A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factor: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. We thus give the first examples of quantum cryptanalysis.>
A computer is generally considered to be a universal computational device; i.e., it is believed able to simulate any physical computational device with a cost in computation time of at most a polynomial factor: It is not clear whether this is still true when quantum mechanics is taken into consideration. Several researchers, starting with David Deutsch, have developed models for quantum mechanical computers and have investigated their computational properties. This paper gives Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored. These two problems are generally considered hard on a classical computer and have been used as the basis of several proposed cryptosystems. We thus give the first examples of quantum cryptanalysis.>
A digital computer is generally believed to be an efficient universal computing device; that is, it is believed to be able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. This paper considers factoring integers and finding discrete logarithms, two problems that are generally thought to be hard on classical computers and that have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum computer. These algorithms take a number of steps polynomial in the input size, for example, the number of digits of the integer to be factored.
A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and which have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored.
We present our perspectives on the current status of, integer factoring and discrete logarithm, algorithms underlying the strength of classical public-key cryptography protocols used in key-exchange, encryption and digital signatures. We present an expository overview of the algebraic, number-theoretic and algorithmic principles behind the canonical, powerful algorithms used in the factoring of large, composite RSA modulii. These cover the two main ideas of group order based methods, and quadratic congruence based methods. We provide the resource estimates for the record breaking factoring based on the general number field sieve. Next we present an overview of the algorithmic theory of quantum computation behind, the polynomial time Shor's quantum algorithm for, period finding in hidden subgroup structure problems. This algorithm has played a key role in advancing post-quantum cryptography, and also in the exciting, contemporary developments in quantum computing, networking, sensing, and materials science as a whole. Latest advances in quantum computing rest on a variety of physics, engineering, algorithms, compilation, hardware, software techniques. There are several tradeoffs based on number of qubits, coherence times, control issues, errors and fault tolerance. In this environment, nuances of mathematical ideas, behind every step of the intricate arithmetical and algebraic computations under quantum models plays a significant role. We provide an overview of some of the recent ideas behind such algorithms. We conclude with a recent deep question on the ultimate feasibility of ultra large-scale, error-free, discrete, quantum computations required typically in cryptanalysis.
The Church-Turing thesis says that a digital computer is a universal computational device; that is, it is able to simulate any physically realizable computational device. It has generally been believed that this simulation can be made efficient so that it entails at most a polynomial increase in computation time. This may not be true if quantum mechanics is taken into consideration. A quantum computer is a hypothetical machine based on quantum mechanics. We explain quantum computing, and give an algorithm for prime factorization on a quantum computer that runs asymptotically much faster than the best known algorithm on a digital computer. It is not clear whether it will ever be possible to build large-scale quantum computers. One of the main difficulties is in manipulating coherent quantum states without introducing errors or losing coherence. We discuss quantum error-correcting codes and fault-tolerant quantum computing, which can guarantee highly reliable quantum computation, given only moderately reliable quantum computing hardware.
This thesis contains a collection of efficient quantum algorithms and classical pre- and post-processing algorithms for various number- and group-theoretical problems with concrete cryptanalytical applications. More specifically, the focus is on the integer factoring problem (IFP), the discrete logarithm problem (DLP), and the order-finding problem (OFP), and on certain important variations of these problems. It is for example explained how the OFP and complete IFP may be solved efficiently in a single run of a reduced version of Shor's algorithm for the OFP with very high success probability, as evidenced by a lower bound that is 1 − o(1), how the short DLP in groups of unknown order may be solved efficiently and with an advantage over Shor's algorithm for the DLP (that furthermore requires the order to be known), as evidenced by a lower bound on the single-run success probability that is 1 − o(1), how the RSA IFP may be solved efficiently and with an advantage over Shor's algorithm for the IFP, by reducing the RSA IFP to a short DLP, how the DLP may heuristically be solved efficiently, and with high success probability that is 1 − o(1), in a single run of a reduced version of Shor’s algorithm for the DLP that admits efficient implementation, how the DLP may be solved in groups of unknown order, such as Schnorr groups, by jointly solving an OFP and a DLP at lower overall cost but higher per-run cost compared to first solving the OFP and then the DLP, how tradeoffs — between the number of group operations that are evaluated quantumly and the number of runs that are required — may be achieved in all of the aforementioned algorithms, and how all of the aforementioned algorithms may be simulated efficiently classically, for arbitrary large cryptographically relevant problem instances, when the solution to the instance is known, enabling the number of runs required when performing tradeoffs to be tightly estimated. It is furthermore for example explained how Regev's recent quantum algorithm for the IFP may be extended to the DLP and OFP, yielding i) a reduction in the circuit size and depth by a factor compared to Shor for these problems when working in , for N an n-bit integer, at the expense of performing runs, and ii) slight improvements of Regev's orginal algorithm for the IFP, how the above algorithms may be simulated efficiently classically, for large special-form problem instances that are classically tractable, and that Regev’s post-processing, and the post-processing for our extensions, are robust to an arbitrarily large fraction < 1 of the runs being bad. The above has implications for cryptography based on the intractability of the IFP and DLP — including but not limited to for widely used schemes such as RSA, DSA and Diffie–Hellman. The algorithms in this thesis are all derived from, and make notable improvements to, the seminal works of Shor. Some of the contributions are joint works with other authors.
This chapter evaluates the stability of the modern cryptographic algorithms to clarify the tasks of cryptanalysis of asymmetric encryption schemes and digital signature based on factorization and discrete logarithm problems in various algebraic structures in a quantum computing model. It considers the features of modeling quantum cryptanalysis algorithms on a quantum scheme, and shows the differences between these algorithms and classical algorithms. The quantum algorithm is performed by implementing a series of sequential unitary operations. Grover's algorithm provides a quadratic increase in speed, while some other quantum algorithms, for example, the Shor factorization algorithm, give an exponential gain compared to the corresponding classical algorithms. Grover's algorithm is modified into an algorithm for recovering the symmetric encryption key from the message text and ciphertext. The stability of the RSA asymmetric encryption system is based on the super-polynomial computational complexity of factorization of natural numbers. However, there is a quantum algorithm whose complexity is polynomial.