Quantum Computing

Quantum Machine Learning

Quantum resources that are accessible on the cloud offer us the ability to improve on classical ML techniques with the speed up that is possible when we move to QML. Applications for such improvement occur in 

1. Logistics - vehicle routing, bin packing

2. Finance - anomaly detection, risk analysis, portfolio optimization

3. Biotech - Cancer pathway predictions

4. High Energy Physics - Particle Track Reconstruction

Ising Machines

The Ising Hamiltonian has long been the playground of physicists interested in understanding hysteresis and magnetism. Today, we recognize that NP-Hard problems, such as MaxCut and Number Partitioning, can also be cast into an Ising problem, and that a low energy state of the Ising Hamiltonian will offer us a solution to the NP-Hard problem. This is the approach used by annealers, both classical and quantum. We now build our own photonic annealers that rival the performance of similar commercial solutions.

Sponsors

1. KLA

2. Mphasis

3. LTIMindtree

4. Nagarro

Collaborators

• Tepper Quantum Computing, Carnegie Mellon University

• IBM Research - India

Students, Staff

1. Shreeya (EP20B036)

2. Aditya (EE22M005) - Optimization

3. Manav

4. Sai Sakunthala

Alumni

1. Sai Sakunthala  - Phishing nodes in Ethereum networks

2. Dhruv Gopalakrishnan, Shashank Ravi - Optimization

3. Anfas Nujum K - Cancer pathways

4. Shriram E - Quantum finance

5. Gautham Govind A (YRF'21)

6. Prajwal Prakash (YRF'20)