying the design and implementation efficiency of some codes derived using graph tools introduced recently by Shor.

The Quantum Approximate Optimization Algorithm (QAOA) is a leading paradigm in the field of variational quantum algorithms, designed to tackle difficult combinatorial optimization problems within the constraints of current quantum hardware (NISQ era). QAOA works by iteratively applying two types of quantum operations: one that encodes the problem we want to solve (the Cost operation) and another that helps explore the possible solutions (the Mixing operation). The influence of these two operations is controlled by a set of parameters that are continually refined by a classical computer running alongside the quantum hardware. My research focuses on the crucial pre-processing step: utilizing advanced tools from classical complexity theory and optimization techniques to map complex problems that contain restrictions or rules (constrained optimization) into the simplified, unrestricted format (unconstrained optimization) required by the QAOA framework. Furthermore, I explore new methods for efficiently refining the control parameters and analyzing the algorithm's guaranteed quality of solution.

If you read my CV, you can notice that I have worked on a variety of topics. I define myself as driven by interest. Any topic that in some sense catches my attention is worth to be investigated. I am curious about any theoretical topic in CS and Quantum Computing, but I do not avoid writing code if it is necessary. Thus, if you have any nice idea you want to discuss, be in touch!