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arXiv:2309.10204

Quantum Multiplier Based on Exponent Adder

Junpeng Zhan

Published Sep 18, 2023• 1 citation
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Abstract

Quantum multiplication is a fundamental operation in quantum computing. It is important to have a quantum multiplier with low complexity. In this paper, we propose the Quantum Multiplier Based on Exponent Adder (QMbead), a new approach that requires just $\log_2(n)$ qubits to multiply two $n$-bit integer numbers, in addition to $O(n)$ ancillary qubits used for quantum state preparation. The QMbead uses a so-called exponent encoding to respectively represent two multiplicands as two superposition states which are prepared by a quantum state preparation method, then employs a quantum adder to obtain the sum of these two superposition states, and subsequently measures the outputs of the quantum adder to calculate the product of the multiplicands. Different quantum adders can be used in the QMbead. The circuit depth and time complexity of the QMbead, using a logarithmic-depth quantum carry lookahead adder (QCLA) as adder, are $O(\log n)$ and $O(n \log n)$, respectively. The gate complexity of the QMbead is $O(n)$. The circuit depth and gate complexity of the QMbead is better than existing quantum multipliers such as the quantum Karatsuba multiplier and the QFT based multiplier. The time complexity of the QMbead is identical to that of the fastest classical multiplication algorithm, Harvey-Hoeven algorithm. Interestingly, the QMbead maintains an advantage over the Harvey-Hoeven algorithm, given that the latter is only suitable for excessively large numbers, whereas the QMbead is valid for both small and large numbers. The multiplicand can be either an integer or a decimal number. The QMbead has been implemented on quantum simulators to compute products with a bit length of up to 273 bits using only 17 qubits, excluding the ancillary qubits used for quantum state preparation. This establishes QMbead as an efficient solution for multiplying large integer or decimal numbers with many bits.

