Graph states are fundamental objects in the theory of quantum information due to their simple classical description and rich entanglement structure. They are also intimately related to instantaneous quantum polynomial-time (IQP) circuits, which have applications in quantum pseudorandomness and quantum advantage. For us, they are a toy model to understand the relation between circuit connectivity, entanglement structure, and computational complexity. In the worst case, a strict dichotomy in the computational universality of such graph states appears as a function of the degree d of a regular graph state [Ghosh , Phys. Rev. Lett. 131, 030601 (2023)]. In this paper, we study the average-case complexity of simulating random graph states of varying degree when measured in random product bases and give distinct evidence that a similar complexity-theoretic dichotomy exists in the average case. Specifically, we consider random d -regular graph states and prove three distinct results: First, we show two families of IQP circuits of depth d and show that they anticoncentrate for any 2 < d = o ( n 1 / 2 ) when measured in a random X - Y plane product basis. This implies anticoncentration for random constant-regular graph states. Second, in the regime d = Θ ( n c ) with c ∈ ( 0 , 1 ) , we prove that random d -regular graph states contain polynomially large grid graphs as induced subgraphs with high probability. This implies that they are universal resource states for measurement-based computation. Third, in the regime of high degree ( d ∼ n / 2 ), we show that random graph states are not sufficiently entangled to be trivially classically simulable, unlike Haar-random states. Proving the three results requires different techniques—the analysis of a classical statistical-mechanics model using Krawtchouck polynomials, graph-theoretic analysis using the switching method, and analysis of the ranks of submatrices of random adjacency matrices, respectively.
Graph states are fundamental objects in the theory of quantum information due to their simple classical description and rich entanglement structure. They are also intimately related to instantaneous quantum polynomial-time (IQP) circuits, which have applications in quantum pseudorandomness and quantum advantage. For us, they are a toy model to understand the relation between circuit connectivity, entanglement structure, and computational complexity. In the worst case, a strict dichotomy in the computational universality of such graph states appears as a function of the degree d of a regular graph state [Ghosh , Phys. Rev. Lett. 131, 030601 (2023)]. In this paper, we study the average-case complexity of simulating random graph states of varying degree when measured in random product bases and give distinct evidence that a similar complexity-theoretic dichotomy exists in the average case. Specifically, we consider random d -regular graph states and prove three distinct results: First, we show two families of IQP circuits of depth d and show that they anticoncentrate for any 2 < d = o ( n 1 / 2 ) when measured in a random X - Y plane product basis. This implies anticoncentration for random constant-regular graph states. Second, in the regime d = Θ ( n c ) with c ∈ ( 0 , 1 ) , we prove that random d -regular graph states contain polynomially large grid graphs as induced subgraphs with high probability. This implies that they are universal resource states for measurement-based computation. Third, in the regime of high degree ( d ∼ n / 2 ), we show that random graph states are not sufficiently entangled to be trivially classically simulable, unlike Haar-random states. Proving the three results requires different techniques—the analysis of a classical statistical-mechanics model using Krawtchouck polynomials, graph-theoretic analysis using the switching method, and analysis of the ranks of submatrices of random adjacency matrices, respectively.
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 analyze the entanglement structure of states generated by random constant-depth two-dimensional quantum circuits, followed by projective measurements of a subset of sites. By deriving a rigorous lower bound on the average entanglement entropy of such postmeasurement states, we prove that macroscopic long-ranged entanglement is generated above some constant critical depth in several natural classes of circuit architectures, which include brickwork circuits and random holographic tensor networks. This behavior had been conjectured based on previous works, which utilize nonrigorous methods such as replica theory calculations, or work in regimes where the local Hilbert space dimension grows with system size. To establish our lower bound, we develop new replica-free theoretical techniques that leverage tools from multiuser quantum information theory, which are of independent interest, allowing us to map the problem onto a statistical mechanics model of self-avoiding walks without requiring large local Hilbert space dimension. Our findings have consequences for the complexity of classically simulating sampling from random shallow circuits and of contracting tensor networks. First, we show that standard algorithms based on matrix product states which are used for both these tasks will fail above some constant depth and bond dimension, respectively. In addition, we also prove that these random constant-depth quantum circuits cannot be simulated by any classical circuit of sublogarithmic depth.
A central question in quantum information theory is the circuit complexity of states arising from standard many-body models. We study this question for quantum p-spin glasses, random Hamiltonians whose interactions act on p-tuples of qubits through Pauli strings. Anschuetz, Gamarnik, and Kiani (arXiv:2404.07231) showed that the optimum energy is separated from the best energy achievable by product states. This leaves open whether shallow circuits can close the gap, since even depth-one circuits can generate entanglement.
We show that the entanglement needed to close the product-state gap cannot be generated at shallow depth. When the average interaction degree grows with n, we prove that, for all sufficiently large fixed p, any circuit preparing an n-qubit state whose normalized energy is within a fixed positive constant of the optimum must have depth Ωp(logn). In the bounded-average-degree regime, we prove a fixed-depth obstruction: for every fixed D, a sufficiently large degree prefactor rules out depth-D preparation of near-ground states. Both results hold uniformly over circuits with an arbitrary number of ancilla qubits.
