The Toffoli gate (controlled-controlled-NOT) and its multi-controlled generalizations are fundamental logical building blocks of quantum algorithms and utilized extensively in pivotal algorithms such as Grover’s algorithm and Shor’s algorithm. Despite being a fundamental logical primitive of quantum algorithms, Toffoli gates must be decomposed to primitive single-qubit and two-qubit gates which are available in physical quantum hardware. These decompositions incur significant implementation costs in circuit depth and gate count, costs that propagate directly to the entire circuit. Consequently, reducing the implementation cots of Toffoli gates provides a direct method of reducing the implementation costs of many quantum algorithms. This thesis evaluates state-of-the-art methods for reducing the implementation costs of Toffoli gates by introducing approximations. While approximations introduce errors to the output of the Toffoli gate, they can reduce the depth and gate count of the implementation. This thesis finds that discrete errors in two-qubit and three-qubit controlled Toffoli gates have been utilized to reduce the gate counts in such a way that the implementation is exact cancelling the errors. Additionally, continuous errors are utilized to reduce the depth of a multi-controlled Toffoli gate decomposition in ancilla-free settings. However, this review also demonstrates other, non-approximate, construction of multi-controlled Toffoli gate, which achieves comparable implementation costs demonstrating marginal gains for the approximation techniques currently.
The Toffoli gate (controlled-controlled-NOT) and its multi-controlled generalizations are fundamental logical building blocks of quantum algorithms and utilized extensively in pivotal algorithms such as Grover’s algorithm and Shor’s algorithm. Despite being a fundamental logical primitive of quantum algorithms, Toffoli gates must be decomposed to primitive single-qubit and two-qubit gates which are available in physical quantum hardware. These decompositions incur significant implementation costs in circuit depth and gate count, costs that propagate directly to the entire circuit. Consequently, reducing the implementation cots of Toffoli gates provides a direct method of reducing the implementation costs of many quantum algorithms. This thesis evaluates state-of-the-art methods for reducing the implementation costs of Toffoli gates by introducing approximations. While approximations introduce errors to the output of the Toffoli gate, they can reduce the depth and gate count of the implementation. This thesis finds that discrete errors in two-qubit and three-qubit controlled Toffoli gates have been utilized to reduce the gate counts in such a way that the implementation is exact cancelling the errors. Additionally, continuous errors are utilized to reduce the depth of a multi-controlled Toffoli gate decomposition in ancilla-free settings. However, this review also demonstrates other, non-approximate, construction of multi-controlled Toffoli gate, which achieves comparable implementation costs demonstrating marginal gains for the approximation techniques currently.
The Toffoli gate is a fundamental building block for quantum arithmetic and reversible logic, yet its efficient realization remains a major challenge in both near-term and fault-tolerant quantum architectures. Recent advances in dynamic quantum circuit capabilities, including mid-circuit measurement and classical feedforward, provide new opportunities for reducing the resource overhead of non-Clifford operations.
In this work, we propose a set of dynamic decomposition strategies for multi-controlled Toffoli gates that exploit adaptive circuit execution and ancilla-assisted constructions. Our methods systematically reduce entangling-gate count, T-count, and T-depth compared with conventional static decompositions, while preserving fault-tolerance guarantees. Through analytical cost models and experimental evaluation, we demonstrate that relative-phase primitives and measurement-conditioned corrections enable scalable implementations with improved depth and resource efficiency.
State-of-the-art studies on quantum layout synthesis have proposed various approaches based on the assumption that the input circuit is only composed of single-qubit and two-qubit gates. This assumption greatly simplifies the layout synthesis problem, and thus they only require ensuring that all controlled-NOT (CNOT) gates satisfy the hardware constraints imposed by a given coupling graph. However, during the design of quantum circuits, multi-controlled Toffoli (MCT) gates are usually used to better characterize the function of the circuits. Directly decomposing them into single and two-qubit gates with a fixed routine ignores the flexibility and optimization opportunity provided by various decomposition (i.e., logic synthesis) possibilities and thus suffers from sub-optimal results. This article proposes a co-optimization approach for quantum logic and layout synthesis. The MCT gates are first decomposed into Toffoli gates, and an efficient qubit mapping checking process is proposed to optimally solve the SWAP-free layout synthesis problem by automatically determining the decomposition result of each Toffoli gate. If a SWAP-free result cannot be found, the proposed algorithm flow is then used to obtain a solution that minimizes the total cost that simultaneously counts the cost caused by the different decomposition methods for Toffoli gates and the cost induced by SWAP gates. Compared with a state-of-the-art method, the proposed approach reduces the number of additional CNOT gates by 16% with around 25X runtime speedup for the cases with SWAP-free solutions, which cannot be obtained without the co-optimization approach.
