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Time-space lower bounds for breaking quantum cryptography
Fangqi Dong, Alex Lombardi
TL;DR
The paper studies the time-space security of quantum cryptographic primitives, including single-copy one-way and pseudorandom states. It analyzes compressed-oracle databases and proves bounds on key recovery and related settings. Its results identify a quantum-cryptography security advantage over the classical setting, while the treatment of random-oracle queries before a unitary-synthesis function query incurs a suboptimal polynomial security loss.
Problem
The paper asks what quantitative security single-copy one-way states and pseudorandom states provide against preprocessing attacks.
Source: §1 · p. 4Method
For a small-database bound, the analysis purifies compressed-oracle databases as uniform superpositions of ordered databases and models challenge generation through swaps.
Source: §2.3 · p. 13Results
The paper bounds recovery of a random key from a binary phase state and reports security against preprocessing space up to N^2 rather than N in the classical setting.
Source: §1.1 · pp. 5–6Takeaways & Limitations
The results identify an advantage of quantum cryptography over classical cryptography in achievable security for a fixed commitment length.
Source: §1.1 · p. 6Queries to the random oracle before the function query are removed with a suboptimal polynomial security loss, leaving their tight analysis for future work.
Source: §2.3 · p. 14
Abstract
from arXiv · showhide
We prove near-optimal time-space lower bounds for breaking quantum cryptography in the random oracle model. Specifically, we show that a $T$-query adversary with $S$ qubits of non-uniform advice can recover a random key $k$ from the $n$-qubit binary phase state $|\psi_k\rangle \propto \sum_{x} R(k,x) |x\rangle$ with probability at most $O(\frac{T^2 + \sqrt{ST}}{N})$ for $N=2^n$. In contrast, the best known bound for post-quantum one-way functions is $O(\frac{T^2 + ST}{N})$, with a trivial attack at $S = N$. This demonstrates a new advantage of quantum cryptography over classical cryptography: $n$ qubits of communication suffice for security against preprocessing attacks with space up to $N^2$ rather than $N$. 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 time-space tradeoffs for breaking post-quantum cryptography. We also prove the following results: (1) We tighten Liu's analysis of post-quantum PRGs in QROM, achieving a distinguishing advantage bound of $O(\frac{T^2}N + \sqrt{\frac{ST}N})$. (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 $O(\frac{\sqrt{S}}N)$ bound for the pseudorandomness of random binary phase states against space $S$ distinguishers.
1 Introduction
The introduction asks how much time and preprocessing advice are needed to break quantum cryptographic primitives, focusing on single-copy one-way states and pseudorandom states. It presents time-space bounds, a trace-moment approach, and extensions to post-quantum PRGs and unitary synthesis.
1.1 Our Results
- Whether the preprocessing term in the time-space tradeoff is tight remains open.Source: §1.1 · p. 5
- The results identify an advantage for quantum cryptography: n-qubit communication can provide security against preprocessing space up to N^2, rather than N in the classical setting.The introduction also states that the time-space tradeoff beats the classical post-quantum setting for T much smaller than N.Source: §1.1 · p. 6
- The proof expresses an optimal preprocessing attack's success probability as the maximum eigenvalue of a random matrix, bounds it with trace moments, and analyzes those moments using compressed oracles.Source: §1.1 · p. 6
- The paper improves the post-quantum PRG security bound in the auxiliary-input QROM, with a distinguishing-advantage result for T queries and S advice qubits.The text attributes the improvement to a centered random matrix and a reduction to a variant of uniform PRG security.Source: §1.1 · p. 6
- For single-copy pseudorandom states at T=0, the paper proves a tight bound and states that their quantitative security matches one-wayness; a symmetric-subspace attack matches up to logarithmic factors.Source: §1.1 · p. 7
- For unitary synthesis, the paper extends one-query lower bounds to adversaries making one arbitrary function query and polynomially many adaptive random-oracle queries, including queries before and after the function query.The bounds for queries on both sides are weaker, but remain negligible for exponential K and polynomial query and workspace resources.Source: §1.1 · p. 8
- The unitary-synthesis analysis interprets prior results [LMW24] through compressed oracles after spectral relaxation removes the arbitrary function query.The same interpretation accommodates actual random-oracle queries.Source: §1.1 · p. 8
2 Technical Overview
The overview reduces adversarial success to expected operator norms of random matrices and bounds them through trace moments interpreted using compressed oracles. It sketches how the approach handles one-way states, online queries, and several additional settings.
