We present a polynomial-time quantum algorithm making a single query (in superposition) to a classical oracle, such that for every state |ψ〉 there exists a choice of oracle that makes the algorithm construct an exponentially close approximation of |ψ〉. Previous algorithms for this problem either used a linear number of queries and polynomial time, or a constant number of queries and polynomially many ancillae but no nontrivial bound on the runtime. As corollaries we do the following:
We present a polynomial-time quantum algorithm making a single query (in superposition) to a classical oracle, such that for every state |ψ〉 there exists a choice of oracle that makes the algorithm construct an exponentially close approximation of |ψ〉. Previous algorithms for this problem either used a linear number of queries and polynomial time, or a constant number of queries and polynomially many ancillae but no nontrivial bound on the runtime. As corollaries we do the following:
We give a positive answer to the constant-error formulation of the Aaronson–Kuperberg unitary synthesis problem. For every n, a quantum oracle circuit generated in polynomial time from n alone can approximate the channel of every n-qubit unitary to full diamond-norm error at most 1/2, after a suitable Boolean oracle is chosen. Using the fixed gates H,T,T^\dagger,\mathrm{CNOT}, the circuit has polynomially many qubits, elementary gates, and oracle calls, and its oracle queries have polynomial length. The oracle may depend on the target unitary; the theorem does not give an efficient classical procedure for constructing it.
The unitary synthesis problem (Aaronson-Kuperberg, CCC 2007) asks whether every n-qubit unitary U is computable by efficient quantum circuits relative to some classical oracle f=fU depending on U. Recently, Lombardi-Ma-Wright (STOC 2024) proved that Haar-random unitaries cannot be efficiently synthesized by algorithms that make 1 query (or poly(n) parallel queries) to an arbitrary classical oracle. In this work, we prove several results about the hardness (and easiness!) of variants of unitary synthesis. Our results include: (1) 1-query vs. 2-query unitary synthesis: we prove 1-query lower bounds for synthesizing random permutation unitaries P∣x⟩=∣π(x)⟩, as well as random alternating-basis phase unitaries F2⋅H⊗n⋅F1. This gives 1-query lower bounds for "explicit" families of unitaries that have efficient (even 2-query) synthesis algorithms. (2) Upper bound for complex phase unitaries: we also consider complex phase unitaries ∣x⟩↦αx∣x⟩, which have a clean 2-query synthesis algorithm with no obvious 1-query algorithm. In this case, we prove an upper bound: there are 1-query algorithms (relative to binary phase oracles) that constant-approximate these unitaries in diamond distance. In order to prove our lower bounds, we introduce and analyze two new cryptographic games: the oracle state search game and the oracle Choi state game. Compared to prior work, our framework is mathematically simple, more flexible in what it can prove, and more accurately captures the hardness of synthesizing unitaries that are not "fully random". Finally, we also use the search game to prove a new hardness-of-approximation result for quantum programs (synthesizing unitaries relative to quantum advice) for phase unitaries, giving a sharper separation between 1-query unitary synthesis and quantum programs.
The Unitary Synthesis Problem (Aaronson-Kuperberg 2007) asks whether any n-qubit unitary U can be implemented by an efficient quantum algorithm A augmented with an oracle that computes an arbitrary Boolean function f. In other words, can the task of implementing any unitary be efficiently reduced to the task of implementing any Boolean function? In this work, we prove a one-query lower bound for unitary synthesis. We show that there exist unitaries U such that no quantum polynomial-time oracle algorithm Af can implement U, even approximately, if it only makes one (quantum) query to f. Our approach also has implications for quantum cryptography: we prove (relative to a random oracle) the existence of quantum cryptographic primitives that remain secure against all one-query adversaries Af. Since such one-query algorithms can decide any language, solve any classical search problem, and even prepare any quantum state, our result suggests that implementing random unitaries and breaking quantum cryptography may be harder than all of these tasks. To prove this result, we formulate unitary synthesis as an efficient challenger-adversary game, which enables proving lower bounds by analyzing the maximum success probability of an adversary Af. Our main technical insight is to identify a natural spectral relaxation of the one-query optimization problem, which we bound using tools from random matrix theory. We view our framework as a potential avenue to rule out polynomial-query unitary synthesis, and we state conjectures in this direction.
This presentation gives a comprehensive overview of quantum query algorithms, beginning with an introduction to the query model of computation where input is accessed via an oracle function. It then details several foundational quantum algorithms that demonstrate a quantum advantage over classical approaches: Deutsch's problem is solved with a single query using phase kickback to determine if a function is constant or balanced; the Deutsch-Jozsa algorithm extends this to n-bit inputs, efficiently distinguishing constant from balanced functions with one query versus an exponential need classically; the Bernstein-Vazirani algorithm identifies a hidden string with one query, outperforming classical linear requirements; and Simon's algorithm, which finds a hidden period in a two-to-one function with a linear number of queries, showcases an exponential speedup over the best classical probabilistic algorithms.
Quantum linear-system algorithms access the coefficient matrix through a block-encoding oracle UA and the right-hand side through a state-preparation oracle Ub. The optimal query complexities of these two oracles are known separately: O(κlog(1/ε)) queries to UA and O(κ/s) queries to Ub, where s=α∥A−1b∥. Whether a single algorithm can achieve both bounds simultaneously has remained open.
We resolve this question affirmatively. Given a constant-factor estimate of s, we give a quantum linear-system algorithm that prepares the normalized solution to error ε using simultaneously $ O\left(κ\log\frac1\varepsilon\right)$ queries to UA and O(κ/s) queries to Ub. The algorithm separates state preparation from precision refinement, so only the matrix-query count depends on the target accuracy.
We also prove a matching same-instance worst-case lower bound: for every quantum linear-system algorithm and every solution-norm scale, there exists a single Hermitian instance on which that algorithm requires both Ω(κlog(1/ε)) matrix queries and Ω(κ/s) state-preparation queries. Thus the two oracle complexities can be optimized simultaneously, and the resulting bounds are jointly optimal.
In the quantum linear systems problem (QLSP), we are given query access to a d-sparse N×N matrix A with condition number κ, and the ability to prepare a quantum state proportional to a vector b. The goal is to output an ε-approximation to the quantum state proportional to the solution x of Ax=b. Following a long line of work, the best previously known quantum algorithms for the QLSP had query complexities O(κdlog(1/ε)) and κd(κd/ε)o(1), while the best known lower bounds were Ω(κlog(1/ε)) and Ω(κd). We improve these bounds and show that the complexity of the QLSP is Θ(κdlog(1/ε)). We also resolve an open problem of Berry and Childs by showing that any N×N unitary can be implemented with bounded error using O(N) queries to its matrix entries.