This thesis investigates techniques for the automated optimization of quantum circuits. In the first part we develop an exponential time algorithm for synthesizing minimal depth quantum circuits. We combine this with effective heuristics for reducing the search space, and show how it can be extended to different optimization problems. We then use the algorithm to compute circuits over the Clifford group and T gate for many of the commonly used quantum gates, improving upon the former best known circuits in many cases. \n \nIn the second part, we present a polynomial time algorithm for the re-synthesis of CNOT and T gate circuits while reducing the number of phase gates and parallelizing them. We then describe different methods for expanding this algorithm to optimize circuits over Clifford and T gates.
This thesis investigates techniques for the automated optimization of quantum circuits. In the first part we develop an exponential time algorithm for synthesizing minimal depth quantum circuits. We combine this with effective heuristics for reducing the search space, and show how it can be extended to different optimization problems. We then use the algorithm to compute circuits over the Clifford group and T gate for many of the commonly used quantum gates, improving upon the former best known circuits in many cases. \n \nIn the second part, we present a polynomial time algorithm for the re-synthesis of CNOT and T gate circuits while reducing the number of phase gates and parallelizing them. We then describe different methods for expanding this algorithm to optimize circuits over Clifford and T gates.
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.
The design and compilation of correct, efficient quantum circuits is integral to the future operation of quantum computers. This thesis makes contributions to the problems of optimizing and verifying quantum circuits, with an emphasis on the development of formal models for such purposes. We also present software implementations of these methods, which together form a full stack of tools for the design of optimized, formally verified quantum oracles. \n \nOn the optimization side, we study methods for the optimization of Rz and CNOT gates in Clifford+Rz circuits. We develop a general, efficient optimization algorithm called phase folding, which reduces the number of Rz gates without increasing any metrics by computing its phase polynomial. This algorithm can further be combined with synthesis techniques for CNOT-dihedral operators to optimize circuits with respect to particular costs. We then study the optimal synthesis problem for CNOT-dihedral operators from the perspectives of Rz and CNOT gate optimization. In the case of Rz gate optimization, we show that the optimal synthesis problem is polynomial-time equivalent to minimum-distance decoding in certain Reed-Muller codes. For the CNOT optimization problem, we show that the optimal synthesis problem is at least as hard as a combinatorial problem related to Gray codes. In both cases, we develop heuristics for the optimal synthesis problem, which together with phase folding reduces T counts by 42% and CNOT counts by 22% across a suite of real-world benchmarks. \n \nFrom the perspective of formal verification, we make two contributions. The first is the development of a formal model of quantum circuits with ancillary bits based on the Feynman path integral, along with a concrete verification algorithm. The path integral model, with some syntactic sugar, further doubles as a natural specification language for quantum computations. Our experiments show some practical circuits with up to hundreds of qubits can be efficiently verified. Our second contribution is a formally verified, optimizing compiler for reversible circuits. The compiler compiles a classical, irreversible language to reversible circuits, with a formal, machine-checked proof of correctness written in the proof assistant F*. The compiler is structured as a partial evaluator, allowing verification to be carried out significantly faster than previous results.
The current phase of quantum computing is in the Noisy Intermediate-Scale Quantum (NISQ) era. On NISQ devices, two-qubit gates such as CNOTs are much noisier than single-qubit gates, so it is essential to minimize their count. Quantum circuit synthesis is a process of decomposing an arbitrary unitary into a sequence of quantum gates, and can be used as an optimization tool to produce shorter circuits to improve overall circuit fidelity. However, the time-to-solution of synthesis grows exponentially with the number of qubits. As a result, synthesis is intractable for circuits on a large qubit scale. In this paper, we propose a hierarchical, block-by-block optimization framework, QGo, for quantum circuit optimization. Our approach allows an exponential cost optimization to scale to large circuits. QGo uses a combination of partitioning and synthesis: 1) partition the circuit into a sequence of independent circuit blocks; 2) re-generate and optimize each block using quantum synthesis; and 3) re-compose the final circuit by stitching all the blocks together. We perform our analysis and show the fidelity improvements in three different regimes: small-size circuits on real devices, medium-size circuits on noise simulations, and large-size circuits on analytical models. Using a set of NISQ benchmarks, we show that QGo can reduce the number of CNOT gates by 29.9% on average and up to 50% when compared with industrial compilers such as t|ket>. When executed on the IBM Athens system, shorter depth leads to higher circuit fidelity. We also demonstrate the scalability of our QGo technique to optimize circuits of 60+ qubits. Our technique is the first demonstration of successfully employing and scaling synthesis in the compilation toolchain for large circuits. Overall, our approach is robust for direct incorporation in production compiler toolchains.