2.1 One-way states with no online queries
- The optimal advice-dependent success probability is expressed as the expected operator norm of a positive semidefinite matrix acting on the advice register.The trace-moment approach replaces a concentration bound that the overview says is suboptimal and does not generalize to T>0.Source: §2.1 · p. 9
- To bound the operator norm of a random matrix, the method considers 2S-th trace moments and chooses moment order S; the trace over 2S basis vectors incurs only a constant-factor loss.Each term starts from an oracle-independent state |z⟩; for fixed z, |φ_S⟩ is supported on compressed oracle databases of size at most 2S.Source: §2.1 · pp. 9–10
- The no-online-query argument generalizes Liu’s alternating measurement game by tracking a joint advice–database state supported on sparse databases.The overview identifies the expectation against a partially filled database as the one-way-state inversion game.Source: §2.1 · p. 10
- For one-way states without online queries, the challenge-preparation analysis separates heavy rows, deleted entries, and newly added entries in the compressed database.For heavy rows, the squared norm is at most ℓ/(Kh); for hits on existing entries, it is at most ℓ/(KN).Source: §2.1 · p. 11
2.2 Adding online queries
- Online oracle queries help inversion only as much as Grover’s algorithm moves a state toward the solution.The analysis purifies the oracle; each application of the resulting operator can increase compressed-database size by at most 2(T+1).Source: §2.2 · p. 12
2.3 Sketches of our additional results
- For pseudorandomness with T=0, the overview uses a centered matrix and an alternative ordered-database purification in its small-database analysis.Challenge generation is described as swaps between existing database positions and a new one, increasing database size by one.Source: §2.3 · p. 13
- For one-query unitary synthesis, the weight-vector decomposition yields a small-database search bound of O((ℓ+1)/K).The overview applies a compressed-oracle analysis to the spectral relaxation and splits rescaling into addition and deletion terms.Source: §2.3 · pp. 13–14
- In the hybrid synthesis model, queries before the function query are removed with a suboptimal polynomial security loss, while the analysis focuses on later queries.The described later-query argument swaps the challenge row with an unused independent row and bounds the initial swap and subsequent query hybrid separately.Source: §2.3 · p. 14
3 Preliminaries
The preliminaries set notation and the random-oracle model, then define the cryptographic settings and oracle-game tools used in the paper. They also state a decomposition that represents an isometry through a fixed weight vector.
3 Preliminaries
- The preliminaries use N for the dimension of the main n-qubit register and M for the post-isometry workspace dimension.They define [K] as {1,…,K} and specify that binary-string addition and multiplication are over F_2^n.Source: §3 · p. 14
3.1 Quantum random oracle model and compressed oracles
- Compressed oracles represent the oracle’s purification register as a database in the Fourier basis, where each query changes at most one entry.The random function is sampled once and fixed; quantum queries act through the corresponding oracle unitary.Source: §3.1 · pp. 14–15
3.2 Canonical quantum bit commitments
- Canonical quantum bit commitments prepare Q_b|0⟩ on commit and witness registers, send the commit register, and verify opening with projector Π_b.The commitment definitions separately specify hiding and binding notions, including computational, statistical, and perfect variants.Source: §3.2 · p. 15
3.3 1OWS and 1PRS
- Single-copy one-way states require recovering the sampled key from one challenge-state copy; random binary phase states are information-theoretically insecure when the full oracle table is known.The stated motivation is to study the query and advice resources needed for recovery.Source: §3.3 · p. 16
- The single-copy pseudorandom-state variant gives the adversary one challenge-state copy and compares against a Haar-random pure state.Its stated parameter condition is n(λ) ≥ κ(λ)+1.Source: §3.3 · p. 16
3.4 The unitary synthesis problem
- Unitary-synthesis hardness is studied through oracle-state distinguishing and search games, which prior reductions connect to synthesis hardness for suitable state families.The paper analyzes these games for random binary phase states.Source: §3.4 · p. 17
3.5 One-query oracle algorithms
- A one-query unitary-synthesis algorithm is represented by a pre-query isometry, a phase-oracle query, and a designated output subsystem.The algorithm prepares U O_f V|ψ⟩ before producing that subsystem.Source: §3.5 · p. 17
3.6 The weight-vector decomposition
- The weight-vector decomposition associates an isometry V with a unit vector |wt_V⟩ and a diagonal operator D_V, yielding a decomposition of V|ψ⟩.The construction sets diagonal entries to zero when the corresponding p_i is zero.Source: §3.6 · p. 18
4 Our use of the trace moment method
The paper’s trace-moment strategy first expresses success or distinguishing advantage through an expected operator norm, then interprets trace moments using compressed oracles. The database-size changes under repeated applications guide the small-database analysis.
