Projects

Searching for optimal quantum embedding using Genetic Algorithm

In contrast to performing optimization algorithms on state preparation circuits, we use Genetic Algorithm to perform search for optimal quantum embedding.

Quantum H/W selection using QuaLITi

Performing hardware configurational analysis using various factors such as qubit coherence times, gate and readout errors, post-transpilation circuit depth to selecting optimal hardware for inferencing QML workloads.

Greedy Parametric Quantum Circuit (PQC) Optimization

A novel greedy algorithm to convert parametric gates in a PQC into equivalent set of non-parametric gates for lower post-transpilation gate count and circuit depth in QML workloads.

Quantum Random Access Memory

Trainable PQC-based QRAM to load and store classical data into quantum Hilbert space.

Quantum Read-Only Memory

Optimized static QROM to read stored classical integer data.

Shot Optimization

Reducing number of shots used in QML algorithms for cost-effective and time-effective training of QML circuits.

Quantum PUF

Quantum hardware identification protocol via cloud service, using special quantum circuits as challenge and noise characteristics as response.