We report a proof-of-concept demonstration of a quantum order-finding algorithm for factoring the integer 21. Our demonstration involves the use of a compiled version of the quantum phase estimation routine, and builds upon a previous demonstration. We go beyond this work by using a configuration of approximate Toffoli gates with residual phase shifts, which preserves the functional correctness and allows us to achieve a complete factoring of [Formula: see text]. We implemented the algorithm on IBM quantum processors using only five qubits and successfully verified the presence of entanglement between the control and work register qubits, which is a necessary condition for the algorithm's speedup in general. The techniques we employ may be useful in carrying out Shor's algorithm for larger integers, or other algorithms in systems with a limited number of noisy qubits.
We report a proof-of-concept demonstration of a quantum order-finding algorithm for factoring the integer 21. Our demonstration involves the use of a compiled version of the quantum phase estimation routine, and builds upon a previous demonstration. We go beyond this work by using a configuration of approximate Toffoli gates with residual phase shifts, which preserves the functional correctness and allows us to achieve a complete factoring of [Formula: see text]. We implemented the algorithm on IBM quantum processors using only five qubits and successfully verified the presence of entanglement between the control and work register qubits, which is a necessary condition for the algorithm's speedup in general. The techniques we employ may be useful in carrying out Shor's algorithm for larger integers, or other algorithms in systems with a limited number of noisy qubits.
RSA cryptography is a principle part in today's cyber-security frameworks, which intensely depends on the diffi-culty of factorizing large integers. Finding a factor of large prime number is not a small feature compared to the time complexity for computation on a classical computer is far beyond comprehension. Back in 1994, Peter Shor proposed a quantum algorithm to factorize integers much more efficiently than a classical computer. This can effectively break asymmetric cryptosystems such as RSA or Diffie-Hellman, depicted as the most famous quantum algorithm. It leverages the efficacy proclaimed by quantum bits and clearly shows how quantum computers can outperform classical ones in specific tasks. In this paper, we show how Shor's algorithm factorizes an integer into its two prime factors. Shor uses both classical and quantum computation to perform this task in polynomial time, using Euclidean algorithm and find the greatest common divisor (gcd) of two numbers in the classical part. Thereafter, the power of Quantum Fourier Transform (QFT) and Quantum Phase Estimation (QPE) is utilised for period-finding using quantum bits. We have implemented this algorithm on IBM's Quantum Lab and successfully factorized the number 15 into its prime factors as 5 and 3, by using 5 qubits. This paper provides an overview of the implementation of Shor's algorithm involving QPE, QFT, Modular Exponentiation and the corresponding individual implementations, the only prerequisite being, to know how a qubit works.
Quantum algorithms are computational routines that exploit quantum mechanics to solve problems exponentially faster than the best classical algorithms. Shor's quantum factoring algorithm is a key example and the prime motivator in the international effort to realise a quantum computer. However, due to the large number of resources required, to date, there have been only four small scale demonstrations. Here we address this resource demand and demonstrate a scalable version of Shor's algorithm in which then qubit control register is replaced by a single qubit that is recycled n times: the total number of qubits is one third of that required in the standard protocol. Encoding the work register in higher-dimensional states, we implement a two-photon compiled algorithm to factor N = 21. Significantly, the algorithmic output exhibits structure that is distinguishable from noise, in contrast to previous demonstrations.
We present an experimental demonstration of factoring 21 using a compiled version of Shor’s algorithm on the Tianyan-176 superconducting processor. Our compilation approach—Fredkin gate synthesis—is primarily derived from the symmetry of the permutation operations in the modular exponentiation function (MEF) modules. The effectiveness of the approximate Toffoli gate substitution is also theoretically validated. Additionally, we propose several novel compilation strategies for implementing a lower-depth, topology-adapted inverse quantum Fourier transform (QFT). The 6-qubit demonstration achieves a state-of-the-art implementation with only 19 two-qubit gates on a grid-based qubit topology, without introducing any SWAP gates. Owing to the permutation symmetry, the demonstration is readily extensible to larger-scale instances and other factoring cases by introducing only a minimal number of SWAP gates, leveraging the commutation properties between the controlled MEF and inverse QFT modules.
To factor an integer N into a product of two other numbers N1 and N2, Shor’s algorithm uses the Quantum Fourier Transform on n qubits, denoted QFTn, to determine the period r of a Modular Exponentiation Function (MEF), which is later used to compute a factor of N. In this work, we present Shor’s algorithm for factoring an integer of n qubits and illustrate an example with n=4. We implement our algorithm using Qiskit and IBM quantum computers, which also can be adapted on other quantum computing platforms with minor adjustments. We discuss the IBM quantum computer limitations for running our circuit and show the impact of device imperfections on the factored result.
We provide a pedagogical presentation of Shor's factoring algorithm, which is a quantum algorithm for factoring very large numbers (of order of hundreds to thousands of bits) in polynomial time. In contrast, all known classical algorithms for the factoring problem take an exponential time to factor such large numbers. Shor's algorithm therefore has profound implication for public-key encryption such as RSA and Diffie–Hellman key exchange. We assume no prior knowledge of Shor's algorithm beyond a basic familiarity with the circuit model of quantum computing. Shor's algorithm contains a number of moving parts, and can be rather daunting at first. The literature is replete with derivations and expositions of Shor's algorithm, but most of them seem to be lacking in essential details, and none of them provide a pedagogical presentation. They require a thicket of appendices and assume a knowledge of quantum algorithms and classical mathematics with which the reader might not be familiar. We therefore start with first principle derivations of the quantum Fourier transform (QFT) and quantum phase estimation (QPE), which are the essential building blocks of Shor's algorithm. We then go on to develop the theory of modular exponentiation (ME) operators, one of the fundamental components of Shor's algorithm, and the place where most of the quantum resources are deployed. We also delve into the number theory that establishes the link between factorization and the period of the modular exponential function. We then apply the QPE algorithm to obtain Shor's factoring algorithm. We also discuss the post-quantum processing and the method of continued fractions, which is used to extract the exact period of the modular exponential function from the approximately measured phase angles of the ME operator. The manuscript then moves on to a series of examples. We first verify the formalism by factoring N=15, the smallest number accessible to Shor's algorithm. We then proceed to factor larger integers, developing a systematic procedure that will find the ME operators for any semi-prime N=p×q (where q and p are prime). Finally, we factor the composite numbers N=21, 33, 35, 143, 247 using the Qiskit simulator. It is observed that the ME operators are somewhat forgiving, and truncated approximate forms are able to extract factors just as well as the exact operators. This is because the method of continued fractions only requires an approximate phase value for its input, which suggests that implementing Shor's algorithm might not be as difficult as first suspected. Quanta 2023; 12: 41–130.
We study the results of a compiled version of Shor's factoring algorithm on the ibmqx5 superconducting chip, for the particular case of N=15, 21, and 35. The semiclassical quantum Fourier transform is used to implement the algorithm with only a small number of physical qubits, and the circuits are designed to reduce the number of gates to the minimum. We use the square of the statistical overlap to give a quantitative measure of the similarity between the experimentally obtained distribution of phases and the predicted theoretical distribution of phases for different values of the period. This allows us to assign a period to the experimental data without the use of the continued fraction algorithm. A quantitative estimate of the error in our assignment of the period is then given by the overlap coefficient.