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arXiv:2204.14205 · Electronic Proceedings in Theoretical Computer Science

Symbolic Synthesis of Clifford Circuits and Beyond

Matthew Amy, Owen Bennett-Gibbs, Neil J. Ross

Published Nov 14, 2023• 6 citations
arXivHTMLPDF

Abstract

Path sums are a convenient symbolic formalism for quantum operations with applications to the simulation, optimization, and verification of quantum protocols.Unlike quantum circuits, path sums are not limited to unitary operations, but can express arbitrary linear ones.Two problems, therefore, naturally arise in the study of path sums: the unitarity problem and the extraction problem.The former is the problem of deciding whether a given path sum represents a unitary operator.The latter is the problem of constructing a quantum circuit, given a path sum promised to represent a unitary operator.In this paper, we show that the unitarity problem is co-NP-hard in general, but that it is in P when restricted to Clifford path sums.We then provide an algorithm to synthesize a Clifford circuit from a unitary Clifford path sum.The circuits produced by our extraction algorithm are of the form C 1 HC 2 , where C 1 and C 2 are Hadamard-free circuits and H is a layer of Hadamard gates.We also provide a heuristic generalization of our extraction algorithm to arbitrary path sums.While this algorithm is not guaranteed to succeed, it often succeeds and typically produces natural looking circuits.Alongside applications to the optimization and decompilation of quantum circuits, we demonstrate the capability of our algorithm by synthesizing the standard quantum Fourier transform directly from a path sum.

