Description:

Quantum state initiation is very crucial step in a quantum application, since by default qubits are initiated to all-zero state therefore bringing the quantum system to the desired initial state is needed before further processing can be done.

I have worked on a circuit which solves the problem using Mottonen’s algorithm. The Qiskit program accepts number of qubits (=n), & N-dimensional vector (N=2^n) values as input and prepares the quantum state from all-zero state by encoding the vector values into probability amplitudes of the quantum state, after unitary transformation.

The circuit for this uses Mottonen’s algorithm. We know that Mottonen’s algorithm converts an arbitrary state to all-zero state, therefore by reversing the circuit for Mottonen’s algorithm we can convert an all-zero state to the desired state. For this, we initially create & load a staging vector using the input vector elements, and then traverse the vector recursively to construct the circuit.

This circuit which creates arbitrary quantum states using amplitude encoding, has depth = O(N) to load an N-dimensional vector.

The Qiskit program accepts 2 parameters: Number of qubits (n), & vector to be encoded (total N values, where N = 2^n) : Every value is converted to square root of the input value, for further processing.

IBM Qiskit is based on Python. In this session I'll be demonstrating this Python use case for the application showcasing the quantum circuits and results.

Prerequisites:

Basic knowledge of Python. I'll explain related quantum computing principles during my session.

Speaker Info:

Proven track record of establishing business, maintaining profitable growth and continuous improvement.

Highly accomplished track record of consulting, managing offshore practice, program/project management, executing complex projects.

Experience across multiple sectors – Legacy Systems, ERP, Cloud, RPA/AI, Blockchain technologies & Quantum Computing

Completed 7 months CEP program in Quantum Computing & Machine Learning from IIT, Delhi, in June 2024, with 89/100 score. Following topics were part of the curriculum. I implemented most of these in Qiskit:

• Quantum Cryptography
• Quantum Algorithms
• Quantum Machine Learning
• Variational Quantum Eigensolver
• Application of VQE to Quantum Chemistry
• Quantum Max-Cut Graph Clustering
• Quantum Adiabatic Theorem
• Quantum Approximate Optimisation Algorithm (QAOA)
• Financial portfolio optimization

Speaker Links:

https://www.linkedin.com/in/rajesh-sahasrabuddhe-7660411/