Quantum Advantage via Solving Multivariate Quadratics
- URL: http://arxiv.org/abs/2411.14697v1
- Date: Fri, 22 Nov 2024 03:10:10 GMT
- Title: Quantum Advantage via Solving Multivariate Quadratics
- Authors: Pierre Briaud, Riddhi Ghosal, Aayush Jain, Paul Lou, Amit Sahai,
- Abstract summary: We show that there is a quantum-time algorithm that simultaneously solves $p_i(x_n)=y_i_iin [m]$ over $mathbbF$.
While a solution exists with high probability, we conjecture that it is hard to find one based on classical cryptanalysis.
- Score: 12.62849227946452
- License:
- Abstract: In this work, we propose a new way to (non-interactively, verifiably) demonstrate Quantum Advantage by solving the average-case $\mathsf{NP}$ search problem of finding a solution to a system of (underdetermined) multivariate quadratic equations over the finite field $\mathbb{F}_2$ drawn from a specified distribution. In particular, we design a distribution of degree-2 polynomials $\{p_i(x_1,\ldots,x_n)\}_{i\in [m]}$ for $m<n$ over $\mathbb{F}_2$ for which we show that there is a quantum polynomial-time algorithm that simultaneously solves $\{p_i(x_1,\ldots,x_n)=y_i\}_{i\in [m]}$ for a random vector $(y_1,\ldots,y_m)$. On the other hand, while a solution exists with high probability, we conjecture that it is classically hard to find one based on classical cryptanalysis that we provide, including a comprehensive review of all known relevant classical algorithms for solving multivariate quadratics. Our approach proceeds by examining the Yamakawa-Zhandry (FOCS 2022) quantum advantage scheme and replacing the role of the random oracle with our multivariate quadratic equations. Our work therefore gives several new perspectives: First, our algorithm gives a counterexample to the conventional belief that generic classically hard multivariate quadratic systems are also quantumly hard. Second, based on cryptanalytic evidence, our work gives an explicit simple replacement for the random oracle from the work of Yamakawa and Zhandry. We show how to instantiate the random oracle with families of just degree two multivariate polynomials over $\mathbb{F}_2$.
Related papers
- Quantum-Computable One-Way Functions without One-Way Functions [0.6349503549199401]
We construct a classical oracle relative to which $mathsfP = mathsfNP$ but quantum-computable quantum-secure trapdoor one-way functions exist.
Our result implies multi-copy pseudorandom states and pseudorandom unitaries, but also classical-communication public-key encryption, signatures, and oblivious transfer schemes.
arXiv Detail & Related papers (2024-11-04T19:40:01Z) - Sum-of-Squares inspired Quantum Metaheuristic for Polynomial Optimization with the Hadamard Test and Approximate Amplitude Constraints [76.53316706600717]
Recently proposed quantum algorithm arXiv:2206.14999 is based on semidefinite programming (SDP)
We generalize the SDP-inspired quantum algorithm to sum-of-squares.
Our results show that our algorithm is suitable for large problems and approximate the best known classicals.
arXiv Detail & Related papers (2024-08-14T19:04:13Z) - Efficient Unitary T-designs from Random Sums [0.6640968473398456]
Unitary $T$-designs play an important role in quantum information, with diverse applications in quantum algorithms, benchmarking, tomography, and communication.
We provide a new construction of $T$-designs via random matrix theory using $tildeO(T2 n2)$ quantum gates.
arXiv Detail & Related papers (2024-02-14T17:32:30Z) - Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$.
We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
arXiv Detail & Related papers (2024-01-17T18:59:38Z) - Quadratic Speed-up in Infinite Variance Quantum Monte Carlo [1.2891210250935148]
We give an extension of Montanaro's arXiv/archive:1504.06987 quantum Monte Carlo method.
It is tailored for computing expected values of random variables that exhibit infinite variance.
arXiv Detail & Related papers (2024-01-15T06:31:36Z) - Agnostically Learning Multi-index Models with Queries [54.290489524576756]
We study the power of query access for the task of agnostic learning under the Gaussian distribution.
We show that query access gives significant runtime improvements over random examples for agnostically learning MIMs.
arXiv Detail & Related papers (2023-12-27T15:50:47Z) - The complexity of solving a random polynomial system [3.420117005350141]
In this paper there is an overview on a general algorithm used to solve a multivariate system.
We then speak about random systems and in particular what "random" means to us.
We give an upper bound on both the degree of regularity and the solving degree of such random systems.
arXiv Detail & Related papers (2023-09-07T17:14:59Z) - On the average-case complexity of learning output distributions of
quantum circuits [55.37943886895049]
We show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model.
This learning model is widely used as an abstract computational model for most generic learning algorithms.
arXiv Detail & Related papers (2023-05-09T20:53:27Z) - 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) - Quantum learning algorithms imply circuit lower bounds [7.970954821067043]
We establish the first general connection between the design of quantum algorithms and circuit lower bounds.
Our proof builds on several works in learning theory, pseudorandomness, and computational complexity.
arXiv Detail & Related papers (2020-12-03T14:03:20Z) - Robustly Learning any Clusterable Mixture of Gaussians [55.41573600814391]
We study the efficient learnability of high-dimensional Gaussian mixtures in the adversarial-robust setting.
We provide an algorithm that learns the components of an $epsilon$-corrupted $k$-mixture within information theoretically near-optimal error proofs of $tildeO(epsilon)$.
Our main technical contribution is a new robust identifiability proof clusters from a Gaussian mixture, which can be captured by the constant-degree Sum of Squares proof system.
arXiv Detail & Related papers (2020-05-13T16:44:12Z)
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.