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A Polarity-Based Approach for Optimization of Multivalued Quantum Multiplexers with Arbitrary Single-Qubit Target Gates

Kevin Jin, Tahsin Saffat, Justin Morgan, Marek A. Perkowski

Published Jan 1, 2020• 2 citations
OpenAlex

Abstract

Previous work has provided methods for decomposing unitary matrices to series of quantum multiplexers, but the multiplexer circuits created in this way may be highly non-minimal. This paper presents a new approach for optimizing quantum multiplexers with arbitrary single-qubit quantum target functions and ternary controls. For multivalued quantum multiplexers, we define standard forms and two types of new forms: Fixed Polarity Quantum Forms (FPQFs) and Kronecker Quantum Forms (KQFs). Drawing inspiration from the usage of butterfly diagrams, we devise a method to exhaustively construct new forms. In contrast to previous butterfly-based methods, which are used with classical Boolean functions, these new forms are used to optimize quantum circuits with arbitrary target unitary matrices. Experimental results on the new forms applied to various target gates such as NOT, V, V+, Hadamard, and Pauli rotations, demonstrate that these new forms greatly reduce the gate costs of ternary quantum multiplexers.

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Previous work has provided methods for decomposing unitary matrices to series of quantum multiplexers, but the multiplexer circuits created in this way may be highly non-minimal. This paper presents a new approach for optimizing quantum multiplexers with arbitrary single-qubit quantum target functions and ternary controls. For multivalued quantum multiplexers, we define standard forms and two types of new forms: Fixed Polarity Quantum Forms (FPQFs) and Kronecker Quantum Forms (KQFs). Drawing inspiration from the usage of butterfly diagrams, we devise a method to exhaustively construct new forms. In contrast to previous butterfly-based methods, which are used with classical Boolean functions, these new forms are used to optimize quantum circuits with arbitrary target unitary matrices. Experimental results on the new forms applied to various target gates such as NOT, V, V+, Hadamard, and Pauli rotations, demonstrate that these new forms greatly reduce the gate costs of ternary quantum multiplexers.
Previous work has provided methods for decomposing unitary matrices to series of quantum multiplexers, but the multiplexers created in this way are highly non-minimal. This paper presents a new approach for optimizing quantum multiplexers with arbitrary single-qubit quantum target functions. For quantum multiplexers, we define standard forms and two types of new forms: fixed polarity quantum forms (FPQF) and Kronecker quantum forms (KQF), which are analogous to Minterm Sum of Products forms, Fixed Polarity Reed-Muller (FPRM) forms, and Kronecker Reed-Muller (KRM) forms, respectively, for classical logic functions. Drawing inspiration from the usage of butterfly diagrams for FPRM and KRM forms, we devise a method to exhaustively construct all FPQF and KQF forms. Thus, the new forms can be used to optimize quantum circuits with arbitrary target unitary matrices, rather than only multi-controlled NOT gates such as CNOT, CCNOT, and their extensions. Experimental results on FPQF and KQF forms, as well as FPRM and KRM classical forms, applied to various target gates such as NOT, V, V+, Hadamard, and Pauli rotations, demonstrate that FPQF and KQF forms greatly reduce the gate cost of quantum multiplexers in both randomly generated data and FPRM benchmarks.
This paper introduces a new logic structure for reversible quantum circuit synthesis. Our synthesis method aims to minimize the quantum cost of reversible quantum circuits with decoders. In this method, multi-valued input, binary output (MVI) functions are utilized as a mathematical concept only, but the circuits are binary. We introduce the new concept of ``Multi-Valued Input Fixed Polarity Reed-Muller (MVI-RM)" forms. Our decoder-based circuit uses three logical levels in contrast to commonly-used methods based on Exclusive-or Sum of Products (ESOP) with two levels (AND-XOR expressions), realized by Toffoli gates. In general, the high number of input qubits in the resulting Toffoli gates is a problem that greatly impacts the quantum cost. Using decoders decreases the number of input qubits in these Toffoli gates. We present two practical algorithms for three-level circuit synthesis by finding the MVI-FPRM: products-matching and the newly developed butterfly diagrams. The best MVI-FPRM forms are factorized and reduced to approximate Multi-Valued Input Generalized Reed-Muller (MVI-GRM) forms.
This paper presents a new approach to optimize arbitrary quantum circuits based on multi-valued Quantum Multiplexers. We define standard, and fixed polarity forms for binary valued quantum multiplexers that are analogous to the disjoint sum of product and Fixed Polarity Reed-Muller Forms for classical logic functions. Then, the method is extended to logic with an arbitrary radix. The algorithm produced requires O(mqm) butterfly transformations, where m is the number of control variables, and q is the radix of logic. A software script is then added and described to facilitate in the computation of larger and more complex quantum multiplexers.
The choice of the best set of universal ternary gates for quantum circuits is an open problem. We create exact minimum cost ternary reversible gates with quantum multiplexers using the method of iterative deepening depth-first search (IDDFS) [25]. Such search is better for small problems than evolutionary algorithms or other search methods. Several new gates that are provably exact minimum cost have been discovered. These gates are next used as library building blocks in the minimization of larger ternary quantum circuits like highly testable GFSOP cascades [15,16] (that generalize ESOP) as well as the wave cascades [24] generalized to ternary logic. They are useful to design oracles for multivalued algorithms such as Deutsch-Jozsa [26] and Grover.
The design and optimization of quantum circuits is central to quantum computation. This paper presents new algorithms for compiling arbitrary 2n×2n unitary matrices into efficient circuits of (n−1)-controlled single-qubit and (n−1)-controlled-NOT gates. We first present a general algebraic optimization technique, which we call the Palindrome Transform, that can be used to minimize the number of selfinverting gates in quantum circuits consisting of concatenations of palindromic subcircuits. For a fixed column ordering of two-level decomposition, we then give an enumerative algorithm for minimal (n−1)-controlled-NOT circuit construction, which we call the Palindromic Optimization Algorithm. Our work dramatically reduces the number of gates generated by the conventional two-level decomposition method for constructing quantum circuits of (n − 1)-controlled single-qubit and (n − 1)-controlled-NOT gates.
The design and optimization of quantum circuits is central to quantum computation. This paper presents new algorithms for compiling arbitrary 2^n x 2^n unitary matrices into efficient circuits of (n-1)-controlled single-qubit and (n-1)-controlled-NOT gates. We first present a general algebraic optimization technique, which we call the Palindrome Transform, that can be used to minimize the number of self-inverting gates in quantum circuits consisting of concatenations of palindromic subcircuits. For a fixed column ordering of two-level decomposition, we then give an numerative algorithm for minimal (n-1)-controlled-NOT circuit construction, which we call the Palindromic Optimization Algorithm. Our work dramatically reduces the number of gates generated by the conventional two-level decomposition method for constructing quantum circuits of (n-1)-controlled single-qubit and (n-1)-controlled-NOT gates.