Quantum effects can enhance information-processing capabilities and speed up the solution of certain computational problems. Whether a quantum advantage can be rigorously proven in some setting or demonstrated experimentally using near-term devices is the subject of active debate. We show that parallel quantum algorithms running in a constant time period are strictly more powerful than their classical counterparts; they are provably better at solving certain linear algebra problems associated with binary quadratic forms. Our work gives an unconditional proof of a computational quantum advantage and simultaneously pinpoints its origin: It is a consequence of quantum nonlocality. The proposed quantum algorithm is a suitable candidate for near-future experimental realizations, as it requires only constant-depth quantum circuits with nearest-neighbor gates on a two-dimensional grid of qubits (quantum bits).
Quantum effects can enhance information-processing capabilities and speed up the solution of certain computational problems. Whether a quantum advantage can be rigorously proven in some setting or demonstrated experimentally using near-term devices is the subject of active debate. We show that parallel quantum algorithms running in a constant time period are strictly more powerful than their classical counterparts; they are provably better at solving certain linear algebra problems associated with binary quadratic forms. Our work gives an unconditional proof of a computational quantum advantage and simultaneously pinpoints its origin: It is a consequence of quantum nonlocality. The proposed quantum algorithm is a suitable candidate for near-future experimental realizations, as it requires only constant-depth quantum circuits with nearest-neighbor gates on a two-dimensional grid of qubits (quantum bits).
Quantum computers were initially proposed to surpass the limitations of classical computers in simulating physical systems. Soon after, the field of quantum computing advanced through landmark discoveries of quantum algorithms, with some conjectured to provide exponential advantages over classical counterparts. These include Shor’s factoring algorithm, which has profound implications in cryptography. Additionally, moderate yet significant polynomial separations have further increased interest in the field. However, despite this progress, unconditional proofs confirming quantum superiority on the theoretical side remain elusive. At the same time, large, noise-resilient quantum computations on the experimental side, which could challenge the extended Church-Turing thesis by potentially breaking classical computational barriers, have yet to be achieved. Meanwhile, our understanding of the differences between classical and quantum mechanics has deepened. We now possess rigorous theoretical tools, such as Bell inequalities and nonlocal games, to describe behaviors unique to these theories. Moreover, the main loopholes for observing non-classical correlations have been closed experimentally, showing that correlations in the physical world exceed those predicted by classical mechanics. This thesis investigates computational complexity differences between quantum and classical models by studying shallow-depth circuits. The primary focus lies in identifying, elucidating, and proving computational quantum advantages. Additionally, the analysis delves into the bijection between these circuits and foundational non-local game constructions, exploring how quantum-classical deviations in multi-party correlation settings translate to computational advantages. A key question addressed is how to synthesize constant-depth quantum circuits for various problems to leverage quantum advantages. To this end, circuit synthesis techniques tailored to measurement-based quantum computation are developed, enabling simultaneously optimal quantum strategies for certain nonlocal games based on a particular bijection between this model and XOR non-local games. Building on this work, we confront a pivotal challenge: If a candidate quantum circuit potentially provides a computational advantage, how can we prove that no classical circuit surpasses it? This work presents new unconditional quantum-classical separations within shallow-depth settings, distinguishing for the first time constant-depth quantum circuits with one- and two-qubit gates from biased polynomial threshold circuits, which incorporate neural-network-type functionalities. These separations extend to prime-dimensional quantum systems, and we conjecture to have identified Razborov-Smolensky-type problems uniquely solvable by specific prime dimensions. The thesis also differentiates constant-depth quantum circuits with multi-qubit Toffoli gates from their classical counterparts, emphasizing the power of parallel quantum computation. Finally, verifying shallow-depth quantum computations is challenging due to conjectured exponential separations from classical computations, limiting correctness verification through simulation. While specific quantum algorithms within BQP have been addressed, we explore whether a quantum computer can reliably execute any instance from a fixed class of quantum circuits. Moving beyond heuristic-based solutions, we propose a benchmarking protocol that certifies, with exponentially high confidence, the execution of arbitrary circuits of specified dimensions, widths, and depths.
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 2O(d)loglog(n/δ) and n2O(d) elementary real-arithmetic operations. Without noise, the same approach gives an exact sampler with parallel runtime 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-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-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(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.
