Fast Quantum Many Body State Synthesis

2025/11/17 by Tiwari, Prashasti, Dylan Lewis, Lewis, Dylan +2
Computer Science · Physics and Astronomy · #Cold Atom Physics and Bose-Einstein Condensates #FOS: Physical sciences #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum many-body systems

paper · pdf · doi:10.48550/arxiv.2511.12923

openalex publication_date 2025/11/17 · openalex created_date 2025/11/19 · openalex updated_date 2026/07/28

Abstract

Quantum Mechanical ground states of many-body systems can be important resources for various investigations: for quantum sensing, as the initial state for nonequilibrium quantum dynamics following quenches, and the simulation of quantum processes that start by coupling systems in ground states, eg, could be a process in quantum chemistry. However, to prepare ground states can be challenging; for example, requires adiabatic switching of Hamiltonian terms slower than an inverse gap, which can be time consuming and bring in decoherence. Here we investigate the possibility of preparing a many-body entangled ground state of a certain Hamiltonian, which can be called a quantum ``problem'' Hamiltonian, using the time evolution of an initial fiducial state by another ``solver'' Hamiltonian/s for a very short fixed (unit) time. The parameters of the solver Hamiltonian are optimised classically using energy minimisation as the cost function. We present a study of up to n=10 qubit many-body states prepared using this methodology.

Citations

• High-Fidelity Scalable Quantum State Preparation via the Fusion Method
• Undecidable problems associated with variational quantum algorithms
• Scalability Challenges in Variational Quantum Optimization under Stochastic Noise
• Variational preparation of normal matrix product states on quantum computers
• Probing the limits of variational quantum algorithms for nonlinear ground states on real quantum hardware: The effects of noise
• Exploiting symmetries in nuclear Hamiltonians for ground state preparation
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• Matrix Product State Pre-Training for Quantum Machine Learning
• Machine learning design of a trapped-ion quantum spin simulator
• Quantum phase transition from a superfluid to a Mott insulator in a gas of ultracold atoms
• Simulating physics with computers

Related

• Learning State Preparation Circuits for Quantum Phases of Matter
• Faster ground state preparation and high-precision ground energy estimation with fewer qubits
• Preparation of Entangled Many-Body States with Machine Learning
• Quantum state preparation with polynomial resources: Branched-Subspaces Adiabatic Preparation (B-SAP)
• A penalty-free quantum algorithm to find energy eigenstates