MétaCan
Menu
Back to cohort
Record W4408690901 · doi:10.1103/prxquantum.6.010356

Quantum Advantage from Measurement-Induced Entanglement in Random Shallow Circuits

2025· article· en· W4408690901 on OpenAlex
Adam Bene Watts, David Gosset, Yinchen Liu, Mehdi Soleimanifar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePRX Quantum · 2025
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaNational Science Foundation
KeywordsQuantum entanglementElectronic circuitQuantum metrologyQuantum sensorPhysicsQuantumComputer scienceQuantum mechanicsStatistical physicsQuantum network

Abstract

fetched live from OpenAlex

We study random constant-depth quantum circuits in a two-dimensional (2D) architecture. While these circuits only produce entanglement between nearby qubits on the lattice, long-range entanglement can be generated by measuring a subset of the qubits of the output state. It is conjectured that this (MIE) proliferates when the circuit depth is at least a constant critical value <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <a:msup> <a:mi>d</a:mi> <a:mo>∗</a:mo> </a:msup> </a:math> . For circuits composed of Haar-random two-qubit gates, it is also believed that this coincides with a in the classical hardness of sampling from the output distribution. Here, we provide evidence for a quantum advantage phase transition in the setting of random circuits. Our work extends the scope of recent separations between the computational power of constant-depth quantum and classical circuits, demonstrating that this kind of advantage is present in canonical random circuit sampling tasks. In particular, we show that in any architecture of random shallow Clifford circuits, the presence of long-range MIE gives rise to an unconditional quantum advantage. In contrast, any depth- <d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <d:mi>d</d:mi> </d:math> 2D quantum circuit that satisfies a short-range MIE property can be classically simulated efficiently and with depth <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <g:mi>O</g:mi> <g:mo stretchy="false">(</g:mo> <g:mi>d</g:mi> <g:mo stretchy="false">)</g:mo> </g:math> . Finally, we introduce a 2D depth- <l:math xmlns:l="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <l:mn>2</l:mn> </l:math> “coarse-grained” circuit architecture, composed of random Clifford gates acting on <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <o:mi>O</o:mi> <o:mo stretchy="false">(</o:mo> <o:mi>log</o:mi> <o:mo></o:mo> <o:mo stretchy="false">(</o:mo> <o:mi>n</o:mi> <o:mo stretchy="false">)</o:mo> <o:mo stretchy="false">)</o:mo> </o:math> qubits, for which we prove the existence of long-range MIE and establish an unconditional quantum advantage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.256
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it