Unitary designs in nearly optimal depth
- URL: http://arxiv.org/abs/2507.06216v2
- Date: Sat, 19 Jul 2025 04:11:58 GMT
- Title: Unitary designs in nearly optimal depth
- Authors: Laura Cui, Thomas Schuster, Fernando Brandao, Hsin-Yuan Huang,
- Abstract summary: We construct unitary designs on qubits in circuit depth $O(log k log log n k / varepsilon)$.<n>The depth is exponentially improved over all known results in all three parameters $n$, $k$, $varepsilon$.<n>We also develop a new analytical framework for bounding errors in quantum experiments involving many queries to random unitaries.
- Score: 40.28216388589026
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We construct $\varepsilon$-approximate unitary $k$-designs on $n$ qubits in circuit depth $O(\log k \log \log n k / \varepsilon)$. The depth is exponentially improved over all known results in all three parameters $n$, $k$, $\varepsilon$. We further show that each dependence is optimal up to exponentially smaller factors. Our construction uses $\tilde{{O}}(nk)$ ancilla qubits and ${O}(nk)$ bits of randomness, which are also optimal up to $\log(n k)$ factors. An alternative construction achieves a smaller ancilla count $\tilde{{O}}(n)$ with circuit depth ${O}(k \log \log nk/\varepsilon)$. To achieve these efficient unitary designs, we introduce a highly-structured random unitary ensemble that leverages long-range two-qubit gates and low-depth implementations of random classical hash functions. We also develop a new analytical framework for bounding errors in quantum experiments involving many queries to random unitaries. As an illustration of this framework's versatility, we provide a succinct alternative proof of the existence of pseudorandom unitaries.
Related papers
- Random unitaries in extremely low depth [0.8680580889074451]
We prove that random quantum circuits on any geometry, including a 1D line, can form approximate unitary designs over $n$ qubits in $log n$ depth.<n>In a similar manner, we construct pseudorandom unitaries (PRUs) in 1D circuits in $textpoly(log n)$ depth, and in all-to-all-connected circuits in $textpoly(log log n)$ depth.
arXiv Detail & Related papers (2024-07-10T15:27:48Z) - Quantum spectral method for gradient and Hessian estimation [4.193480001271463]
Gradient descent is one of the most basic algorithms for solving continuous optimization problems.
We propose a quantum algorithm that returns an $varepsilon$-approximation of its gradient with query complexity $widetildeO (1/varepsilon)$.
We also propose two quantum algorithms for Hessian estimation, aiming to improve quantum analogs of Newton's method.
arXiv Detail & Related papers (2024-07-04T11:03:48Z) - Variance-Reduced Fast Krasnoselkii-Mann Methods for Finite-Sum Root-Finding Problems [8.0153031008486]
We propose a new class of fast Krasnoselkii--Mann methods with variance reduction to solve a finite-sum co-coercive equation $Gx = 0$.<n>Our algorithm is single-loop and leverages a new family of unbiased variance-reduced estimators specifically designed for a wider class of root-finding algorithms.<n> numerical experiments validate our algorithms and demonstrate their promising performance compared to state-of-the-art methods.
arXiv Detail & Related papers (2024-06-04T15:23:29Z) - More Efficient $k$-wise Independent Permutations from Random Reversible Circuits via log-Sobolev Inequalities [0.10241134756773229]
We prove that the permutation computed by a reversible circuit with $tildeO(nkcdot log (1/varepsilon))$ random $3$-bit gates is $varepsilon$-approximately $k$-wise independent.
Our bound improves on currently known bounds in the regime when the approximation error $varepsilon$ is not too small.
arXiv Detail & Related papers (2024-05-08T22:38:35Z) - Spacetime-Efficient Low-Depth Quantum State Preparation with
Applications [93.56766264306764]
We show that a novel deterministic method for preparing arbitrary quantum states requires fewer quantum resources than previous methods.
We highlight several applications where this ability would be useful, including quantum machine learning, Hamiltonian simulation, and solving linear systems of equations.
arXiv Detail & Related papers (2023-03-03T18:23:20Z) - Mind the gap: Achieving a super-Grover quantum speedup by jumping to the
end [114.3957763744719]
We present a quantum algorithm that has rigorous runtime guarantees for several families of binary optimization problems.
We show that the algorithm finds the optimal solution in time $O*(2(0.5-c)n)$ for an $n$-independent constant $c$.
We also show that for a large fraction of random instances from the $k$-spin model and for any fully satisfiable or slightly frustrated $k$-CSP formula, statement (a) is the case.
arXiv Detail & Related papers (2022-12-03T02:45:23Z) - Quantum Resources Required to Block-Encode a Matrix of Classical Data [56.508135743727934]
We provide circuit-level implementations and resource estimates for several methods of block-encoding a dense $Ntimes N$ matrix of classical data to precision $epsilon$.
We examine resource tradeoffs between the different approaches and explore implementations of two separate models of quantum random access memory (QRAM)
Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.
arXiv Detail & Related papers (2022-06-07T18:00:01Z) - Low-degree learning and the metric entropy of polynomials [44.99833362998488]
We prove that any (deterministic or randomized) algorithm which learns $mathscrF_nd$ with $L$-accuracy $varepsilon$ requires at least $Omega(sqrtvarepsilon)2dlog n leq log mathsfM(mathscrF_n,d,|cdot|_L,varepsilon) satisfies the two-sided estimate $$c (1-varepsilon)2dlog
arXiv Detail & Related papers (2022-03-17T23:52:08Z) - An Optimal Separation of Randomized and Quantum Query Complexity [67.19751155411075]
We prove that for every decision tree, the absolute values of the Fourier coefficients of a given order $ellsqrtbinomdell (1+log n)ell-1,$ sum to at most $cellsqrtbinomdell (1+log n)ell-1,$ where $n$ is the number of variables, $d$ is the tree depth, and $c>0$ is an absolute constant.
arXiv Detail & Related papers (2020-08-24T06:50:57Z) - Epsilon-nets, unitary designs and random quantum circuits [0.11719282046304676]
Epsilon-nets and approximate unitary $t$-designs are notions of unitary operations relevant for numerous applications in quantum information and quantum computing.
We prove that for a fixed $d$ of the space, unitaries constituting $delta$-approx $t$-expanders form $epsilon$-nets for $tsimeqfracd5/2epsilon$ and $delta=left(fracepsilon3/2dright)d2$.
We show that approximate tdesigns can be generated
arXiv Detail & Related papers (2020-07-21T15:16:28Z) - Streaming Complexity of SVMs [110.63976030971106]
We study the space complexity of solving the bias-regularized SVM problem in the streaming model.
We show that for both problems, for dimensions of $frac1lambdaepsilon$, one can obtain streaming algorithms with spacely smaller than $frac1lambdaepsilon$.
arXiv Detail & Related papers (2020-07-07T17:10:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.