DOI
10.4204/eptcs.394.17

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Path sums are a convenient symbolic formalism for quantum operations with applications to the simulation, optimization, and verification of quantum protocols.Unlike quantum circuits, path sums are not limited to unitary operations, but can express arbitrary linear ones.Two problems, therefore, naturally arise in the study of path sums: the unitarity problem and the extraction problem.The former is the problem of deciding whether a given path sum represents a unitary operator.The latter is the problem of constructing a quantum circuit, given a path sum promised to represent a unitary operator.In this paper, we show that the unitarity problem is co-NP-hard in general, but that it is in P when restricted to Clifford path sums.We then provide an algorithm to synthesize a Clifford circuit from a unitary Clifford path sum.The circuits produced by our extraction algorithm are of the form C 1 HC 2 , where C 1 and C 2 are Hadamard-free circuits and H is a layer of Hadamard gates.We also provide a heuristic generalization of our extraction algorithm to arbitrary path sums.While this algorithm is not guaranteed to succeed, it often succeeds and typically produces natural looking circuits.Alongside applications to the optimization and decompilation of quantum circuits, we demonstrate the capability of our algorithm by synthesizing the standard quantum Fourier transform directly from a path sum.
Optimal circuit synthesis is the problem of finding the shortest-depth circuit representation of a given functionality with respect to a pre-specified elementary gate set. This is a central problem in both quantum and classical hardware design. While the classical version is very well understood -- both in terms of heuristics and rigorous hardness assertions -- much less is known about optimal quantum circuit synthesis. We focus on optimal Clifford circuit synthesis, for which various heuristics are known, e.g. via reduction to 3-SAT. Our main result supplies a matching hardness result: a variant of optimal Clifford synthesis is NP-hard. The proof proceeds in two parts: (i) reduce 3-edge colorability on 3-regular graphs to a circuit synthesis problem that only involves CZ gates, (ii) prove that the availability of additional elementary Clifford gates -- most notably: Hadamard, phase and CNOT -- cannot lead to further improvements of the optimal circuit depth. Our work sharpens the complexity-theoretic understanding of Clifford circuits: simulation and equivalence checking are in P, whereas deciding whether a Clifford unitary admits an implementation within a prescribed depth is NP-complete.
We show that the problem of synthesizing a quantum circuit for a given Clifford operator using at most a specified amount of resources is NP-complete for several resource models of practical interest. Specifically, we study synthesis of a given Clifford operator using single-qubit Clifford gates and two-qubit Clifford gates, with full qubit connectivity and no additional ancilla qubits. With single-qubit gates free and CNOT as the only two-qubit gate, we show that deciding whether a given target admits an implementation using at most a specified number of CNOT gates is NP-complete, and we show that the problem remains NP-complete when any two-qubit Clifford gate is allowed and the budget counts all such gates. Moreover, we show that both results continue to hold when SWAP gates may be inserted for free, and when single-qubit gates are also considered part of the budget. In each case, we build on recent work by Li and Guan and show that there are polynomial-time reductions from Hamiltonian path in grid graphs to these decision problems, using a number of qubits quadratic in the number of grid vertices.
Minimizing the number of CNOT gates required to synthesize a Clifford operator is a central problem in quantum circuit optimization. We extend the link--middle--cut (LMC) framework for linear reversible circuits to Clifford operators represented by binary symplectic tableaux. By introducing block-support connectivity graphs and determinantal invariants, we obtain efficiently computable lower bounds on ancilla-free CNOT complexity. We prove that these bounds are tight for qubit permutations: a permutation of nnn qubits with kkk cycles requires exactly 3(n−k)3(n-k)3(n−k) CNOT gates, even when arbitrary one-qubit Clifford gates are available. We also derive efficiently computable bounds for synthesis up to a permutation of the output qubits, corresponding to free qubit relabeling. Finally, we further demonstrate the utility of the bound by using it as an admissible heuristic for A∗A^*A∗-based Clifford synthesis and show that, on benchmark instances, the resulting search can prove optimality beyond what was possible with earlier SAT-based methods, and that it may be used in combination with earlier A∗A^*A∗ heuristics to improve the CNOT counts obtained through heuristic search.
This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a specific gate count, or a combination of these factors. While past research has focused predominantly on continuous gate sets, synthesizing unitaries from the parameter-free Clifford+T gate set remains a challenge. Although the time complexity of this task will inevitably remain exponential in the number of qubits for general unitaries, reducing the runtime for simple problem instances still poses a significant challenge. In this study, we apply the tree-search method Gumbel AlphaZero to solve the problem for a subset of exactly synthesizable Clifford+T unitaries. Our method effectively synthesizes circuits for up to five qubits generated from randomized circuits with up to 60 gates, outperforming existing tools like QuantumCircuitOpt and MIN-T-SYNTH in terms of synthesis time for larger qubit counts. Furthermore, it surpasses Synthetiq in successfully synthesizing random, exactly synthesizable unitaries. These results establish a strong baseline for future unitary synthesis algorithms.
Quantum computers are only as powerful as the compilers that optimize the circuits they run. As quantum hardware scales, reducing gate counts and circuit depth becomes critical for practical computation. This work implements a core synthesis algorithm in OCaml that maps arbitrary Cliffords to circuits, a fundamental building block in quantum circuit compilation. The Clifford synthesis implementation is designed around how LambdaPC thinks about quantum computation, through Pauli operators. It frames synthesis as a search problem: given a Clifford tableau, find a path to the identity by applying gates. Working factor by factor, it reduces each Pauli pair (P, Q) to a canonical (Z_i, X_i) form using single-qubit Clifford gates and TQE gates, until the full tableau is decomposed into an executable circuit. The main challenge was working within LambdaPC's typed representation, where n-qubit Paulis are encoded as nested pairs. This required helper functions to extract Pauli data from those structures and pattern-matching logic to find minimal gate sequences, laying the groundwork for end-to-end Clifford compilation in LambdaPC.
Random Clifford operators have numerous applications in quantum computing, including randomized benchmarking, classical shadows, and quantum authentication. However, sampling and implementing uniformly random nnn-qubit Clifford incur near-quadratic complexity due to the size of Clifford group. We introduce a cryptographic way to overcome these barriers: trapdoored Clifford operator distributions whose samples are computationally indistinguishable from uniformly random Cliffords, yet implementing them can be much faster given the trapdoor. We construct a distribution of trapdoored Clifford operators whose elements can be sampled and implemented in near-linear time under a variant of the learning parity with noise assumption. Our constructions allow fast tableau action on Pauli labels for classical simulation, and also can be optimized to admit polylogarithmic-depth implementation. Along the way, we construct trapdoored matrices over finite fields that support efficient multiplication by both a matrix and its inverse, resolving an open question left by Vaikuntanathan and Zamir [SODA'26]. We use these constructions to obtain faster protocols based on random Cliffords. We also explore their applications to the worst-case to average-case reductions for matrix and Clifford problems including the iterated matrix multiplication and Clifford circuit synthesis. In particular, we show the hardness of batching Clifford circuits: synthesizing circuits that apply the same Clifford to multiple registers is at least as hard as worst-case matrix multiplication, even when synthesis succeeds on a small constant fraction of random Cliffords. This extends to approximate implementations by general quantum circuits.