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Annual Symposium on Foundations of Computer Science

Tight Quantum Time-Space Tradeoffs for Function Inversion

Kai-Min Chung, Siyao Guo, Qipeng Liu, Luowen Qian

Published Nov 1, 2020• 40 citations
Publisher

Abstract

In function inversion, we are given a function f:[N]→[N], and want to prepare some advice of size S, such that we can efficiently invert any image in time T. This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower bounds. Investigation of this problem in the quantum setting was initiated by Nayebi, Aaronson, Belovs, and Trevisan (2015), who proved a lower bound of ST2=Ω̃(N) for random permutations against classical advice, leaving open an intriguing possibility that Grover's search can be sped up to time Õ(√{N/S}). Recent works by Hhan, Xagawa, and Yamakawa (2019), and Chung, Liao, and Qian (2019) extended the argument for random functions and quantum advice, but the lower bound remains ST2=Ω̃(N). In this work, we prove that even with quantum advice, ST+ T2=Ω̃(N), is required for an algorithm to invert random functions. This demonstrates that Grover's search is optimal for S=Õ(√N), ruling out any substantial speed-up for Grover's search even with quantum advice. Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019). To prove this result, we develop a general framework for establishing quantum time-space lower bounds. We further demonstrate the power of our framework by proving the following results. (a) Yao's box problem: We prove a tight quantum time-space lower bound for classical advice. For quantum advice, we prove a first time-space lower bound using shadow tomography. These results resolve two open problems posted by Nayebi et al (2015). (b) Salted cryptography: We show that “salting generically provably defeats preprocessing,” a result shown by Coretti, Dodis, Guo, and Steinberger (2018), also holds in the quantum setting. In particular, we prove quantum time-space lower bounds for a wide class of salted cryptographic primitives in the quantum random oracle model. This yields the first quantum time-space lower bound for salted collision-finding, which in turn implies that PWPPO⊈ FBQPO/qpoly relative to a random oracle O.

DOI
10.1109/focs46700.2020.00068

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Tight Quantum Time-Space Tradeoffs for Function Inversion

Kai-Min Chung, Siyao Guo, Qipeng Liu +1 more

In function inversion, we are given a function $f: [N] \mapsto [N]$, and want to prepare some advice of size $S$, such that we can efficiently invert any image in time $T$. This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower bounds. Investigation of this problem in the quantum setting was initiated by Nayebi, Aaronson, Belovs, and Trevisan (2015), who proved a lower bound of $ST^2 = \tildeΩ(N)$ for random permutations against classical advice, leaving open an intriguing possibility that Grover's search can be sped up to time $\tilde O(\sqrt{N/S})$. Recent works by Hhan, Xagawa, and Yamakawa (2019), and Chung, Liao, and Qian (2019) extended the argument for random functions and quantum advice, but the lower bound remains $ST^2 = \tildeΩ(N)$. In this work, we prove that even with quantum advice, $ST + T^2 = \tildeΩ(N)$ is required for an algorithm to invert random functions. This demonstrates that Grover's search is optimal for $S = \tilde O(\sqrt{N})$, ruling out any substantial speed-up for Grover's search even with quantum advice. Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019). To prove this result, we develop a general framework for establishing quantum time-space lower bounds. We further demonstrate the power of our framework by proving quantum time-space lower bounds for Yao's box problem and salted cryptography.

