Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient Quantum Simulation of Chemical Systems

Area of Science:

• Quantum Computing
• Computational Chemistry
• Quantum Algorithms

Background:

• The variational quantum eigensolver (VQE) is a key quantum algorithm for solving the Schrödinger equation.
• VQE performance is critically dependent on the expressibility and efficiency of the wave function ansatz.
• Noisy Intermediate-Scale Quantum (NISQ) computers present challenges due to limited qubit counts and coherence times.

Purpose of the Study:

• To propose a novel, hardware-efficient wave function ansatz for VQE.
• To enhance the expressibility and adaptability of the ansatz across different quantum hardware.
• To improve quantum resource utilization and noise resilience in NISQ computations.

Main Methods:

• Developed a new ansatz inspired by quantum neural network architectures.
• Investigated expressibility by varying circuit depth and width.
• Demonstrated circuit depth reduction using ancilla qubits on superconducting quantum computers.

Main Results:

• The proposed ansatz's expressibility scales with circuit depth or width, offering hardware adaptability.
• Ancilla qubits significantly reduce circuit depth, a critical factor for superconducting hardware.
• The ansatz exhibits increased robustness against noise in practical applications.

Conclusions:

• The new hardware-heuristic ansatz provides a flexible framework for VQE on NISQ devices.
• Ancilla qubit integration enhances efficiency and noise resilience, crucial for practical quantum computation.
• This work paves the way for broader applications of quantum computing in the NISQ era.