4 Our use of the trace moment method
- Winning probability or distinguishing advantage is bounded by the expected operator norm of a random Hermitian matrix.For quantum advice, the matrix arises by optimizing over the advice state; for one-query synthesis, it arises from a spectral relaxation.Source: §4 · pp. 18–19
- The trace moment is represented with a compressed oracle whose database starts empty, and each matrix application changes database size by a bounded amount.The analysis then controls the operator on states supported on small databases; choosing t on the order of the input qubits makes the trace-dimension factor constant or cancels a union bound or guessing factor.Source: §4 · p. 19
5 Non-uniform security of 1OWS in QROM
Section 5 establishes non-uniform QROM security for single-copy binary phase states and proves it using trace moments and compressed-oracle databases. Its proof reduces the analysis to search from bounded-size initial databases.
5 Non-uniform security of 1OWS in QROM
- The section proves non-uniform QROM security for single-copy binary phase states against T-query algorithms with S-qubit advice.The security game gives the adversary an oracle-dependent advice state before the challenge and asks it to recover the challenge key.Source: §5 · p. 19
5.1 1OWS trace-moment setup
- The trace-moment setup expresses optimal search probability through an operator norm on the advice register, then bounds its expectation using compressed-oracle representations.The proof overview reduces the main step to bounding search when the algorithm begins entangled with a compressed database of size at most ℓ.Source: §5 · p. 20, §5.1 · pp. 20–21
5.2 Bounding the trace moment on small databases
- The small-database lemma recasts the trace-moment bound as a search game beginning with a state supported on compressed databases of size at most ℓ.The game supplies one phase-state challenge for a uniformly random key, allows T compressed-oracle queries, and asks the algorithm to guess the key.Source: §5.2 · pp. 21–22
5.3 Proof of the 1OWS bound for states with initial database
- The proof separates cases by challenge-row database size and whether the challenge entry remains in the final database, accounting for entries present initially or added by queries.It uses the distinction to bound contributions from database occupancy and query history.Source: §5 · p. 20, §5.2 · p. 22
6 Non-uniform security of 1OWS in QROM, with classical advice
Section 6 analyzes one-way-state security when preprocessing advice is classical. It uses fixed-advice scalar moments and a controlled change of purification to derive a stronger lower-bound condition than for quantum advice when K < N.
6 Non-uniform security of 1OWS in QROM, with classical advice
- For constant success, classical advice requires ST = Ω(KN), versus ST = Ω(K^2) with quantum advice; the conditions agree at K = N, and the classical bound is stronger by N/K when K < N.The comparison concerns non-uniform security in the QROM.Source: §6 · p. 26
6.1 1OWS scalar-moment setup
- Fixing the classical advice string makes the success probability a scalar, so its compressed-oracle representation acts only on the database register.The initial algorithm state is fixed and unentangled with the database, even when the database is in a superposition.Source: §6 · p. 27, §6.1 · p. 27
6.3 Proof of the 1OWS bound for an initial database state
- A controlled change of purification compares the real challenge with a maximally mixed challenge independent of the hidden key; the conjugated oracle differs only on hidden-row queries.A query-by-query hybrid contributes O(T/K) to the success amplitude.Source: §6 · p. 27, §6.3 · p. 29
6.2 Bounding the scalar moment on small databases
- The scalar-moment lemma bounds the game when its initial database state is supported on databases of size at most ℓ.The supplied passage states the lemma’s scope but does not give its bound’s formula.Source: §6.2 · p. 28
7 Quantum bit commitments in QROM
Section 7 constructs canonical quantum bit commitments in the QROM from single-copy one-way-state security. The resulting scheme is perfectly correct, statistically hiding, and computationally binding, with a flavor conversion giving the opposite computational and statistical properties.
