Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2608.12255

Conditional dependence and Scrooge ensembles in shallow random quantum circuits

Yinchen Liu, Max McGinley, Thomas Schuster, David Gosset

quant-phcond-mat.stat-mechSubmitted Aug 12, 2026• Announced Aug 13, 2026• 0 citations
arXivHTMLPDF

Abstract

The output state of a 2D geometrically local shallow random quantum circuit does not have long range correlations due to its lightcone structure. But this changes if one measures a subset of the qubits: long-range entanglement can be induced by the measurement process, leading to conditional correlations between distant qubits. In this paper we investigate the structure of conditional dependence in these circuits and its consequences for quantum advantage. For a tripartition $ABC$ of the qubits, we consider the ensemble of post-measurement states on $A$ that is conditioned on a specific measurement outcome on $B$ and ranges over all possible measurement outcomes on $C$. For circuit depths exceeding a constant critical value $d^*$, we conjecture that this ensemble is well approximated by a certain generalization of the Haar ensemble, called the Scrooge ensemble~[Jozsa \textit{et al.}, \href{https://doi.org/10.1103/PhysRevA.49.668}{Phys. Rev. A \textbf{49}, 668 (1994)}]; we also provide supporting numerical and analytical evidence. Our conjecture describes a precise sense in which the state retains its lightcone structure on the remaining unmeasured qubits, but also develops some globally random features arising from the measurement. A consequence is that $n$-qubit shallow random quantum circuits in two dimensions are classically efficiently simulable in the presence of a tiny depolarizing noise rate $Ω(\log(n)/n)$.

Related papers

Quantum Advantage from Measurement-Induced Entanglement in Random Shallow Circuits

Adam Bene Watts, David Gosset, Yinchen Liu +1 more

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 d ∗ . 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 2D quantum circuit that satisfies a short-range MIE property can be classically simulated efficiently and with depth O ( d ) . Finally, we introduce a 2D depth- 2 “coarse-grained” circuit architecture, composed of random Clifford gates acting on O ( log  ( n ) ) qubits, for which we prove the existence of long-range MIE and establish an unconditional quantum advantage.

arXiv:2407.21203•PRX Quantum•Mar 21, 2025•6 citations

Efficient Classical Simulation of Random Shallow 2D Quantum Circuits

John C. Napp, Rolando L. La Placa, Alexander M. Dalzell +2 more

A central question of quantum computing is determining the source of the advantage of quantum computation over classical computation.Even though simulating quantum dynamics on a classical computer is thought to require exponential overhead in the worst case, efficient simulations are known to exist in several special cases.It was widely assumed that these easy-to-simulate cases as well as any yetundiscovered ones could be avoided by choosing a quantum circuit at random.We prove that this intuition is false by showing that certain families of constant-depth, 2D random circuits can be approximately simulated on a classical computer in time only linear in the number of qubits and gates, even though the same families are capable of universal quantum computation and are hard to exactly simulate in the worst case (under standard hardness assumptions).While our proof applies to specific random circuit families, we demonstrate numerically that typical instances of more general families of sufficiently shallow constantdepth 2D random circuits are also efficiently simulable.We propose two classical simulation algorithms.One is based on first simulating spatially local regions which are then "stitched" together via recovery maps.The other reduces the 2D simulation problem to a problem of simulating a form of 1D dynamics consisting of alternating rounds of random local unitaries and weak measurements.Similar processes have recently been the subject of an intensive research focus, which has observed that the dynamics generally undergo a phase transition from a low-entanglement (and efficient-to-simulate) regime to a highentanglement (and inefficient-to-simulate) regime as measurement strength is varied.Via a mapping from random quantum circuits to classical statistical mechanical models, we give analytical evidence that a similar computational phase transition occurs for both of our algorithms as parameters of the circuit architecture like the local Hilbert space dimension and circuit depth are varied and, additionally, that the effective 1D dynamics corresponding to sufficiently shallow random quantum circuits falls within the efficient-to-simulate regime.Implementing the latter algorithm for the depth-3 "brickwork" architecture, for which exact simulation is hard, we find that a laptop could simulate typical instances on a 409 × 409 grid with a total variation distance error less than 0.01 in approximately one minute per sample, a task intractable for previously known circuit simulation algorithms.Numerical results support our analytic evidence that the algorithm is asymptotically efficient.

arXiv:2001.00021•Physical Review X•Apr 27, 2022•140 citations

Parallel classical simulation of noisy shallow circuits: no quantum advantage in 1D

