Quantum Assisted Simulator

Abstract

Quantum simulation can help us study poorly understood topics such as high-temperature superconductivity and drug design. However, existing quantum simulation algorithms for current quantum computers often have drawbacks that impede their application. Here, we provide a novel hybrid quantum-classical algorithm for simulating the dynamics of quantum systems. Our approach takes the Ansatz wavefunction as a linear combination of quantum states. The quantum states are fixed, and the combination parameters are variationally adjusted. Unlike existing variational quantum simulation algorithms, our algorithm does not require any classical-quantum feedback loop and by construction bypasses the barren plateau problem. Moreover, our algorithm does not require any complicated measurements such as the Hadamard test. The entire framework is compatible with existing experimental capabilities and thus can be implemented immediately.

arXiv comment10 pages, 8 figures. arXiv admin note: text overlap with arXiv:2010.05638

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Cited by in corpus (28)

• Noisy intermediate-scale quantum (NISQ) algorithms
• The Variational Quantum Eigensolver: a review of methods and best practices
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Diagnosing Barren Plateaus with Tools from Quantum Optimal Control
• Equivalence of quantum barren plateaus to cost concentration and narrow gorges
• Higher Order Derivatives of Quantum Neural Networks with Barren Plateaus
• An efficient quantum algorithm for the time evolution of parameterized circuits
• Quantum Krylov subspace algorithms for ground and excited state energy estimation
• Theory for Equivariant Quantum Neural Networks
• Neural Predictor based Quantum Architecture Search
• Learning quantum data with the quantum Earth Mover's distance
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model
• Accessing ground state and excited states energies in many-body system after symmetry restoration using quantum computers
• Long-time simulations with high fidelity on quantum hardware
• Probing many-body localization by excited-state VQE
• A semi-agnostic ansatz with variable structure for quantum machine learning
• Generalized Quantum Assisted Simulator
• Noisy intermediate-scale quantum algorithm for semidefinite programming
• Optimal training of variational quantum algorithms without barren plateaus
• Variational dynamics as a ground-state problem on a quantum computer
• Digital Quantum Simulation of the Spin-Boson Model under Open System Dynamics
• Improved variational quantum eigensolver via quasi-dynamical evolution
• Avoiding local minima in Variational Quantum Algorithms with Neural Networks
• Quantum Algorithms for Open Lattice Field Theory
• Co-Design quantum simulation of nanoscale NMR
• Approximate Quantum Compiling for Quantum Simulation: A Tensor Network based approach
• NISQ algorithm for the matrix elements of a generic observable
• Fast-Forwarding with NISQ Processors without Feedback Loop