Our results give an obstruction in the spirit of the No Low-Energy Trivial States problem of Freedman and Hastings (arXiv:1301.1363), but for random quantum spin glasses rather than code-based Hamiltonians such as those of Anshu, Breuckmann, and Nirkhe (arXiv:2206.13228), whose ground states admit polynomial-size preparation circuits. This setting opens a probabilistic route to NLTS-like questions: we recast state-preparation lower bounds for random quantum Hamiltonians as uniform control of Gaussian processes indexed by shallow circuits.
Quantum advantage is a demonstration of a computation by a quantum computer that cannot be performed by the best classical computer in a reasonable time. A well-studied approach to demonstrating this on near-term quantum computers is to use random circuit sampling. It has been suggested that a good candidate for demonstrating quantum advantage with random circuit sampling is to use instantaneous quantum polynomial (IQP) circuits. These are quantum circuits where the unitary it implements is diagonal. In this paper, we introduce improved techniques for exactly classically simulating random IQP circuits. We find a simple algorithm to calculate an amplitude of an n-qubit IQP circuit with dense random two-qubit interactions in time O(nlog2n2n), which for sparse circuits (where each qubit interacts with O(logn) other qubits) runs in o(2n/poly(n)) for any given polynomial. Using a more complicated stabilizer decomposition approach we improve the algorithm for dense circuits to $O(\frac{{(logn)}^{4\ensuremath{-}\ensuremath{\beta}}}{{n}^{2\ensuremath{-}\ensuremath{\beta}}}{2}^{n})$ where $\ensuremath{\beta}\ensuremath{\approx}0.396$. We further discuss how our techniques also lead to improved simulation times for IQP circuits on restricted architectures. We benchmarked our main algorithm and found that we can simulate up to 50-qubit circuits in a couple of minutes on a laptop, with 68-qubit sparse circuits taking a couple of hours. We estimate dense 70-qubit circuits are in range for large computing clusters.
We consider quantum circuits consisting of randomly chosen two-local gates and study the number of gates needed for the distribution over measurement outcomes for typical circuit instances to be anticoncentrated, roughly meaning that the probability mass is not too concentrated on a small number of measurement outcomes.An understanding of the conditions for anticoncentration is important for determining which quantum circuits are difficult to simulate classically, as anticoncentration has been in some cases an ingredient of mathematical arguments that simulation is hard and in other cases a necessary condition for easy simulation.Our definition of anticoncentration is that the expected collision probability of the distribution-that is, the probability that two independently drawn outcomes will agree-is only a constant factor larger than the collision probability for the uniform distribution.We show that when the two-local gates are each drawn from the Haar measure (or any 2-design), at least (n log(n)) gates (and thus (log(n)) circuit depth) are needed for this condition to be met on an n-qudit circuit.In both the case where the gates are nearest neighbor on a one-dimensional ring and the case where gates are long range, we show that O(n log(n)) gates are also sufficient and we precisely compute the optimal constant prefactor for the n log(n).The technique we employ relies upon a mapping from the expected collision probability to the partition function of an Ising-like classical statistical-mechanical model, which we manage to bound using stochastic and combinatorial techniques.
This thesis is focused on the generation and understanding of particular kinds of quantum randomness. Randomness is useful for many tasks in physics and information processing, from randomized benchmarking , to black hole physics , as well demonstrating a so-called quantum speedup , and many other applications. On the one hand we explore how to generate a particular form of random evolution known as a t-design. On the other we show how this can also give instances for quantum speedup - where classical computers cannot simulate the randomness efficiently. We also show that this is still possible in noisy realistic settings. More specifically, this thesis is centered around three main topics. The first of these being the generation of epsilon-approximate unitary t-designs. In this direction, we first show that non-adaptive, fixed measurements on a graph state composed of poly(n,t,log(1/epsilon)) qubits, and with a regular structure (that of a brickwork state) effectively give rise to a random unitary ensemble which is a epsilon-approximate t-design. This work is presented in Chapter 3. Before this work, it was known that non-adaptive fixed XY measurements on a graph state give rise to unitary t-designs , however the graph states used there were of complicated structure and were therefore not natural candidates for measurement based quantum computing (MBQC), and the circuits to make them were complicated. The novelty in our work is showing that t-designs can be generated by fixed, non-adaptive measurements on graph states whose underlying graphs are regular 2D lattices. These graph states are universal resources for MBQC. Therefore, our result allows the natural integration of unitary t-designs, which provide a notion of quantum pseudorandomness which is very useful in quantum algorithms, into quantum algorithms running in MBQC. Moreover, in the circuit picture this construction for t-designs may be viewed as a constant depth quantum circuit, albeit with a polynomial number of ancillas. We then provide new constructions of epsilon-approximate unitary t-designs both in the circuit model and in MBQC which are based on a relaxation of technical requirements in previous constructions. These constructions are found in Chapters 4 and 5.