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In function inversion, we are given a function f:[N]→[N], and want to prepare some advice of size S, such that we can efficiently invert any image in time T. This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower bounds. Investigation of this problem in the quantum setting was initiated by Nayebi, Aaronson, Belovs, and Trevisan (2015), who proved a lower bound of ST2=Ω̃(N) for random permutations against classical advice, leaving open an intriguing possibility that Grover's search can be sped up to time Õ(√{N/S}). Recent works by Hhan, Xagawa, and Yamakawa (2019), and Chung, Liao, and Qian (2019) extended the argument for random functions and quantum advice, but the lower bound remains ST2=Ω̃(N). In this work, we prove that even with quantum advice, ST+ T2=Ω̃(N), is required for an algorithm to invert random functions. This demonstrates that Grover's search is optimal for S=Õ(√N), ruling out any substantial speed-up for Grover's search even with quantum advice. Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019). To prove this result, we develop a general framework for establishing quantum time-space lower bounds. We further demonstrate the power of our framework by proving the following results. (a) Yao's box problem: We prove a tight quantum time-space lower bound for classical advice. For quantum advice, we prove a first time-space lower bound using shadow tomography. These results resolve two open problems posted by Nayebi et al (2015). (b) Salted cryptography: We show that “salting generically provably defeats preprocessing,” a result shown by Coretti, Dodis, Guo, and Steinberger (2018), also holds in the quantum setting. In particular, we prove quantum time-space lower bounds for a wide class of salted cryptographic primitives in the quantum random oracle model. This yields the first quantum time-space lower bound for salted collision-finding, which in turn implies that PWPPO⊈ FBQPO/qpoly relative to a random oracle O.
In function inversion, we are given a function f:[N]↦[N]f: [N] \mapsto [N]f:[N]↦[N], and want to prepare some advice of size SSS, such that we can efficiently invert any image in time TTT. This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower bounds. Investigation of this problem in the quantum setting was initiated by Nayebi, Aaronson, Belovs, and Trevisan (2015), who proved a lower bound of ST2=Ω~(N)ST^2 = \tildeΩ(N)ST2=Ω~(N) for random permutations against classical advice, leaving open an intriguing possibility that Grover's search can be sped up to time O~(N/S)\tilde O(\sqrt{N/S})O~(N/S​). Recent works by Hhan, Xagawa, and Yamakawa (2019), and Chung, Liao, and Qian (2019) extended the argument for random functions and quantum advice, but the lower bound remains ST2=Ω~(N)ST^2 = \tildeΩ(N)ST2=Ω~(N). In this work, we prove that even with quantum advice, ST+T2=Ω~(N)ST + T^2 = \tildeΩ(N)ST+T2=Ω~(N) is required for an algorithm to invert random functions. This demonstrates that Grover's search is optimal for S=O~(N)S = \tilde O(\sqrt{N})S=O~(N​), ruling out any substantial speed-up for Grover's search even with quantum advice. Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019). To prove this result, we develop a general framework for establishing quantum time-space lower bounds. We further demonstrate the power of our framework by proving quantum time-space lower bounds for Yao's box problem and salted cryptography.
We prove near-optimal lower bounds for preprocessing attacks on quantum cryptography in the random oracle model. Specifically, we show that a TTT-query adversary with SSS qubits of non-uniform advice can recover a random key kkk from the nnn-qubit binary phase state ∣ψk⟩∝∑xR(k,x)∣x⟩|ψ_k\rangle \propto \sum_{x} R(k,x) |x\rangle∣ψk​⟩∝∑x​R(k,x)∣x⟩ with probability at most O(T2+STN)O(\frac{T^2 + \sqrt{ST}}{N})O(NT2+ST​​) for N=2nN=2^nN=2n. In contrast, the best known bound for post-quantum one-way functions is O(T2+STN)O(\frac{T^2 + ST}{N})O(NT2+ST​), with a trivial attack at S=NS = NS=N. This demonstrates a new advantage of quantum cryptography over classical cryptography: nnn qubits of communication suffice for security against preprocessing attacks with space up to N2N^2N2 rather than NNN. Our methodology is simple: express the optimal preprocessing attack as the operator norm of a random matrix, and bound this value in expectation over the random oracle via the trace-moment method. These trace moments have a natural interpretation using compressed oracles [Zhandry, Crypto 2019], which we then analyze. This can be viewed as a simplification and generalization of the approach of Liu [Eurocrypt 2023] for proving the security of post-quantum cryptography against preprocessing attacks. We also prove the following results: (1) We tighten Liu's analysis of post-quantum PRGs in QROM, achieving a distinguishing advantage bound of O(T2N+STN)O(\frac{T^2}N + \sqrt{\frac{ST}N})O(NT2​+NST​​). (2) For unitary synthesis, we extend the one-query lower bound of Lombardi-Ma-Wright [STOC 2024] to hold against adversaries that can make one arbitrary function query along with polynomially many (adaptive) queries to the random oracle, either before or after the function query. This also interprets the original LMW24 result in terms of compressed oracles. (3) Finally, we prove a tight O(SN)O(\frac{\sqrt{S}}N)O(NS​​) bound for the pseudorandomness of random binary phase states against space SSS distinguishers.
