Fast-Forwarding with NISQ Processors without Feedback Loop

TL;DR

This paper introduces the Classical Quantum Fast Forwarding (CQFF) algorithm, a new quantum simulation method for NISQ devices that eliminates feedback loops, improves accuracy, and demonstrates significant performance gains on current quantum hardware.

Contribution

The paper proposes CQFF, an innovative diagonalisation-based quantum simulation algorithm that removes the need for classical-quantum feedback, enhancing practicality and scalability for NISQ devices.

Findings

CQFF achieves a 10^4 performance improvement over previous methods.

CQFF does not require feedback loops or controlled multi-qubit unitaries.

CQFF is experimentally demonstrated on existing quantum processors.

Abstract

Simulating quantum dynamics is expected to be performed more easily on a quantum computer than on a classical computer. However, the currently available quantum devices lack the capability to implement fault-tolerant quantum algorithms for quantum simulation. Hybrid classical quantum algorithms such as the variational quantum algorithms have been proposed to effectively use current term quantum devices. One promising approach to quantum simulation in the noisy intermediate-scale quantum (NISQ) era is the diagonalisation based approach, with some of the promising examples being the subspace Variational Quantum Simulator (SVQS), Variational Fast Forwarding (VFF), fixed-state Variational Fast Forwarding (fs-VFF), and the Variational Hamiltonian Diagonalisation (VHD) algorithms. However, these algorithms require a feedback loop between the classical and quantum computers, which can be a crucial bottleneck in practical application. Here, we present the Classical Quantum Fast Forwarding (CQFF) as an alternative diagonalisation based algorithm for quantum simulation. CQFF shares some similarities with SVQS, VFF, fs-VFF and VHD but removes the need for a classical-quantum feedback loop and controlled multi-qubit unitaries. The CQFF algorithm does not suffer from the barren plateau problem and the accuracy can be systematically increased. Furthermore, if the Hamiltonian to be simulated is expressed as a linear combination of tensored-Pauli matrices, the CQFF algorithm reduces to the task of sampling some many-body quantum state in a set of Pauli-rotated bases, which is easy to do in the NISQ era. We run the CQFF algorithm on existing quantum processors and demonstrate the promise of the CQFF algorithm for current-term quantum hardware. Our work provides a $10^4$ improvement over the previous record.