Quantum Tensor Engine

Introduction

Quantum computers harness fundamental aspects of quantum behavior to drive exponential increases in the speed with which certain computations can be performed. They have potentially a tremendous long-term impact in areas such as quantum many-body physics and material science, and further afield in machine learning.

Quantum Many-Body Problems

The quantum many-body problems studied by condensed matter physicists are perhaps the most likely to yield early demonstrations of this potential. However, current and near-term intermediate-scale quantum (NISQ) devices are limited in the number of operations that they can carry out before their performance is degraded by interactions with the environment.

Need for Efficient Algorithms

To take advantage of these platforms and to outperform classical computers, highly efficient and specialized quantum algorithms are required. The implementation and benchmarking of these basic algorithms on different quantum computing platforms is challenging and requires a detailed knowledge of the underlying physics.

Our Approach

Our approach is to produce a ready-to-use, highly innovative software package based upon quantum tensor networks.

Quantum Tensor Engine (QTEngine)

The Quantum Tensor Engine (QTEngine) will provide a unifying framework for both quantum and classical algorithms. The QTEngine will serve as an engine to drive fast and easy implementation of:

1. Quantum simulation
2. Quantum machine learning
3. Optimization algorithms

Target User Base

The anticipated user base includes academic groups as well as commercial research and development groups.