Reducing entanglement with a Hamiltonian derived Clifford transformation
James Brown, Erika Lloyd, Alexandre Fleury, Tarini S Hardikar, Kenny Heitritter
Abstract
Recently (Physica Scripta, 100(10):105401, 2025), an algorithm was introduced that deterministically generates a Clifford transformation from the Qubit Coupled Cluster (QCC) algorithm which we call Q-Cliff (QCC+Clifford). There, it was shown that Q-Cliff could be utilized to generate a hardware efficient version of the QCC ansatz. Here, we examine and refine these techniques and show that Q-Cliff can be utilized to generate efficient classical and quantum approximations to the ground states of chemical systems. The algorithm generates an efficient variational method that generally has accuracy between MP2 and CISD with O(N6). Furthermore, we show through DMRG calculations that the entanglement between qubits is reduced significantly and therefore the accuracy for a given bond dimension can be vastly improved (up to an order of magnitude). Finally, we refine the previously reported algorithm to generate low-depth and CNOT efficient circuits that can be optimized with a comparable number of energy evaluations to state-of-the-art VQE algorithms. All these results show that this Hamiltonian derived Clifford transformation should be a tool used for many classical and quantum algorithms.
Create a lesson
Related papers
Quantum hypothesis testing of non-mixed-unitarity: A multifaceted hierarchy of quantum channel discrimination
Pratik Ghosal, Pritam Halder, Ayan Patra et al.
All Unitaries Have Constant Depth Quantum Circuits
Barak Nehoran, Henry Yuen
On The Simplest Quantum-Secure Block Cipher
Gorjan Alagic, Joseph Carolan, Christian Majenz et al.
Efficient Calculation of Equilibrium Correlation Functions
Yizhi Shen, Roel Van Beeumen, Wibe A. de Jong et al.
Gibbs Sampling in the Shattered Phase by Decoded Quantum Interferometry
Leo Zhou, Noah Shutty, Mark Sellke et al.
Quantum de Finetti theorems for states and channels in any distance measure
Liuhang Ye, Bjarne Bergh, Nilanjana Datta