Robert Koenig, Marco Tomamichel

We consider quantum circuits consisting of $d$ layers of nearest-neighbor two-qubit gates acting on $n$ qubits arranged on a line, where every qubit is independently depolarized with a constant probability before each layer. We describe a randomized parallel algorithm which samples from the output distribution of any such circuit to within total variation error $δ$, with parallel runtime $2^{O(d)}\log\log(n/δ)$ and $n 2^{O(d)}$ elementary real-arithmetic operations. Without noise, the same approach gives an exact sampler with parallel runtime $O(\log n)$. For constant depth, we further show that the input/output behavior of the noisy quantum circuit is reproduced up to a constant error by a randomized $\mathsf{AC}^0$-circuit, that is, a Boolean circuit of polynomial size and constant depth with unbounded fan-in AND and OR gates and NOT gates. Consequently, every relation problem solved by a noisy constant-depth quantum circuit in one dimension is also solved, with essentially the same success probability, by a randomized $\mathsf{AC}^0$-circuit. This rules out, for noisy circuits in one dimension, the unconditional quantum advantage established for noisy shallow circuits in two and three dimensions, where polynomial-size classical circuits over the same gate set require depth $Ω(\log n/\log\log n)$. Our algorithm exploits the fact that depolarizing noise cuts a one-dimensional circuit into independent pieces of logarithmic width, each of which can be sampled exactly in parallel.

quant-pharXiv:2609.39448•Oct 1, 2026•0 citations

Limitations of Noisy Geometrically Local Quantum Circuits

Jon Nelson, Joel Rajakumar, Michael J. Gullans

Quantum circuits with a constant rate of depolarizing noise per qubit per time step are known to converge to the uniform distribution at depth $ω(p^{-1}\log n)$, and hence become trivially classically simulable by uniform sampling. We show that under the physically natural constraint of geometric locality, noisy circuits become classically simulable at shallower depths by substantially more structured classical algorithms. We consider arbitrary geometrically local quantum circuits on $n$ qubits, initialized in an arbitrary product state, with nearest-neighbor gates in $O(1)$ spatial dimensions and interspersed depolarizing noise of any constant strength $p$. Our first result is that when the depth exceeds a threshold $d^*=Θ(p^{-1}\log n)$, the output distribution can be approximately sampled in quasipolynomial time. This gives a worst-case simulability result for noisy geometrically local circuits, and matches in its $n$-dependence the best previously known results for noisy random circuits. The proof is based on new information-theoretic bounds showing that in geometrically local noisy circuits, local regions lose correlations and independently converge to maximally mixed strictly before the entire system converges to the uniform distribution. We further prove structural results suggesting a sharper transition at depth $\tildeΘ(p^{-1})$: after coarse-graining the lattice, Pauli weight supported on long connected paths can be truncated with exponentially small error. These results provide evidence for a percolation-like mechanism behind classical simulability at constant depth, and motivate our conjecture that all noisy geometrically local circuits admit quasipolynomial-time approximate sampling once circuit depth exceeds $\tildeΘ(p^{-1})$.

arXiv:2510.06346•Oct 7, 2025•0 citations

Quantum Advantage with Adaptive Shallow Circuits

Yusen Wu, Yukun Zhang, XIaoming Zhang +2 more

Quantum advantage is widely expected to require sufficiently deep circuits, where correlations and global computational structure can grow beyond the reach of efficient classical simulation. This expectation is especially stark for constant-depth circuits with local readout: the expectation value of any fixed local observable lies within a bounded backward lightcone and is therefore classically tractable. Here we show that measurement feedback changes this picture. We establish a strict hierarchy of computational power: at fixed coherent depth, increasing the number of feedback outcomes strictly enlarges the class of functions accessible through a local expectation value. The two ends of this hierarchy exhibit distinct computational regimes. With logarithmic feedback, local expectation values for product-state inputs are efficiently classically simulable. Polynomial feedback, by contrast, enables an explicit family of adaptive shallow circuits to encode prime-field discrete logarithm problem~(DLP) into a fixed single-qubit expectation. Assuming the standard worst-case classical hardness of DLP, estimating this expectation value is classically hard. These results reveal a feedback-driven complexity transition, with further implications for resource lower bounds on DLP and the complexity of local-observable estimation under area-law entanglement. Our results open a new route to quantum advantage with shallow quantum circuits.