7 Quantum bit commitments in QROM
- The construction uses one-way-state security for computational binding and statistical hiding; flavor conversion yields computational hiding and statistical binding.The conversion’s computational security changes from ε to √ε.Source: §7 · pp. 31–33
- The constructed canonical commitment is perfectly correct, statistically hiding, and computationally binding in the QROM.The construction is based on non-uniform security of single-copy one-way states.Source: §7 · p. 31
- For K = N^(1-Ω(1)), the two post-commitment receiver states have negligible trace distance with probability 1−negl(n).The passage bounds the probability that the trace distance exceeds ε by K^-1/(4Nε^2).Source: §7 · p. 32
8 Non-uniform security of PRGs in QROM
Section 8 tightens non-uniform PRG security in the QROM for quantum advice and queries. Its proof uses trace moments and compressed-oracle databases, with a resampling hybrid for the bounded-database step.
8 Non-uniform security of PRGs in QROM
- The section proves a tighter non-uniform PRG security bound in the QROM for T-query algorithms with S-qubit advice.The game distinguishes a random image from an independent range element.Source: §8 · p. 33
- The resulting bound recovers the classical result’s S(T+1)/N dependence while allowing quantum advice and quantum oracle access.The comparison is with the classical-query, classical-advice result of.Source: §8 · p. 33
8.1 PRG trace-moment setup
- The trace-moment setup expresses distinguishing advantage as the expected norm of a Hermitian operator, then bounds the trace moment using compressed-oracle databases.The small-database step considers states supported on databases of size at most ℓ.Source: §8 · p. 33, §8.1 · p. 34, §8.2 · p. 35
8.2 Bounding the trace moment on small databases
- A resampling hybrid replaces the hidden-seed oracle value with an independent challenge value, separating initial-database overlap from the query contribution.The ideal-experiment contribution is bounded using a modified BBBV-style hybrid argument.Source: §8 · pp. 33–34
9 One-query unitary synthesis lower bound from compressed oracles
The section gives a common compressed-oracle trace-moment approach to reprove one-query unitary-synthesis lower bounds for oracle-state search and distinguishing games. The analyses reduce to bounding operators on small compressed databases.
9 One-query unitary synthesis lower bound from compressed oracles
- The compressed-oracle trace-moment approach recovers the asymptotic one-query lower bounds for oracle-state search and distinguishing [LMW24].It provides a common alternative to the earlier analyses for both games.Source: §9 · p. 36
9.1 Oracle state search game hardness
- In the search game, an adversary receives one copy of a randomly keyed state and makes one oracle query before outputting the key.The oracle may depend on the sampled random variable but not on the challenge key.Source: §9.1 · p. 36
9.1.1 Spectral relaxation and the compressed oracle trace moment
- Search success is bounded by an expected operator norm after weight-vector decomposition and spectral relaxation, then by trace moments in compressed-database mode.The small-database estimate is the key remaining step in the proof overview.Source: §9 · p. 36, §9.1 · p. 37
9.1.2 Bounding the trace moment on small databases
- For search, database components containing the challenge entry are bounded by grouping terms at cost O(ℓ), while absent entries are charged to the initial database.Since each moment-operator application changes database size by at most two, a moment of order 1 + log M yields the asymptotic bound.Source: §9.1 · p. 37
9.2.1 Spectral relaxation and the compressed oracle trace moment
- For distinguishing, symmetrization with an independent oracle copy preserves centering, and swapping the challenge-key rows makes the operator difference depend on occupied database rows.An order-(1 + log M) trace-moment argument proves the one-query theorem.Source: §9.2 · p. 41
9.2.2 Bounding the trace moment on small databases
- The distinguishing analysis also reduces to a small-database bound, under the condition ℓ + 1 ≤ K.The game distinguishes a keyed state from a uniformly sampled computational-basis state with one oracle query.Source: §9.2 · p. 40, §9.2.2 · p. 42
10 One-query unitary synthesis with additional query access to 𝑅
The section extends one-query unitary-synthesis search and distinguishing bounds to adversaries with random-oracle queries before or after the arbitrary phase query. Post-query access is handled by compressed-oracle analysis; pre-query access is reduced to that setting by resampling.
