4 research outputs found
Phase transition in Random Circuit Sampling
Quantum computers hold the promise of executing tasks beyond the capability
of classical computers. Noise competes with coherent evolution and destroys
long-range correlations, making it an outstanding challenge to fully leverage
the computation power of near-term quantum processors. We report Random Circuit
Sampling (RCS) experiments where we identify distinct phases driven by the
interplay between quantum dynamics and noise. Using cross-entropy benchmarking,
we observe phase boundaries which can define the computational complexity of
noisy quantum evolution. We conclude by presenting an RCS experiment with 70
qubits at 24 cycles. We estimate the computational cost against improved
classical methods and demonstrate that our experiment is beyond the
capabilities of existing classical supercomputers
Measurement-induced entanglement and teleportation on a noisy quantum processor
Measurement has a special role in quantum theory: by collapsing the
wavefunction it can enable phenomena such as teleportation and thereby alter
the "arrow of time" that constrains unitary evolution. When integrated in
many-body dynamics, measurements can lead to emergent patterns of quantum
information in space-time that go beyond established paradigms for
characterizing phases, either in or out of equilibrium. On present-day NISQ
processors, the experimental realization of this physics is challenging due to
noise, hardware limitations, and the stochastic nature of quantum measurement.
Here we address each of these experimental challenges and investigate
measurement-induced quantum information phases on up to 70 superconducting
qubits. By leveraging the interchangeability of space and time, we use a
duality mapping, to avoid mid-circuit measurement and access different
manifestations of the underlying phases -- from entanglement scaling to
measurement-induced teleportation -- in a unified way. We obtain finite-size
signatures of a phase transition with a decoding protocol that correlates the
experimental measurement record with classical simulation data. The phases
display sharply different sensitivity to noise, which we exploit to turn an
inherent hardware limitation into a useful diagnostic. Our work demonstrates an
approach to realize measurement-induced physics at scales that are at the
limits of current NISQ processors
Accurate and Efficient Parallel Implementation of an Effective Linear-Scaling Direct Random Phase Approximation Method
Recent developments in the PySCF program package
PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. This paper explains the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science. Published under license by AIP Publishing.Peer reviewe