Tamal Acharya
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Tamal Acharya has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Akash Kundu
Department of Physics, University of Helsinki, Helsinki, Finland
1 shared publication - Aritra Sarkar
Delft University of Technology
1 shared publication - Bao Gia Bach
Quantum Science and Engineering, University of Delaware
1 shared publication
Latest Publications
- Open Access
Visualizing Quantum Circuit Probability: Estimating Quantum State Complexity for Quantum Program Synthesis
This work applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. The relations among the statistical, algorithmic, computational, and circuit complexities of states are reviewed. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select some characteristic sets. The reachability and expressibility in a space-time-bounded setting for these gate sets are enumerated and visualized. These results are studied in terms of computational resources, universality, and quantum behavior. The article suggests how applications like geometric quantum machine learning, novel quantum algorithm synthesis, and quantum artificial general intelligence can benefit by studying circuit probabilities.
Open AccessVisualizing Quantum Circuit Probability: Estimating Quantum State Complexity for Quantum Program Synthesis
This work applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. The relations among the statistical, algorithmic, computational, and circuit complexities of states are reviewed. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select some characteristic sets. The reachability and expressibility in a space-time-bounded setting for these gate sets are enumerated and visualized. These results are studied in terms of computational resources, universality, and quantum behavior. The article suggests how applications like geometric quantum machine learning, novel quantum algorithm synthesis, and quantum artificial general intelligence can benefit by studying circuit probabilities.