10 One-query unitary synthesis with additional query access to 𝑅
- The adversary may make t random-oracle queries before and T after one arbitrary phase query, whose function may depend on the oracle but not on the challenge.All non-oracle operations are fixed independently of the random oracle.Source: §10 · p. 46
10.1.1 Spectral relaxation with post-query access
- For post-query access, the preceding isometry remains independent of the oracle, so the spectral relaxation still applies and the compressed-oracle analysis bounds the remaining trace moment.The pre-query setting does not admit this analysis directly because its preceding isometry depends on the oracle.Source: §10 · p. 46, §10.1 · p. 46
10.3 Extension to the setting with both post-query and pre-query access
- For pre-query access, resampling a randomly selected subset of oracle rows reduces security to the post-query case, with quantitatively weaker bounds.When K is exponential and query counts and workspace qubits are polynomial, the resulting success probability and distinguishing advantage remain negligible.Source: §10 · p. 46, §10.3 · p. 53
10.1.3 Proof of the search bound with post-query
- O((ℓ + T² + 1)/K) bounds the search success contribution on databases of size at most ℓ in the post-query analysis.The proof introduces an unqueried auxiliary row and swaps it with the challenge row to handle correlations between the challenge and subsequent queries.Source: §10.1 · pp. 46–47
10.1.2 From the small-database estimate to the trace moment
- The search trace-moment argument accommodates post-query accesses by allowing each moment operator to change database size by at most 2(T + 1).The resulting theorem extends the one-query search bound to adversaries making T random-oracle queries after the arbitrary phase query.Source: §10.1 · p. 46, §10.1.2 · p. 48
10.2 Oracle state distinguishing game with post-query access
- The distinguishing analysis extends the [LMW24] one-query lower bound to adversaries making polynomially many random-oracle queries after the arbitrary query.It follows the search hybrids for the centered distinguishing operator.Source: §10.1 · p. 47, §10.2 · p. 51
11 Quantum advice lower bound for 1PRS
The section proves a zero-online-query quantum-advice lower bound for distinguishing a random binary phase state from a Haar-random state. Its proof uses trace moments and a small-database analysis of challenge-state interference.
11 Quantum advice lower bound for 1PRS
- For zero-query adversaries, constant distinguishing advantage requires Ω(K²) advice qubits, or Ω(N²) when K = Θ(N).The result concerns distinguishing a single random binary phase state from a Haar-random state without online random-oracle queries.Source: §11 · pp. 55–56
11.1 1PRS trace-moment setup
- The proof bounds the expected norm of a centered acceptance matrix using trace moments in compressed-oracle database mode.The advice register and challenge register are combined in the acceptance operator.Source: §11.1 · p. 56
11.2 Proof of the small-database bound
- The small-database analysis compares the real challenge with an independent maximally mixed state and represents interference between different new-entry choices as swaps.Challenge preparation can add or delete a database entry; deletions have small total weight.Source: §11.2 · p. 57
11.3 An elementary bound for the swap operator
- An auxiliary empty-slot model makes distinct swaps orthogonal, yielding square-root dependence on the number m of stored entries.This supports the partial-trace swap-operator bound used in the small-database proof.Source: §11.3 · pp. 59–61
A A matching zero-query attack for 1OWS and 1PRS
A symmetric-subspace attack uses stored copies of selected states to test a challenge sequentially, achieving zero-query guarantees for both 1OWS key recovery and 1PRS distinguishing. Its performance matches the paper’s zero-query bounds up to logarithmic factors.
