Quantum disentanglement and phase measurements

Ning Bao, Keiichiro Furuya, Gün Süer

We search for efficient disentanglers on random Clifford circuits of two-qubit gates arranged in a brick-wall pattern, using the proximal policy optimization (PPO) algorithm \cite{schulman2017proximalpolicyoptimizationalgorithms}. Disentanglers are defined as a set of projective measurements inserted between consecutive entangling layers. An efficient disentangler is a set of projective measurements that minimize the averaged von Neumann entropy of the final state with the least number of total projections possible. The problem is naturally amenable to reinforcement learning techniques by taking the binary matrix representing the projective measurements along the circuit as our state, and actions as bit flipping operations on this binary matrix that add or delete measurements at specified locations. We give rewards to our agent dependent on the averaged von Neumann entropy of the final state and the configuration of measurements, such that the agent learns the optimal policy that will take him from the initial state of no measurements to the optimal measurement state that minimizes the entanglement entropy. Our results indicate that the number of measurements required to disentangle a random quantum circuit is drastically less than the numerical results of measurement-induced phase transition papers. Additionally, the reinforcement learning procedure enables us to characterize the pattern of optimal disentanglers, which is not possible in the works of measurement-induced phase transitions.

Z. Hradil

Juan Yao

Abstract In this work, we report a novel quantum state reconstruction process based on the disentanglement algorithm. We propose a sequential disentanglement scheme, which can transform an unknown quantum state into a product of computational zero states. The inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequentially disentangling the qubits one by one, we reduce the required measurements with only individual qubit measurement and identify the transformation unitary efficiently. Variational quantum circuit and reinforcement learning methods are used for the quantum circuit design for continuous and discrete quantum gates implementation. Demonstrations with our proposal for the reconstruction of the random states are presented. Our method is universal and imposes no specific ansatz or constraint on the quantum state.

Jaewoo Noh

Leah Henderson

Daniel Ben-Zion, John McGreevy, Tarun Grover

Measurements destroy entanglement. Building on ideas used to study `quantum disentangled liquids,' we explore the use of this effect to characterize states of matter. We focus on systems with multiple components, such as charge and spin in a Hubbard model or local moments and conduction electrons in a Kondo lattice model. In such systems, measurements of (a subset of) one of the components can leave behind a quantum state of the other that is easy to understand, for example in terms of scaling of entanglement entropy of subregions. We bound the outcome of this protocol, for any choice of measurement, in terms of more standard information-theoretic quantities. We apply this quantum disentangling protocol to several problems of physical interest, including gapless topological phases, heavy fermions, and scar states in the Hubbard model.