DOI
10.48550/arxiv.2309.10204

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Quantum multiplication is a fundamental operation in quantum computing. It is important to have a quantum multiplier with low complexity. In this paper, we propose the Quantum Multiplier Based on Exponent Adder (QMbead), a new approach that requires just log⁡2(n)\log_2(n)log2​(n) qubits to multiply two nnn-bit integer numbers, in addition to O(n)O(n)O(n) ancillary qubits used for quantum state preparation. The QMbead uses a so-called exponent encoding to respectively represent two multiplicands as two superposition states which are prepared by a quantum state preparation method, then employs a quantum adder to obtain the sum of these two superposition states, and subsequently measures the outputs of the quantum adder to calculate the product of the multiplicands. Different quantum adders can be used in the QMbead. The circuit depth and time complexity of the QMbead, using a logarithmic-depth quantum carry lookahead adder (QCLA) as adder, are O(log⁡n)O(\log n)O(logn) and O(nlog⁡n)O(n \log n)O(nlogn), respectively. The gate complexity of the QMbead is O(n)O(n)O(n). The circuit depth and gate complexity of the QMbead is better than existing quantum multipliers such as the quantum Karatsuba multiplier and the QFT based multiplier. The time complexity of the QMbead is identical to that of the fastest classical multiplication algorithm, Harvey-Hoeven algorithm. Interestingly, the QMbead maintains an advantage over the Harvey-Hoeven algorithm, given that the latter is only suitable for excessively large numbers, whereas the QMbead is valid for both small and large numbers. The multiplicand can be either an integer or a decimal number. The QMbead has been implemented on quantum simulators to compute products with a bit length of up to 273 bits using only 17 qubits, excluding the ancillary qubits used for quantum state preparation. This establishes QMbead as an efficient solution for multiplying large integer or decimal numbers with many bits.
We present a quantum algorithm for multiplying two nnn-bit integers with overall circuit depth and TTT-depth both bounded by O(log⁡n)O(\log n)O(logn), while using O(n2)O(n^2)O(n2) gates and ancillary qubits. Our construction generates partial products via indicator-controlled copying and adds them using a tree of reversible 333-to-222 adders followed by one quantum carry-lookahead adder. Building on the carry-save adders of Kim et al., we combine full input fanout, workspace reuse, and phase-corrected gate implementations to reduce TTT-depth. We provide explicit circuit constructions, proofs of correctness, and concrete closed-form resource bounds.
Quantum modular multipliers are a promising development in the field of quantum computing that aim to perform modular multiplication faster and more efficiently than classical algorithms. This is achieved by encoding the numbers being multiplied into quantum bits (qubits) and using quantum gates to manipulate them in a way that represents the calculation. Since quantum physics principles like superposition and entanglement dramatically speed up processing compared to conventional computing, many calculations may be run simultaneously. Quantum modular multipliers have the potential to have a significant impact in a range of fields, including cryptography and beyond, since they offer a faster and more efficient way to do modular multiplications. Several fields, including simulation, optimization, and cryptography, can benefit from the use of quantum computing.One of the crucial operations in many of these applications is modular multiplication, a sort of multiplication that requires obtaining the remainder of the product of two numbers when divided by a third number. In this paper, we provide a novel modular multiplication method based on a quantum algorithm. To examine its complexity and compare it to that of current adders, we employ a novel form of adder that is intended to not retain the final carry. This adder's decrease percentage in terms of gates and depth is roughly 70%. Our method takes advantage of the special qualities of qubits to complete the computation significantly more quickly than conventional algorithms. It is based on the fundamental ideas of quantum physics, such as superposition and entanglement.
Quantum computing (Q-computing) is a revolutionary technology that applies quantum mechanics (QM) to implement calculations significantly quicker than traditional computers. Q-computing employs Q-bits (qubits) rather than classical bits, which can take on a superposition state, making it possible to represent both 0 and 1 at once. This distinctive property allows qubits to perform multiple calculations simultaneously, making Q computers exponentially more powerful as more qubits are added, compared to the linear scaling of classical computers. However, challenges such as qubit instability and error rates remain significant obstacles to fully realizing this potential. A vital aspect of this technology is the development of Q arithmetic circuits, which act as the foundational blocks for efficiently executing Q algorithms. Q half-adder and full-adder circuits are integral to performing fundamental arithmetic operations within Q computers. In contrast to classical adders, Q adders rely on Q gates such as Controlled NOT (CNOT) and Toffoli gates. These gates exploit Q phenomena like entanglement and superposition, allowing for more efficient computations. The Q half-adder consists of two qubits and adds two single bits, producing a carry and sum output. It utilizes the CNOT gate to compute the sum and the Toffoli gate (controlled-controlled-NOT [CCNOT]) to compute the carry. The Q full-adder builds on this, adding three qubits to produce a sum and a carry output, further increasing computational capacity. Through the entanglement of qubits, these circuits allow for more parallelism and speed compared to classical adders. Q carry-look-ahead adders (QCLAs) and Q ripple-carry adders (QRCAs) are used to perform more complex arithmetic operations based on these half-adder and full-adder structures. Q arithmetic circuits have broad applications, boosting secure key generation in cryptography, speeding up matrix operations for faster model training in machine learning, and enabling more precise, efficient image transformations in Q image processing, benefiting fields like medical imaging and computer vision.
Quantum squaring circuits have been used as helpful arithmetic modules in various quantum algorithms for calculating series expansions or distances of vectors, etc. Quantum multipliers can replace quantum squaring circuits, but squaring with quantum multipliers is inefficient because it involves using quantum gates for unnecessary bitwise multiplication. In this paper, we propose a depth-optimized quantum circuit dedicated to squaring by eliminating these unnecessary quantum gates and implementing quantum gates in parallel. We also discuss the optimal distribution of the partial products to reduce further the gate cost of the quantum adder used for the sum of the partial products. The proposed partial product distribution method lowers the quantum adder’s number of gates and depth by half. Our quantum squaring circuit is the most efficient, with an average improvement of 68% and 79.7% in T-count and T-depth, respectively, compared to existing quantum squaring circuits. Despite the increased qubit counts caused by the depth optimization, we demonstrate that the proposed circuit has the smallest KQT\hbox {KQ}_\textrm{T}KQT​ .
In this paper, we propose an efficient quantum carry-lookahead adder based on the higher radix structure. For the addition of two n-bit numbers, our adder uses [Formula: see text] qubits and [Formula: see text] T gates to get the correct answer in T-depth [Formula: see text], where r is the radix. Quantum carry-lookahead adder has already attracted some attention because of its low T-depth. Our work further reduces the overall cost by introducing a higher radix layer. By analyzing the performance in T-depth, T-count, and qubit count, it is shown that the proposed adder is superior to existing quantum carry-lookahead adders. Even compared to the Draper out-of-place adder which is very compact and efficient, our adder is still better in terms of T-count.
In this study, we propose an efficient quantum multiplication approach based on a QFT-assisted parallelized addition scheme. The multiplication stage is implemented using a structure composed entirely of Toffoli gates, which generate partial products. In the second stage, these partial results are accumulated using a QFT-based adder. Unlike conventional QFT-based arithmetic circuits, the proposed design eliminates the repeated application of QFT and inverse QFT (IQFT) operations during intermediate summation processes. This leads to a significant reduction in the total gate count and circuit complexity, enabling a more resource-efficient implementation. To demonstrate the feasibility of the proposed approach, a quantum circuit that performs the multiplication of two 3-bit numbers is designed. The circuit is tested and validated using IBM quantum simulators. The results indicate that the proposed method provides a more efficient alternative to traditional quantum multiplication techniques in terms of gate cost and circuit depth.