A A matching zero-query attack for 1OWS and 1PRS
- The attack matches the zero-query bounds for both 1OWS and 1PRS up to logarithmic factors.Constant success probability or distinguishing advantage is achievable with O(K^2 log N) advice qubits and no online queries.Source: A A matching zero-query attack for… · pp. 63, 65
- With 32L^2 log N advice qubits and no queries, the attack achieves 1OWS key-recovery probability at least 3L/4K and 1PRS distinguishing advantage at least L/2K, averaged over the random oracle.The guarantees hold for K ≤ N and 1 ≤ L ≤ min{K, N/32}.Source: A A matching zero-query attack for… · p. 63
- The attack stores copies for a fixed set of keys and tests the challenge sequentially with symmetric-subspace measurements, stopping at the first acceptance.The quantum union bound controls disturbance from earlier tests; non-matching tests have expected acceptance probability 1/N.Source: A A matching zero-query attack for… · p. 64
B Proof of Lemma 6.3
The proof builds single-key database unitaries and applies them key by key to establish Lemma 6.3. Its construction relies on advice being unentangled with the database, so it does not extend to arbitrary quantum advice.
B Proof of Lemma 6.3
- The single-key construction supplies a unitary acting only on D0B and as identity on the x register, with an estimate parameterized by the database’s expected size.It begins with a normalized vector on a single-key database register.Source: B Proof of Lemma 6.3 · p. 65
- The proof factors M+ through its Gram matrix G+ and an isometry V, extending the vectors associated with zero eigenvalues to an orthonormal family.The constructed vectors are orthonormal because their inner products are given by G+ in its eigenbasis.Source: B Proof of Lemma 6.3 · p. 66
- Wβ maps the orthonormal encoding of x into B while restoring |β⟩ on the database row.The two orthonormal families are completed to bases and mapped by a unitary.Source: B Proof of Lemma 6.3 · pp. 66–67
- Applying the construction key by key yields a unitary W that commutes with oracle-independent algorithm operations and the key-diagonal measurement.On other-key blocks, oracle operations act as scalars; W also leaves algorithm registers untouched.Source: B Proof of Lemma 6.3 · p. 67
- The argument does not extend to arbitrary quantum advice because entanglement can force the constructed unitary to act on advice register A.That would prevent the needed commutation with algorithm operations and the measurement.Source: B Proof of Lemma 6.3 · p. 68
C Proof of Lemma 8.3
The proof of Lemma 8.3 decomposes the operator into two parts and bounds their expected contributions in the compressed-oracle framework. A simpler subsection interprets the bound through a distinguishing game with a bounded-size database.
C Proof of Lemma 8.3
- The proof writes YR as ΓR + BR and bounds the expected compressed-oracle contributions of the two terms separately.Linearity gives the corresponding decomposition of the compressed operator.Source: C Proof of Lemma 8.3 · p. 68
- For Γ, the compressed-oracle operator vanishes on databases where x is absent because that projection averages the oracle value at x.The proof also relates the projectors onto databases containing x to database size.Source: C Proof of Lemma 8.3 · p. 69
- For B, the proof compares query-by-query states under the compressed oracle and a variant that changes the response at x without changing the database.It combines the resulting average-state difference with orthogonality across distinct x; together with the Γ bound, this proves the lemma.Source: C Proof of Lemma 8.3 · pp. 69–70
C.1 A weaker but simpler bound
- The simpler bound is recast as distinguishing whether a challenge is R(x) or a random range string, after which the algorithm may make T oracle queries.The initial state is supported on databases of size at most ℓ, and the game’s advantage is identified with the expression being bounded.Source: §C.1 · p. 70
- The resulting bounded-database distinguishing problem is the target of the proof, and the text notes its study in [CGLQ20, Lemma 5.9].The interpretation is used to derive Lemma 8.3.Source: §C.1 · pp. 70–71