Function inversion is the problem that given a random function f:[M]→[N]f: [M] \to [N]f:[M]→[N], we want to find pre-image of any image f−1(y)f^{-1}(y)f−1(y) in time TTT. In this work, we revisit this problem under the preprocessing model where we can compute some auxiliary information or advice of size SSS that only depends on fff but not on yyy. It is a well-studied problem in the classical settings, however, it is not clear how quantum algorithms can solve this task any better besides invoking Grover's algorithm, which does not leverage the power of preprocessing. Nayebi et al. proved a lower bound ST2≥Ω~(N)ST^2 \ge \tildeΩ(N)ST2≥Ω~(N) for quantum algorithms inverting permutations, however, they only consider algorithms with classical advice. Hhan et al. subsequently extended this lower bound to fully quantum algorithms for inverting permutations. In this work, we give the same asymptotic lower bound to fully quantum algorithms for inverting functions for fully quantum algorithms under the regime where M=O(N)M = O(N)M=O(N). In order to prove these bounds, we generalize the notion of quantum random access code, originally introduced by Ambainis et al., to the setting where we are given a list of (not necessarily independent) random variables, and we wish to compress them into a variable-length encoding such that we can retrieve a random element just using the encoding with high probability. As our main technical contribution, we give a nearly tight lower bound (for a wide parameter range) for this generalized notion of quantum random access codes, which may be of independent interest.
Non-uniform security allows an adversary to receive bounded advice about an oracle before attempting a fresh challenge. This captures the most realistic attacks and has already been studied extensively in prior work. In this work, we introduce an operator-norm approach for non-uniform security in the quantum random oracle and random permutation models. This new approach yields a unified reduction for both search success probability and distinguishing advantage. Previously, the reduction only worked with success probability even in the decision games, yielding a worse bound. Our framework enables tight bounds for Yao's box, both with and without salting, and improved bounds for pseudorandom generators. We also prove an optimal generic salting theorem for decision games. By defining a property of a game, which separates the contributions of the existing queries in the offline stage and subsequent online queries, we obtain stronger bounds for specific salted constructions. These include salted permutation inversion, tight up to logarithmic factors, and salted random-function inversion, tight up to logarithmic factors and the gap already present in classical function inversion.
We prove lower bounds on the time and space required for quantum computers to solve a wide variety of problems involving matrices, many of which have only been analyzed classically in prior work. Using a novel way of applying recording query methods we show that for many linear algebra problems—including matrix-vector product, matrix inversion, matrix multiplication and powering—existing classical time-space tradeoffs also apply to quantum algorithms with at most a constant factor loss. For example, for almost all fixed matrices A, including the discrete Fourier transform (DFT) matrix, we prove that quantum circuits with at most T input queries and S qubits of memory require T=Ω(n2/S) to compute matrix-vector product Ax for x ∈ {0,1}n. We similarly prove that matrix multiplication for n× n binary matrices requires T=Ω(n3 / √S). Because many of our lower bounds are matched by deterministic algorithms with the same time and space complexity, our results show that quantum computers cannot provide any asymptotic advantage for these problems at any space bound. We also improve the previous quantum time-space tradeoff lower bounds for n× n Boolean (i.e. AND-OR) matrix multiplication from T=Ω(n2.5/S1/2) to T=Ω(n2.5/S1/4) which has optimal exponents for the powerful query algorithms to which it applies. Our method also yields improved lower bounds for classical algorithms.
Time and space (memory) are two of the most important measures of cost in computation, even more so for quantum computation. Yet, our tools for proving unconditional quantum tradeoffs between time and space are surprisingly limited. The first quantum time-space tradeoff lower bounds were proven for sorting by Klauck, Špalek and de Wolf. Unfortunately, their method is limited to proving output-oblivious lower bounds (i.e. the lower bounds only apply to algorithms with a non-adaptive output schedule) and other methods have yielded nothing beyond output-oblivious lower bounds for sorting. Here, we prove the first fully general quantum time-space tradeoff lower bound for sorting. We do so by introducing a novel method based on the noise operator to add to the analysis toolkit for proving quantum query and time-space tradeoff lower bounds. By combining our resulting quantum noise stability bound with quantum recording query methods, we prove an Ω(n4/3(log⁡log⁡n)/(S1/3log⁡n))Ω(n^{4/3} (\log \log n)/(S^{1/3} \log n))Ω(n4/3(loglogn)/(S1/3logn)) lower bound on the number of queries that a fully general quantum algorithm with at most SSS qubits of memory requires to sort nnn numbers from [n2][n^2][n2]. Applying our noise operator argument involves purely classical reasoning, which makes it particularly simple to use. We also use it to prove that, for any strongly universal (pairwise independent) hash function family HHH from nnn bits to mmm bits, almost all hash functions in HHH require any algorithm with at most SSS qubits of memory to make Ω(nm/S)Ω(nm/S)Ω(nm/S) quantum queries to input xxx in order to compute h(x)h(x)h(x), even with very small success probability. Previously, Mansour, Nisan, and Tiwari had shown a matching classical lower bound for computing h(x)h(x)h(x) with both hhh and xxx as inputs using their hash mixing lemma. Our noise operator method allows us to use a related property of hash functions to prove our quantum lower bounds.