quant-pharXiv:2608.15545•Aug 18, 2026•0 citations

Shallow randomized measurement in noisy quantum devices

Gyungmin Cho, Dohun Kim

Quantum hardware is steadily improving, but near-term quantum devices remain limited by noise and circuit depth. This motivates measurement protocols that can use shallow-depth circuits while remaining robust to experimental errors. In this work, we study the advantages of shallow randomized measurements over non-entangling single-qubit measurements for learning properties of quantum states. Although shallow measurements have shown advantages in selected applications, their usefulness across different learning tasks has not been systematically understood. Here, we develop a theoretical framework based on Clifford ensembles that incorporates shallow measurements into derandomized measurements, multi-shot protocols, common randomized measurements, error-mitigated estimators, and hybrid quantum-classical learning. Finally, we validate these results on IBM quantum hardware in experiments with up to 40 qubits and 46 layers of two-qubit gates. These results indicate that shallow-depth measurements can provide practical benefits on noisy quantum devices.

arXiv:2504.15698•Apr 22, 2025•0 citations
The output state of a 2D geometrically local shallow random quantum circuit does not have long range correlations due to its lightcone structure. But this changes if one measures a subset of the qubits: long-range entanglement can be induced by the measurement process, leading to conditional correlations between distant qubits. In this paper we investigate the structure of conditional dependence in these circuits and its consequences for quantum advantage. For a tripartition ABCABCABC of the qubits, we consider the ensemble of post-measurement states on AAA that is conditioned on a specific measurement outcome on BBB and ranges over all possible measurement outcomes on CCC. For circuit depths exceeding a constant critical value d∗d^*d∗, we conjecture that this ensemble is well approximated by a certain generalization of the Haar ensemble, called the Scrooge ensemble~[Jozsa \textit{et al.}, \href{https://doi.org/10.1103/PhysRevA.49.668}{Phys. Rev. A \textbf{49}, 668 (1994)}]; we also provide supporting numerical and analytical evidence. Our conjecture describes a precise sense in which the state retains its lightcone structure on the remaining unmeasured qubits, but also develops some globally random features arising from the measurement. A consequence is that nnn-qubit shallow random quantum circuits in two dimensions are classically efficiently simulable in the presence of a tiny depolarizing noise rate Ω(log⁡(n)/n)Ω(\log(n)/n)Ω(log(n)/n).
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 d ∗ . 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 2D quantum circuit that satisfies a short-range MIE property can be classically simulated efficiently and with depth O ( d ) . Finally, we introduce a 2D depth- 2 “coarse-grained” circuit architecture, composed of random Clifford gates acting on O ( log  ( n ) ) qubits, for which we prove the existence of long-range MIE and establish an unconditional quantum advantage.
A central question of quantum computing is determining the source of the advantage of quantum computation over classical computation.Even though simulating quantum dynamics on a classical computer is thought to require exponential overhead in the worst case, efficient simulations are known to exist in several special cases.It was widely assumed that these easy-to-simulate cases as well as any yetundiscovered ones could be avoided by choosing a quantum circuit at random.We prove that this intuition is false by showing that certain families of constant-depth, 2D random circuits can be approximately simulated on a classical computer in time only linear in the number of qubits and gates, even though the same families are capable of universal quantum computation and are hard to exactly simulate in the worst case (under standard hardness assumptions).While our proof applies to specific random circuit families, we demonstrate numerically that typical instances of more general families of sufficiently shallow constantdepth 2D random circuits are also efficiently simulable.We propose two classical simulation algorithms.One is based on first simulating spatially local regions which are then "stitched" together via recovery maps.The other reduces the 2D simulation problem to a problem of simulating a form of 1D dynamics consisting of alternating rounds of random local unitaries and weak measurements.Similar processes have recently been the subject of an intensive research focus, which has observed that the dynamics generally undergo a phase transition from a low-entanglement (and efficient-to-simulate) regime to a highentanglement (and inefficient-to-simulate) regime as measurement strength is varied.Via a mapping from random quantum circuits to classical statistical mechanical models, we give analytical evidence that a similar computational phase transition occurs for both of our algorithms as parameters of the circuit architecture like the local Hilbert space dimension and circuit depth are varied and, additionally, that the effective 1D dynamics corresponding to sufficiently shallow random quantum circuits falls within the efficient-to-simulate regime.Implementing the latter algorithm for the depth-3 "brickwork" architecture, for which exact simulation is hard, we find that a laptop could simulate typical instances on a 409 × 409 grid with a total variation distance error less than 0.01 in approximately one minute per sample, a task intractable for previously known circuit simulation algorithms.Numerical results support our analytic evidence that the algorithm is asymptotically efficient.