Abstract We investigate the problem of synthesizing T-depth optimal quantum circuits for exactly implementable unitaries over the Clifford+T gate set. We construct a subset, Vn Vn , of T-depth 1 unitaries. T-depth-optimal decomposition of unitaryUis eiϕ(∏iVi)C eiϕ∏iViC , Vi∈Vn Vi∈Vn ,Cis Clifford and ∣Vn∣≤n⋅25.6n ∣Vn∣≤n⋅25.6n . We use nested meet-in-the-middle technique to synthesize provably depth-optimal and T-depth-optimal circuits. For the latter, we achieve space and time complexity O((4n2)⌈d/c⌉) O((4n2)⌈d/c⌉) and O((4n2)(c−1)⌈d/c⌉) O((4n2)(c−1)⌈d/c⌉) respectively (dis the minimum T-depth,c ≥ 2 a constant). The previous best algorithm had complexity O((3n⋅2kn2)⌈2d⌉⋅2kn2) O((3n⋅2kn2)⌈d2⌉⋅2kn2) (k > 2.5 a constant). We design a more efficient algorithm with space and time complexity poly(n, 25.6n, d) (or poly(nlogn,25.6n,d) poly(nlogn,25.6n,d) with weaker assumptions). The claimed efficiency, optimality depends on conjectures.
Executing quantum algorithms on a quantum computer requires compilation to representations that conform to all restrictions imposed by the device. Due to devices' limited coherence times and gate fidelities, the compilation process has to be optimized as much as possible. To this end, an algorithm's description first has to be synthesized using the device's gate library. In this paper, we consider the optimal synthesis of Clifford circuits---an important subclass of quantum circuits, with various applications. Such techniques are essential to establish lower bounds for (heuristic) synthesis methods and gauging their performance. Due to the huge search space, existing optimal techniques are limited to a maximum of six qubits. The contribution of this work is twofold: First, we propose an optimal synthesis method for Clifford circuits based on encoding the task as a satisfiability (SAT) problem and solving it using a SAT solver in conjunction with a binary search scheme. The resulting tool is demonstrated to synthesize optimal circuits for up to 26 qubits---more than four times as many as the current state of the art. Second, we experimentally show that the overhead introduced by state-of-the-art heuristics exceeds the lower bound by 27 % on average. The resulting tool is publicly available at https://github.com/cda-tum/qmap.
Efficiently implementing Clifford circuits is crucial for quantum error correction and quantum algorithms. Linear reversible circuits, equivalent to circuits composed of CNOT\xspace gates, have important applications in classical computing. In this work we present methods for CNOT\xspace and general Clifford circuit synthesis which can be used to minimise either the entangling two-qubit gate count or the circuit depth. We present three families of algorithms - optimal synthesis which works on small circuits, A* synthesis for intermediate-size circuits and greedy synthesis for large circuits. We benchmark against existing methods, including rustiq, tket and qiskit and show that our approach results in circuits with lower two-qubit gate count. For encoding circuits, our methods outperform previous reinforcement learning results and find a lower two-qubit gate count circuit for the Golay code than previously known. The algorithms have been implemented in a GitHub repository for use by the classical and quantum computing community.