Multi-controlled Toffoli gates are fundamental building blocks in quantum computation, with applications in quantum arithmetic, simulation, and search algorithms. In fault-tolerant architectures, their realization is constrained by the high cost of non-Clifford resources, particularly in terms of T-count and T-depth. Recent advances have demonstrated that the use of ancillary qubits, relative-phase Toffoli gates, and dynamic circuit techniques can substantially reduce this overhead.
In this work, we investigate the decomposition of large Toffoli gates using 3- and 4-input relative-phase Toffoli gates in the presence of a single clean ancilla and conditionally clean ancillas. We derive explicit resource bounds for Clifford+T implementations incorporating dynamic-circuit-based uncomputation and measurement-conditioned corrections. Our analysis emphasizes T-depth reduction under fixed CX and T-count overhead, ensuring relevance for near-term devices. We show that introducing 4-input relative-phase Toffoli gates enables significant T-depth reductions through enhanced parallelism while maintaining favorable ancilla requirements. We further validate our theoretical results through experimental evaluation and comparative analysis with existing approaches.
Currently, the most used universal quantum gate set is the augmented Clifford+T set of gates. Most commonly, we use the cost reduction in terms of the T-depth or T-count to implement logic primitives such as the Toffoli gate. While a lot of research has been done in this area, the Clifford+T set of gates is difficult to use for the synthesis of larger multi-controlled NOT gates (CnNOT) or simply larger circuits. To address this issue, in this work, we examine the composition of the Toffoli gate in relation to the T-gates. We adopt an approach based on the logic formalization of the T-gate placement and deduce three main results. First, if the trailing phase is not a concern, we can efficiently reduce the number of T gates. The second result is that to construct CnNOT gates for n>2 the T gates require smaller angles of rotation (analogous to the original scaling problem in the Barenco et al. Toffoli implementation). Lastly, we demonstrate that it is feasible to substitute all T gates with RZrotations, ensuring the correct logic operation and a constant phase across all outputs.
The decomposition of complex quantum operations into experimentally feasible gate sets has been a central challenge since the early development of quantum computing. The multi-controlled Toffoli (MCT) gate is a key example, with applications across a wide range of quantum algorithms, whose decomposition into smaller gates, however, typically leads to deep circuits. In this work, we introduce a teleportation-based decomposition that implements an MCT gate. This decomposition has unit Toffoli depth, independent of the number of controls, while achieving the lowest Toffoli count among existing approaches for more than five controls at the cost of a linear overhead in ancilla qubits. We also investigate the parameter regimes in which the teleportation-based decomposition is preferable by analyzing the relative quality of distributed entanglement and Toffoli gates. We further demonstrate the advantages of this implementation in circuits that rely on MCT gates, such as the adder operator, quantum read-only memory, quantum neurons, and quantum decision trees.
Abstract We propose a method for efficient mixed polarity multiple controlled Toffoli (MPMCT) gate decomposition from the perspective of a cost metric related to Toffoli gates, namely Toffoli-depth. When using the technique presented in a previous study, there is a range in which Toffoli-depth (consequently T-depth) of the implemented circuit increases proportionally as the number of provided (clean) work qubits increases. In other words, using the previous technique may result in more inefficient MPMCT gates even though the number of helpful work qubits has increased. In this work, a technique is devised to provide sufficient help from clean work qubits at the central part of the implemented circuit as many as possible, thereby addressing the issues with the previous technique. Meanwhile, one of the representative algorithms that use MPMCT gates is Grover’s algorithm. We show the implementation results for MPMCT gates according to the number of work qubits, using Grover’s algorithm as an example. It is experimentally demonstrated that T-depth decreases much more quickly when using our method than the previous method.