Efficient Tensor-Network Simulations of Weakly-Measured Quantum Circuits

ORAL

Abstract

We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the tensor network, propagating their effect forward along the spatial direction. As a demonstration of our algorithm, we consider a (1+1)-dimensional brickwall circuit of Haar-random unitaries, interspersed with generalized single-qubit measurements of variable strength. We simulate the dynamics for tens to hundreds of qubits if the circuit exhibits area-law entanglement (under strong measurements), and tens of qubits if it exhibits volume-law entanglement (under weak measurements). We observe signatures of a measurement-induced phase transition between the two regimes as a function of measurement strength.

*D.P. acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) (Ref. No. PGSD-567963-2022). L.B. acknowledges financial support from: PNRR Ministero Università e Ricerca Project No. PE0000023-NQSTI, funded by European Union-Next-Generation EU; Prin 2022 - DD N. 104 del 2/2/2022, entitled "understanding the LEarning process of QUantum Neural networks (LeQun)", proposal code 2022WHZ5XH, CUP B53D23009530006; the European Union's Horizon Europe research and innovation program under EPIQUE Project GA No. 101135288.

Publication: arXiv:2510.07211

Presenters

  • Darren Pereira

    • Cornell University

Authors

  • Darren Pereira

    • Cornell University
  • Leonardo Banchi

    • University of Florence