SearcharxivSearch

arXiv · 2505.16889

Quantum-to-classical transition and the emergence of trajectory level Darwinism with measurements distributed in time: a path integral approach

Abstract

We present a formulation for the emergence of classical dynamics in a quantum world using a path integral approach that incorporates continuous measurements. Our approach complements decoherence and coarse-grained quantum-to-classical transition frameworks. The path-integral formulation provides the joint statistics of a sequence of measurements, with each Feynman path picking up an additional random phase. Its magnitude is proportional to the measurement strength, and we give conditions under which the dominant contribution to the probability amplitude comes from trajectories near classical paths. Information proliferates across the environment, a key feature of quantum Darwinism, via plane-wave probe scattering. Extending to repeated measurements, we show that in the continuous limit each system trajectory picks up an additional phase due to momentum kicks from the probes--the origin of the back-action force. We provide conditions under which measurements yield enough ``which-path'' information while keeping the wave packet localized. This allows the quantum-to-classical transition to be described from individual measurement records, complementing the ensemble description from density matrices. We further show that the same scattering that decoheres a trajectory heats it, tying decoherence and measurement back-action together. This bounds how redundantly an individual classical trajectory can be recorded before back-action randomises it into Brownian motion. For a trapped particle, the ceiling is fixed by the resolution measured in units of the zero-point motion. It is not restrictive for macroscopic systems; it collapses to a single record where the semiclassical description of a trajectory fails, delimiting the regime in which objective classical trajectories exist. The deterministic-to-Brownian crossover is accessible in levitated optomechanics.

Explore related subjects

Keep this discovery

BibTeXRIS

Harsh Arora, Bishal Kumar Das, Baladitya Suri, Vaibhav Madhok. 2025-05-22. Quantum-to-classical transition and the emergence of trajectory level Darwinism with measurements distributed in time: a path integral approach. https://arxiv.org/abs/2505.16889

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Probing the Error-Mitigation Threshold with Matrix Product States

Quantum error mitigation relies on accurate noise characterization, but mismatches between the actual and characterized noise can be amplified and drive a sharp threshold between successful and failed mitigation. In random circuits, this threshold maps onto a random-field Ising transition, but previous exact numerics were limited to small one-dimensional and all-to-all systems, leaving explicit two-dimensional architectures unresolved. We develop a fixed-bond-dimension matrix-product-state method for the replicated transfer dynamics that extends threshold calculations beyond exact propagation while retaining the finite-size signatures of the transition. At system sizes beyond previous exact studies, we recover the predicted absence of a threshold for quenched disorder in 1D, obtain a sharper annealed all-to-all critical point, and resolve architecture-dependent finite-depth thresholds in 2D square and heavy-hex circuits. These results establish replicated tensor-network dynamics as a practical tool for probing error-mitigation thresholds in large and higher-dimensional noisy circuits.

quant-ph

Low-cost algorithm-to-execution framework for surface-code quantum computing

The execution of useful quantum algorithms on fault-tolerant processors requires more than a mapping from logical gates to encoded operations: the spatial organization, non-Clifford resource supply, and execution schedule must also be determined while keeping physical overhead within practical limits. Although the theoretical hierarchy from logical circuits to fault-tolerant operations is well established, these implementation choices are often specified and optimized separately. Here we develop a low-cost algorithm-to-execution framework for surface-code quantum computing. From hierarchical algorithm descriptions, it constructs dependency-preserving logical schedules and an executable workload capturing logical interactions, operation parallelism, and time-resolved non-Clifford demand, thereby linking logical computation to surface-code organization, resource-state preparation, and fault-tolerant execution in a traceable workflow. We apply the framework to twenty benchmark circuits across seven algorithm families and a hierarchically composed application-scale elliptic-curve discrete-logarithm workload. Physical costs vary substantially even for circuits with similar logical resource counts. Under our direct-rotation calibration, non-Clifford implementation selection reduces space-time volume by up to 241.5 times versus an all-synthesis baseline for the QAOA amplitude-amplification workload. Circuit-specific surface-code layouts reduce routed-latency estimates for all twenty benchmarks; thirteen also reduce space-time volume because communication savings outweigh added spatial overhead. These results show that low-cost fault-tolerant execution depends on computation scheduling and organization, not aggregate logical resource counts alone.

quant-ph

Sample-optimal learning of stabilizer states

It is well-known that learning a pure $n$-qubit stabilizer state $|\psi\rangle$ both requires, and can be accomplished with, access to a number of copies of $|\psi\rangle$ linear in $n$. However, the precise constant coefficient of this scaling does not appear to have been determined. Here we prove that $L_\delta(n)$, the smallest number of copies from which a quantum procedure can identify any stabilizer state with failure probability at most $0<\delta<1/8$, satisfies $n+\lceil\log_2(1/\delta)\rceil-3\leq L_\delta(n)\leq n+\left\lceil\log_2(1/\delta)\right\rceil+4$. We present a polynomial-time quantum learning algorithm that saturates this bound, achieving a constant factor improvement in sample-complexity over previously known approaches. As an immediate corollary, we obtain via the Choi-Jamiolkowski isomorphism an algorithm for learning an unknown $n$-qubit Clifford unitary from $2n+\left\lceil\log_2(1/\delta)\right\rceil+4$ queries, the $n$-dependence of which we show to be optimal. Our proof technique, which involves Fourier analysis on the abelian group $\mathbb{Z}_4^n \times \mathbb{F}_2^{n(n-1)/2}$, seems to be qualitatively different to previous approaches to stabilizer state learning, and may be of some independent interest; in particular, it admits natural generalisations to further problems in quantum learning theory.

quant-ph