The rapid evolution of quantum devices fuels concerted efforts to experimentally establish quantum advantage over classical computing. Many demonstrations of quantum advantage, however, rely on computational assumptions and face verification challenges. Furthermore, steady advances in classical algorithms and machine learning make the issue of provable, practically demonstrable quantum advantage a moving target. In this work, we unconditionally demonstrate that parallel quantum computation can exhibit greater computational power than previously recognized. We prove that polynomial-size biased threshold circuits of constant depth-which model neural networks with tunable expressivity-fail to solve certain problems solvable by small constant-depth quantum circuits with local gates, for values of the bias that allow quantifiably large computational power. Additionally, we identify a family of problems that are solvable in constant depth by a universal quantum computer over prime-dimensional qudits with bounded connectivity, but remain hard for polynomial-size biased threshold circuits. We thereby bridge the foundational theory of non-local games in higher dimensions with computational advantage on emerging devices operating on a wide range of physical platforms. Finally, we show that these quantum advantages are robust to noise across all prime qudit dimensions with all-to-all connectivity, enhancing their practical appeal.
Abstract The rapid evolution of quantum devices fuels concerted efforts to experimentally establish quantum advantage over classical computing. Many demonstrations of quantum advantage, however, rely on computational assumptions and face verification challenges. Furthermore, steady advances in classical algorithms and machine learning make the issue of provable, practically demonstrable quantum advantage a moving target. In this work, we unconditionally demonstrate that parallel quantum computation can exhibit greater computational power than previously recognized. We prove that polynomial-size biased threshold circuits of constant depth—which model neural networks with tunable expressivity—fail to solve certain problems solvable by small constant-depth quantum circuits with local gates, for values of the bias that allow quantifiably large computational power. Additionally, we identify a family of problems that are solvable in constant depth by a universal quantum computer over prime-dimensional qudits with bounded connectivity, but remain hard for polynomial-size biased threshold circuits. We thereby bridge the foundational theory of non-local games in higher dimensions with computational advantage on emerging devices operating on a wide range of physical platforms. Finally, we show that these quantum advantages are robust to noise across all prime qudit dimensions with all-to-all connectivity, enhancing their practical appeal.
We give a sampling problem that is solvable by shallow quantum circuits, hard for polynomial-time classical algorithms under lattice-based assumptions, and efficiently verifiable by a classical computer. The quantum sampler admits two implementations: one uses log-logarithmic-depth quantum circuits with one- and two-qubit gates, i.e., QNC0[loglog] circuits, while the other uses constant-depth quantum circuits with unbounded fan-in gates, i.e., QAC0 circuits. Our construction can be seen as compiling the Learning with Errors (LWE)-based single-round proof of quantumness of Arabadjieva et al. (2025) to very low depth. The price paid for this compilation is the reliance on less standard, though well-motivated, assumptions: in addition to the lattice knowledge assumption used by Arabadjieva et al. (2025), we require a strengthened variant of the adaptive-hardcore-bit property of LWE, for which we provide supporting evidence. Unlike previous low-depth proofs of quantumness, the quantum computation here requires no mid-circuit measurements or feed-forward: it consists only of running a shallow circuit and sampling from its output distribution. This shows that shallow quantum circuits have sufficient structure to solve certain classically hard tasks whose solutions can be verified efficiently.
Quantum theory promises computational speed-ups over classical approaches. The celebrated Gottesman-Knill Theorem implies that the full power of quantum computation resides in the specific resource of “magic” states—the secret sauce to establish universal quantum computation. However, it is still questionable whether magic indeed brings the believed quantum advantage, ridding unproven complexity assumptions or black-box oracles. In this work, we demonstrate the first unconditional magic advantage: a separation between the power of generic constant-depth or shallow quantum circuits and magic-free counterparts. For this purpose, we link the shallow circuit computation with the strongest form of quantum nonlocality—quantum pseudo-telepathy, where distant non-communicating observers generate perfectly synchronous statistics. We prove quantum magic is indispensable for such correlated statistics in a specific nonlocal game inspired by the linear binary constraint system. Then, we translate generating quantum pseudo-telepathy into computational tasks, where magic is necessary for a shallow circuit to meet the target. As a by-product, we provide an efficient algorithm to solve a general linear binary constraint system over the Pauli group, in contrast to the broad undecidability in constraint systems. We anticipate our results will enlighten the final establishment of the unconditional advantage of universal quantum computation. "Magic” is a resource in quantum computation that is believed to be important for quantum speedup, but the proofs of this have involved complexity assumptions or invoking a black-box oracle. Here, by constructing magic-required quantum pseudo-telepathy and converting it into a relation problem, the authors show an unconditional separation between constant-depth quantum circuits and magic-free ones.