We consider quantum circuits consisting of ddd layers of nearest-neighbor two-qubit gates acting on nnn qubits arranged on a line, where every qubit is independently depolarized with a constant probability before each layer. We describe a randomized parallel algorithm which samples from the output distribution of any such circuit to within total variation error δδδ, with parallel runtime 2O(d)log⁡log⁡(n/δ)2^{O(d)}\log\log(n/δ)2O(d)loglog(n/δ) and n2O(d)n 2^{O(d)}n2O(d) elementary real-arithmetic operations. Without noise, the same approach gives an exact sampler with parallel runtime O(log⁡n)O(\log n)O(logn). For constant depth, we further show that the input/output behavior of the noisy quantum circuit is reproduced up to a constant error by a randomized AC0\mathsf{AC}^0AC0-circuit, that is, a Boolean circuit of polynomial size and constant depth with unbounded fan-in AND and OR gates and NOT gates. Consequently, every relation problem solved by a noisy constant-depth quantum circuit in one dimension is also solved, with essentially the same success probability, by a randomized AC0\mathsf{AC}^0AC0-circuit. This rules out, for noisy circuits in one dimension, the unconditional quantum advantage established for noisy shallow circuits in two and three dimensions, where polynomial-size classical circuits over the same gate set require depth Ω(log⁡n/log⁡log⁡n)Ω(\log n/\log\log n)Ω(logn/loglogn). Our algorithm exploits the fact that depolarizing noise cuts a one-dimensional circuit into independent pieces of logarithmic width, each of which can be sampled exactly in parallel.
Quantum circuits with a constant rate of depolarizing noise per qubit per time step are known to converge to the uniform distribution at depth ω(p−1log⁡n)ω(p^{-1}\log n)ω(p−1logn), and hence become trivially classically simulable by uniform sampling. We show that under the physically natural constraint of geometric locality, noisy circuits become classically simulable at shallower depths by substantially more structured classical algorithms. We consider arbitrary geometrically local quantum circuits on nnn qubits, initialized in an arbitrary product state, with nearest-neighbor gates in O(1)O(1)O(1) spatial dimensions and interspersed depolarizing noise of any constant strength ppp. Our first result is that when the depth exceeds a threshold d∗=Θ(p−1log⁡n)d^*=Θ(p^{-1}\log n)d∗=Θ(p−1logn), the output distribution can be approximately sampled in quasipolynomial time. This gives a worst-case simulability result for noisy geometrically local circuits, and matches in its nnn-dependence the best previously known results for noisy random circuits. The proof is based on new information-theoretic bounds showing that in geometrically local noisy circuits, local regions lose correlations and independently converge to maximally mixed strictly before the entire system converges to the uniform distribution. We further prove structural results suggesting a sharper transition at depth Θ~(p−1)\tildeΘ(p^{-1})Θ~(p−1): after coarse-graining the lattice, Pauli weight supported on long connected paths can be truncated with exponentially small error. These results provide evidence for a percolation-like mechanism behind classical simulability at constant depth, and motivate our conjecture that all noisy geometrically local circuits admit quasipolynomial-time approximate sampling once circuit depth exceeds Θ~(p−1)\tildeΘ(p^{-1})Θ~(p−1).
Quantum advantage is widely expected to require sufficiently deep circuits, where correlations and global computational structure can grow beyond the reach of efficient classical simulation. This expectation is especially stark for constant-depth circuits with local readout: the expectation value of any fixed local observable lies within a bounded backward lightcone and is therefore classically tractable. Here we show that measurement feedback changes this picture. We establish a strict hierarchy of computational power: at fixed coherent depth, increasing the number of feedback outcomes strictly enlarges the class of functions accessible through a local expectation value. The two ends of this hierarchy exhibit distinct computational regimes. With logarithmic feedback, local expectation values for product-state inputs are efficiently classically simulable. Polynomial feedback, by contrast, enables an explicit family of adaptive shallow circuits to encode prime-field discrete logarithm problem~(DLP) into a fixed single-qubit expectation. Assuming the standard worst-case classical hardness of DLP, estimating this expectation value is classically hard. These results reveal a feedback-driven complexity transition, with further implications for resource lower bounds on DLP and the complexity of local-observable estimation under area-law entanglement. Our results open a new route to quantum advantage with shallow quantum circuits.
Quantum hardware is steadily improving, but near-term quantum devices remain limited by noise and circuit depth. This motivates measurement protocols that can use shallow-depth circuits while remaining robust to experimental errors. In this work, we study the advantages of shallow randomized measurements over non-entangling single-qubit measurements for learning properties of quantum states. Although shallow measurements have shown advantages in selected applications, their usefulness across different learning tasks has not been systematically understood. Here, we develop a theoretical framework based on Clifford ensembles that incorporates shallow measurements into derandomized measurements, multi-shot protocols, common randomized measurements, error-mitigated estimators, and hybrid quantum-classical learning. Finally, we validate these results on IBM quantum hardware in experiments with up to 40 qubits and 46 layers of two-qubit gates. These results indicate that shallow-depth measurements can provide practical benefits on noisy quantum devices.